%cec2017.awk Revision: 1.4 $ by WBL http://www.cs.ucl.ac.uk/staff/W.Langdon/ 08 Aug 2017 @INPROCEEDINGS{7969278, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Authors list}, year={2017}, editor = {Jose A. Lozano}, pages = {1--14}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Presents an index of the authors whose articles are published in the conference proceedings record.}, doi={10.1109/CEC.2017.7969278}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969278}}, } @INPROCEEDINGS{7969279, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={[Front cover]}, year={2017}, editor = {Jose A. Lozano}, pages={c1-c1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Presents the front cover or splash screen of the proceedings record.}, keywords = {Evolutionary computation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969279}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969279}}, } @INPROCEEDINGS{7969280, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={[Title page]}, year={2017}, editor = {Jose A. Lozano}, pages = {1--1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Presents the title page of the proceedings record.}, doi={10.1109/CEC.2017.7969280}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969280}}, } @INPROCEEDINGS{7969281, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={[Copyright notice]}, year={2017}, editor = {Jose A. Lozano}, pages = {1--1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={}, doi={10.1109/CEC.2017.7969281}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969281}}, } @INPROCEEDINGS{7969282, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Table of contents}, year={2017}, editor = {Jose A. Lozano}, pages = {1--1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Presents the table of contents/splash page of the proceedings record.}, doi={10.1109/CEC.2017.7969282}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969282}}, } @INPROCEEDINGS{lozan:2017:CEC, author={J. A. Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={General chair's welcome}, year={2017}, editor = {Jose A. Lozano}, pages = {1--1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={It is my pleasure to welcome you to Donostia / San Sebastian for the 2017 IEEE Congress on Evolutionary Computation (CEC).}, keywords = {Atmosphere, Optimization, Organizations, Standards organizations}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969283}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969283}}, } @INPROCEEDINGS{7969284, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Plenary talks [4 abstracts]}, year={2017}, editor = {Jose A. Lozano}, pages = {1--4}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Provides an abstract for each of the four plenary presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.}, keywords = {Algorithm design and analysis, Biographies, Clustering algorithms, Computers, Optimization, Urban areas}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969284}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969284}}, } @INPROCEEDINGS{7969285, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Tutorials [25 abstracts]}, year={2017}, editor = {Jose A. Lozano}, pages = {1--27}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Provides an abstract for each of the 25 tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.}, keywords = {Benchmark testing, Convergence, Optimization, Software, Software algorithms, Tools, Tutorials}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969285}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969285}}, } @INPROCEEDINGS{7969286, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Organizing Committee}, year={2017}, editor = {Jose A. Lozano}, pages = {1--1}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Provides a listing of current committee members and society officers.}, keywords = {Evolutionary computation, Tutorials}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969286}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969286}}, } @INPROCEEDINGS{7969287, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Reviewers list}, year={2017}, editor = {Jose A. Lozano}, pages = {1--8}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The conference offers a note of thanks and lists its reviewers.}, doi={10.1109/CEC.2017.7969287}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969287}}, } @INPROCEEDINGS{dubois-lacoste:2017:CEC, author={J. Dubois-Lacoste and T. Stützle}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Tuning of a stigmergy-based traffic light controller as a dynamic optimization problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1--8}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we present results on the automatic tuning of an adaptive traffic light controller. The traffic light controller is inspired by some swarm intelligence techniques and uses numerical values that are adapted by the principles of stigmergy to estimate queue lengths. These estimates are used in a probabilistic mechanism that switches between traffic light control strategies and determines phase lengths. This traffic light controller adapts through this specific mechanism to the current traffic situation but to define its behavior more than 100 parameters are used. To determine appropriate parameter settings, we therefore explored the automatic configuration of this traffic light controller. The main focus in this article is to examine whether the controller parameters require adaptation to major time-varying changes to reach best possible performance. We propose a strategy to deal with such changes and experimentally evaluate the impact limited re-tunings have on the controller performance.}, keywords = {adaptive control, control system synthesis, time-varying systems, traffic control, adaptive traffic light controller, automatic tuning, dynamic optimization problem, stigmergy-based traffic light controller, time-varying changes, Algorithm design and analysis, Heuristic algorithms, Microscopy, Optimization, Probabilistic logic, Tools, Tuning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969288}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969288}}, } @INPROCEEDINGS{shao:2017:CEC, author={Weishi Shao and Dechang Pi and Zhongshi Shao}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A hybrid iterated greedy algorithm for the distributed no-wait flow shop scheduling problem}, year={2017}, editor = {Jose A. Lozano}, pages = {9--16}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a hybrid iterated greedy (HIG) algorithm to solve the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion. The HIG mainly consists of four components, i.e. initialization phase, construction and destruction, local search, acceptance criterion. In the initialization phase, a modified NEH (Nawaz-Enscore-Ham) is proposed to generate a promising initial solution. In the local search phase, four local searching methods based on problem properties (i.e. insert move within factory, insert move between factories, swap move between factories) are proposed to enhance searching ability. The effectiveness of the initialization phase and local search method is shown by numerical comparison, and the comparisons with the recently published iterated greedy algorithms demonstrate the high effectiveness and searching ability of the proposed HIG for solving the DNWFSP.}, keywords = {flow shop scheduling, greedy algorithms, iterative methods, search problems, DNWFSP, Nawaz-Enscore-Ham, acceptance criterion, construction and destruction, distributed no-wait flow shop scheduling problem, hybrid iterated greedy algorithm, initialization phase, local search phase, makespan criterion, modified NEH, searching ability, Computer science, Job shop scheduling, Production facilities, local searching method, makespan}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969289}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969289}}, } @INPROCEEDINGS{wang:2017:CEC, author={S. Wang and J. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A multi-agent genetic algorithm for improving the robustness of communities in complex networks against attacks}, year={2017}, editor = {Jose A. Lozano}, pages = {17--22}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The design of robust networked structures is of significance in reality, and the integrity of network connections has been greatly emphasized in previous studies. However, besides structural integrity, a system should also keep the functionality when suffering from attacks and failures, i.e. robust community structure. Focusing on enhancing community robustness on complex networks, in this paper, based on a community robustness measure R_c , a multi-agent genetic algorithm, termed as MAGA-R_c , has been proposed to enhance the community robustness against attacks. The performance of MAGA-R_c is validated on several real-world networks, and the results show that MAGA-R_c could deal with the optimization of community robustness and outperforms several existing methods. The results provide convenience for networked property analyses and applicable to solve realistic optimization problems.}, keywords = {genetic algorithms, multi-agent systems, network theory (graphs), MAGA-Rc, community robustness measure, complex networks, multiagent genetic algorithm, networked property analyses, realistic optimization problems, robust networked structure design, structural integrity, Algorithm design and analysis, Heuristic algorithms, Lattices, Optimization, Resource management, Robustness, community, complex network, multi-agent algorithm, robustenss}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969290}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969290}}, } @INPROCEEDINGS{wang:2017:CECa, author={S. Wang and J. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Enhancing the robustness of complex networks against edge-based-attack cascading failures}, year={2017}, editor = {Jose A. Lozano}, pages = {23--28}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Existing studies indicated that it is crucial to design network structures with well tolerance against potential attacks and failures in reality, and several attack models have been proposed and lucubrated. Aiming at enhancing network robustness suffering from edge-based attack cascading failures, we first propose a measure, R_ce , to numerically evaluate the robustness of networks under cascading failures, and then a memetic algorithm, termed as MA-R_ce , is devised to optimize network robustness without changing the degree distribution. The experimental results on synthetic and real-world networks show that MA-R_ce can provide candidate networks with better anti-attack performance initialized by an initial network, also outperforms exist heuristic optimization algorithm, which facilitates theoretic analyses and provides potential solutions to realistic dilemmas in networked systems for decision makers.}, keywords = {complex networks, failure analysis, network theory (graphs), optimisation, MA-R_ce algorithm, antiattack performance, complex network robustness enhancement, decision makers, edge-based-attack cascading failures, memetic algorithm, network robustness evaluation, network structures, Algorithm design and analysis, Biological cells, Memetics, Optimization, Power system faults, Power system protection, Robustness, cascading failure}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969291}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969291}}, } @INPROCEEDINGS{torabi:2017:CEC, author={S. Torabi and M. Wahde}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Fuel consumption optimization of heavy-duty vehicles using genetic algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {29--36}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The performance of a method for reducing the fuel consumption of a heavy duty vehicle (HDV) is described and evaluated both in simulation and using a real HDV. The method, which involves speed profile optimization using a genetic algorithm, was applied to a set of road profiles (covering sections of 10 km), resulting in average fuel savings of 11.5% and 10.2% (relative to standard cruise control), in the simulation and the real HDV, respectively. Here, a compact representation of road profiles in the form of composite Bézier curves has been used, thus reducing the search space for speed profile optimization, compared to an earlier approach. In addition to outperforming MPC-based methods commonly found in the literature by at least 3 percentage points (in similar settings), the results also show that our simulations are sufficiently accurate to be transferred directly to a real HDV. In cases where the allowed range of speed variation was restricted, the proposed method outperformed standard predictive cruise control (PCC) by an average of around 3 percentage points as well, over the same road profiles.}, keywords = {curve fitting, fuel economy, genetic algorithms, predictive control, road traffic control, road vehicles, HDV, MPC-based methods, PCC, composite Bezier curve, fuel consumption optimization, genetic algorithm, heavy-duty vehicles, predictive cruise control, road profile, speed profile optimization, Cruise control, Force, Fuels, Optimization, Roads, Splines (mathematics), Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969292}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969292}}, } @INPROCEEDINGS{jordeh:2017:CEC, author={A. R. Jordehi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells}, year={2017}, editor = {Jose A. Lozano}, pages = {37--42}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Due to undeniable environmental, economical and technical reasons, renewable energy-based power generation in electric power systems is continually increasing. Among renewables, photovoltaic (PV) power generation is a viable and attractive choice. For modeling photovoltaic systems, accurate modeling of PV cells is a must. PV cells are often modeled as single diode or double diode models. The process of estimating circuit model parameters of PV cells based on datasheet information or experimental I-V measurements is called PV cell parameter estimation problem and is being frequently researched in the last three decades. The research effort is being put to achieve more accurate circuit model parameters. In this paper, gravitational search algorithm (GSA) with linearly decreasing gravitational constant is proposed for solving PV cell parameter estimation problem. The results of application of the proposed GSA to PV cell parameter estimation problem vividly show its outperformance over GSA with constant gravitational constant, GSA with exponentially decreasing gravitational constant, genetic algorithm, evolutionary programming and Newton algorithm.}, keywords = {diodes, evolutionary computation, parameter estimation, search problems, solar cells, PV cell parameter estimation problem, double diode models, electric power systems, gravitational constant, gravitational search algorithm, photovoltaic cells, photovoltaic power generation, photovoltaic systems, renewable energy-based power generation, single models, Biological system modeling, Computational modeling, Integrated circuit modeling, Linear programming, Mathematical model, Optimization, PV cells, PV modeling, metaheuristics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969293}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969293}}, } @INPROCEEDINGS{zhou:2017:CEC, author={Yawen Zhou and J. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A multi-agent genetic algorithm for multi-period emergency resource scheduling problems in uncertain traffic network}, year={2017}, editor = {Jose A. Lozano}, pages = {43--50}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={With the frequent occurrence of large-scale disasters, such as landslide and earthquake, timely and effective emergency resource scheduling becomes more and more important. Lots of disasters need multi-period rescue to satisfy the demand of disaster areas. In order to find a better plan to achieve the multi-period disaster relief, in this paper, a multi-period emergency resource scheduling problem is solved using the multi-agent genetic algorithm (MAGA) considering the uncertainty of traffic. The experimental results show that multi-agent genetic algorithm is more effective than genetic algorithm (GA) for this problem and it has better convergence.}, keywords = {disasters, emergency management, genetic algorithms, network theory (graphs), road traffic, scheduling, MAGA, convergence analysis, disaster areas, multiagent genetic algorithm, multiperiod disaster relief, multiperiod emergency resource scheduling problems, multiperiod rescue, traffic uncertainty, uncertain traffic network, Erbium, Linear programming, Optimization, Roads, Uncertainty, Emergency scheduling, Multi-agent genetic algorithm, Multi-period}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969294}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969294}}, } @INPROCEEDINGS{gan:2017:CEC, author={X. Gan and J. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A multi-objective evolutionary algorithm for emergency logistics scheduling in large-scale disaster relief}, year={2017}, editor = {Jose A. Lozano}, pages = {51--58}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The emergency logistics scheduling (ELS) is to enable the dispatch of emergency supplies to the victims of disasters timely and effectively, which plays a crucial role in large-scale disaster relief. In this paper, we first design a new multi-objective model that considers both the total unsatisfied time and transportation cost for the ELS problem in large-scale disaster relief (ELSP-LDR), which is on the scenery of multi-disasters and multi-suppliers with several kinds of resources and vehicles. Then, a modified non-dominated sorting genetic algorithm II (mNSGA-II) is proposed to search for a variety of optimal emergency scheduling plans for decision-makers. With the intrinsic properties of ELSP-LDR in mind, we design three repair operators to generate improved feasible solutions. Compared with the original NSGA-II, a local search operator is also designed for mNSGA-II, which significantly improves the performance. We conduct two experiments (the case of Chi-Chi earthquake and Great Sichuan Earthquake) to validate the performance of the proposed algorithm.}, keywords = {decision making, emergency management, genetic algorithms, logistics, scheduling, search problems, ELS, decision-makers, emergency logistics scheduling, emergency supply dispatching, large-scale disaster relief, local search operator, mNSGA-II, modified non-dominated sorting genetic algorithm II, multiobjective evolutionary algorithm, optimal emergency scheduling plans, Maintenance engineering, Optimization, Sociology, Sorting, Statistics, Transportation, disaster relief, multi-objective evolutionary algorithm, non-dominated sorting}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969295}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969295}}, } @INPROCEEDINGS{soncco-álvarez:2017:CEC, author={J. L. Soncco-Álvarez and M. Ayala-Rincón}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Variable neighborhood search for the large phylogeny problem using gene order data}, year={2017}, editor = {Jose A. Lozano}, pages = {59--66}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Computing evolutionary distances using gene order data is a complex combinatory problem; nevertheless, for specific metrics exact polynomial algorithms were proposed, having in many cases non trivial approaches. This scenario can become harder if we want to reconstruct phylogenies based on gene order data: first it is necessary to explore the search space of possible tree structures which is well-known to be exponential; second, it is necessary a method for evaluating the cost of these trees, i.e. to find a labeling of the internal nodes that leads to the most parsimonious cost of a tree under a given evolutionary distance. The latter problem was shown to be NP-hard even for 3 genomes (median problem) under many evolutionary distances. In this paper we propose a variable neighborhood search approach for solving the large phylogeny problem for data based on gene orders. Also, a greedy approach is proposed for the small phylogeny problem aiming to reduce the running time of the Kovac et al. dynamic programming approach. Our proposed algorithms were implemented as the software called HELPHY. Experiments showed that the running time is improved for finding trees with good scores (reversal distance) for the Campanulaceae dataset, and a new tree structure was found having the best known score (double cut and join distance) for the case of Hemiascomycetes dataset.}, keywords = {combinatorial mathematics, computational complexity, dynamic programming, evolution (biological), genetics, greedy algorithms, polynomials, search problems, trees (mathematics), Campanulaceae dataset, HELPHY software, Hemiascomycetes dataset, NP-hard problem, complex combinatory problem, evolutionary distance computing, exact polynomial algorithms, exponential tree structures, gene order data, greedy approach, large phylogeny problem, median problem, phylogenies reconstruction, reversal distance, search space, tree finding, variable neighborhood search, Biological cells, Genomics, Labeling, Phylogeny, Software, Space exploration}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969296}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969296}}, } @INPROCEEDINGS{budhraja:2017:CEC, author={K. K. Budhraja and T. Oates}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Neuroevolution-based Inverse Reinforcement Learning}, year={2017}, editor = {Jose A. Lozano}, pages = {67--76}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature-based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. This results in better correspondence to observed examples as opposed to using linear combinations. This work also extends existing work on Bayesian Non-Parametric Feature construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. This translates to real-world control problems such as those in robotics and automation (e.g. the robust output tracking problem or controlling an n-joint arm), where the underlying equations can be made linear. A conclusive performance hierarchy between evaluated algorithms is presented.}, keywords = {Bayes methods, Markov processes, decision theory, learning (artificial intelligence), neural nets, nonparametric statistics, Bayesian nonparametric feature construction, feature-based state evaluation, learning from demonstration, neural networks, neuroevolution-based inverse reinforcement learning, nondeterministic Markov decision processes, nonlinear combinations, state space, Genetic algorithms, Kernel, Robustness}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969297}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969297}}, } @INPROCEEDINGS{biswas:2017:CEC, author={P. P. Biswas and N. H. Awad and P. N. Suganthan and M. Z. Ali and G. A. J. Amaratunga}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Minimizing THD of multilevel inverters with optimal values of DC voltages and switching angles using LSHADE-EpSin algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {77--82}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multilevel inverters are mainly used for DC to AC power conversion and these inverters can be classified into types current source inverter (CSI) and voltage source inverter (VSI). Voltage source inverters are more common in power industry to convert lower levels of DC voltages into higher levels of AC voltages. In the process of conversion widely implemented pulse width modulated (PWM) switching technique of DC sources introduces harmonics in inverter output voltage. Total harmonic distortion (THD) is a measure of harmonic pollution in the power system and it is observed that variations in both DC voltages and switching angles of inverter affect the THD of inverter output voltage. Cascaded multilevel symmetric inverters ideally have DC sources all equal and constant. This paper considers inverters where DC sources can be unequal, a justifiable and realistic supposition. Optimal values of DC voltages and switching angles, which minimize THD level, are found using evolutionary algorithm. An advanced form of Differential Evolution (DE), called LSHADE-EpSin, is applied for the optimization problem. SHADE is a success history based parameter adaptation technique of DE. LSHADE improves the performance of SHADE with linearly reducing the population size in successive generations. LSHADE-EpSin introduces an additional adaptation technique for control parameters of the evolutionary algorithm. The algorithm has successfully been implemented for higher levels of inverters considered in the scope of our research study.}, keywords = {PWM invertors, evolutionary computation, harmonic distortion, DC to AC power conversion, DC voltages, LSHADE-EpSin algorithm, THD, current source inverter, differential evolution, evolutionary algorithm, history based parameter adaptation technique, multilevel inverters, pulse width modulated swiching, switching angles, total harmonic distortion, voltage source inverter, Bridge circuits, Harmonic analysis, Inverters, Optimization, Sociology, Statistics, Switches, LSHDAE EpSin algorithm, cascaded multi-level inverter, switching angle, total harmonic distortion (THD), unequal DC sources}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969298}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969298}}, } @INPROCEEDINGS{biswas:2017:CECa, author={P. P. Biswas and P. N. Suganthan and G. A. J. Amaratunga}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal placement of wind turbines in a windfarm using L-SHADE algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {83--88}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Setting of turbines in a windfarm is a complex task as several factors need to be taken into consideration. During recent years, researchers have applied various evolutionary algorithms to windfarm layout problem by converting it to a single objective and at the most two objective optimization problem. The prime factor governing placement of turbines is the wake effect attributed to the loss of kinetic energy by wind after it passes over a turbine. Downstream turbine inside the wake region generates less output power. Optimizing the wake loss helps extract more power out of the wind. The cost of turbine is tactically entwined with generated output to form single objective of cost per unit of output power e.g. cost/kW. This paper proposes an application of L-SHADE algorithm, an advanced form of Differential Evolution (DE) algorithm, to minimize the objective cost/kW. SHADE is a success history based parameter adaptation technique of DE. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. DE has historically been used mainly for optimization of continuous variables. The present study suggests an approach of using algorithm L-SHADE in discrete location optimization problem. Case studies of varying wind directions with constant and variable wind speeds have been performed and results are compared with some of the previous studies.}, keywords = {evolutionary computation, optimisation, wakes, wind power plants, wind turbines, DE, L-SHADE algorithm, differential evolution algorithm, discrete location optimization problem, downstream turbine, evolutionary algorithms, kinetic energy, optimal placement, optimization problem, variable wind speeds, wake effect, wake loss, windfarm, windfarm layout problem, Mathematical model, Optimization, Sociology, Statistics, Wind speed, LSHADE algorithm, efficiency, location optimization, wind turbine placement, windfarm power}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969299}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969299}}, } @INPROCEEDINGS{li:2017:CEC, author={Y. Li and T. Yue and S. Ali and L. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A multi-objective and cost-aware optimization of requirements assignment for review}, year={2017}, editor = {Jose A. Lozano}, pages = {89--96}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A typical way to improve the quality of requirements is to assign them to suitable stakeholders for reviewing. Due to different characteristics of requirements and diverse background of stakeholders, it is needed to find an optimal solution for requirements assignment. Existing search-based requirements assignment solutions focus on maximizing stakeholders' familiarities to assigned requirements and balancing the overall workload of each stakeholder. However, a cost-effective requirements assignment solution should also take into account another two optimization objectives: 1) minimizing required time for reviewing requirements, and 2) minimizing the monetary cost required for performing reviewing tasks. We formulated the requirements assignment problem as a search problem and defined a fitness function considering all the five optimization objectives. We conducted an empirical evaluation to assess the fitness function together with six search algorithms using a real-world case study and 120 artificial problems to assess the scalability of the proposed fitness function. Results show that overall, our optimization problem is complex and further justifies the use for multi-objective search algorithms, and the Speed-constrained Multi-Objective Particle Swarm Optimization (SMPSO) algorithm performed the best among all the search algorithms.}, keywords = {formal specification, minimisation, particle swarm optimisation, search problems, SMPSO, cost-aware optimization, cost-effective requirements assignment, fitness function, monetary cost minimization, multiobjective optimization, multiobjective search algorithms, optimization objectives, requirements quality, reviewing tasks, search problem, search-based requirements assignment, speed-constrained multiobjective particle swarm optimization, time minimization, workload balancing, Complexity theory, Optimization, Software algorithms, Software engineering, Stakeholders, Standards, muti-objectives search algorithms, requirements assignment, search-based software engineering}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969300}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969300}}, } @INPROCEEDINGS{tagawa:2017:CEC, author={K. Tagawa and S. Miyanaga}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Weighted empirical distribution based approach to Chance Constrained Optimization Problems using Differential Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {97--104}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a new approach to solve Chance Constrained Optimization Problems (CCOPs). The stochastic objective and constraint values in CCOP are evaluated efficiently by using an approximation of Cumulative Distribution Function (CDF) instead of the primitive Monte Carlo simulation. In order to approximate CDF from samples, a technique of the computational statistics called Empirical CDF (ECDF) is widely known. In this paper, an improved version of ECDF named Weighted Empirical CDF (W ECDF) is used. Then, for solving CCOP, a modified Differential Evolution (DE) combined with W ECDF is proposed. The results of numerical experiments show that DE with W ECDF finds a feasible solution of CCOP and outperforms DE with ECDF in the accuracy of solution.}, keywords = {approximation theory, evolutionary computation, optimisation, statistics, stochastic processes, CCOPs, chance constrained optimization problems, computational statistics, constraint values, cumulative distribution function approximation, differential evolution, stochastic objective, weighted empirical CDF, weighted empirical distribution based approach, Distribution functions, Monte Carlo methods, Optimization, Probability density function, Random variables, Robustness, Uncertainty}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969301}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969301}}, } @INPROCEEDINGS{bhattacharjee:2017:CEC, author={K. S. Bhattacharjee and H. K. Singh and T. Ray and Q. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Decomposition Based Evolutionary Algorithm with a Dual Set of reference vectors}, year={2017}, editor = {Jose A. Lozano}, pages = {105--112}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Decomposition based approaches are increasingly being used to solve many-objective optimization problems (MaOPs). In such approaches, the MaOP is decomposed into several single-objective sub-problems and solved simultaneously guided by a set of predefined, uniformly distributed reference vectors. The reference vectors are constructed by joining a set of uniformly sampled points to the ideal point. Use of such reference vectors originating from the ideal point has so far performed reasonably well on common benchmarks such as DTLZs and WFGs, since the geometry of their Pareto fronts can be easily mapped using these reference vectors. However, the approach may not deliver a set of well distributed solutions for problems with Pareto fronts which are convex/concave or where the shape of the Pareto front is not best suited for such set of reference vectors (e.g. minus series of DTLZ and WFG test problems). While the notion of reference vectors originating from the nadir point has been suggested in the literature in the past, they have rarely been used in decomposition based algorithms. Such reference vectors are complementary in nature with the ones originating from the ideal point. Therefore, in this paper, we introduce a decomposition based approach which attempts to use both these two sets of reference vectors and chooses the most appropriate set at each generation based on the s-energy metric. The performance of the approach is presented and objectively compared with a number of recent algorithms. The results clearly highlight the benefits of such an approach especially when the nature of the Pareto front is not known a priori.}, keywords = {Pareto optimisation, evolutionary computation, set theory, vectors, MaOP, Pareto fronts, energy metric, evolutionary algorithm, geometry, ideal point, manyobjective optimization problems, single objective subproblems, uniformly distributed reference vectors, uniformly sampled points, Electronic mail, Optical fibers, Optimization, Sociology, Statistics, Systematics, Adaptive, Decomposition, Many-objective optimization, Reference directions}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969302}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969302}}, } @INPROCEEDINGS{vanneschi:2017:CEC, author={L. Vanneschi and I. Bakurov and M. Castelli}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An initialization technique for geometric semantic GP based on demes evolution and despeciation}, year={2017}, editor = {Jose A. Lozano}, pages = {113--120}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Initializing the population is a crucial step for genetic programming, and several strategies have been proposed so far. The issue is particularly important for geometric semantic genetic programming, where initialization is known to play a very important role. In this paper, we propose an initialization technique inspired by the biological phenomenon of demes despeciation, i.e. the combination of demes of previously distinct species into a new population. In synthesis, the initial population of geometric semantic genetic programming is created using the best individuals of a set of separate subpopulations, or demes, some of which run standard genetic programming and the others geometric semantic genetic programming for few generations. Geometric semantic genetic programming with this novel initialization technique is shown to outperform geometric semantic genetic programming using the traditional ramped half-and-half algorithm on six complex symbolic regression applications. More specifically, on the studied problems, the proposed initialization technique allows us to generate solutions with comparable or even better generalization ability, and of significantly smaller size than the ramped half-and-half algorithm.}, keywords = {genetic algorithms, genetic programming, regression analysis, biological phenomenon, complex symbolic regression applications, demes despeciation, demes evolution, geometric semantic GP, initialization technique, Evolution (biology), Semantics, Sociology, Standards, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969303}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969303}}, } @INPROCEEDINGS{vanneschi:2017:CECa, author={L. Vanneschi and B. Galvão}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A parallel and distributed semantic Genetic Programming system}, year={2017}, editor = {Jose A. Lozano}, pages = {121--128}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the last few years, geometric semantic genetic programming has incremented its popularity, obtaining interesting results on several real life applications. Nevertheless, the large size of the solutions generated by geometric semantic genetic programming is still an issue, in particular for those applications in which reading and interpreting the final solution is desirable. In this paper, we introduce a new parallel and distributed genetic programming system, with the objective of mitigating this drawback. The proposed system (called MPHGP, which stands for Multi-Population Hybrid Genetic Programming) is composed by two subpopulations, one of which runs geometric semantic genetic programming, while the other runs a standard multi-objective genetic programming algorithm that optimizes, at the same time, training error and the size of the solutions. The two subpopulations evolve independently and in parallel, exchanging individuals at prefixed synchronization instants. The presented experimental results, obtained on five real-life symbolic regression applications, suggest that MPHGP is able to find solutions that are comparable, or even better, than the ones found by geometric semantic genetic programming, both on training and on unseen testing data. At the same time, MPHGP is also able to find solutions that are significantly smaller than the ones found by geometric semantic genetic programming.}, keywords = {genetic algorithms, genetic programming, algorithm theory, geometry, MPHGP, distributed semantic genetic programming system, geometric semantic genetic programming, multiobjective genetic programming algorithm, multipopulation hybrid genetic programming, prefixed synchronization instants, symbolic regression applications, Optimization, Semantics, Sociology, Standards, Statistics, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969304}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969304}}, } @INPROCEEDINGS{blum:2017:CEC, author={C. Blum and M. J. Blesa}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem}, year={2017}, editor = {Jose A. Lozano}, pages = {129--136}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of art-annotated ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.}, keywords = {RNA, combinatorial mathematics, computational complexity, evolutionary computation, medical computing, string matching, NP-hard combinatorial optimization problem, RNA sequences, art-annotated ribonucleic acid sequences, computational biology, hybrid evolutionary algorithm, longest arc-preserving common subsequence problem, solution merging, Computer science, Context, Heuristic algorithms, Merging, Optimized production technology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969305}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969305}}, } @INPROCEEDINGS{costa:2017:CEC, author={M. H. Costa and M. G. Ravetti and R. R. Saldanha and E. G. Carrano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary multiobjective based approach to improve robustness in directional overcurrent relay coordination}, year={2017}, editor = {Jose A. Lozano}, pages = {137--144}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The coordination of electrical power system protection relays is a hard problem to solve, due to its discrete/nonlinear nature and its complex constraint structure. A multiobjective algorithm to coordinate directional overcurrent relays is proposed in this work. Two objective functions are considered: minimization of the sum of relay operation times, and; maximization of the minimum coordination time interval between relay pairs (primary and backup). The second objective is novel, and we prove to be very useful to improve the coordination robustness. The search for efficient solutions is performed by a matheuristic algorithm, which combines Non-dominated Sorting Genetic Algorithm II, Differential Evolution and Mathematical Programming formulations. Results for four case studies from the literature suggest that the proposed approach is able to generate solutions that are equivalent, or even better, than the ones obtained with mono-objective approaches. In addition, the proposed algorithm can find some solutions that are considerably more robust taking into account variations in system conditions.}, keywords = {genetic algorithms, mathematical programming, power system protection, relays, coordination time interval, differential evolution, directional overcurrent relay coordination, electrical power system protection relays, evolutionary multiobjective based approach, metaheuristic algorithm, multiobjective algorithm, nondominated sorting genetic algorithm II, objective functions, relay operation time, sum minimization, Linear programming, Optimization, Robustness, Sociology, Sorting, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969306}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969306}}, } @INPROCEEDINGS{mohamed:2017:CEC, author={A. W. Mohamed and A. A. Hadi and A. M. Fattouh and K. M. Jambi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems}, year={2017}, editor = {Jose A. Lozano}, pages = {145--152}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={To improve the optimization performance of LSHADE algorithm, an alternative adaptation approach for the selection of control parameters is proposed. The proposed algorithm, named LSHADE-SPA, uses a new semi-parameter adaptation approach to effectively adapt the values of the scaling factor of the Differential evolution algorithm. The proposed approach consists of two different settings for two control parameters F and Cr. The benefit of this approach is to prove that the semi-adaptive algorithm is better than pure random algorithm or fully adaptive or self-adaptive algorithm. To enhance the performance of our algorithm, we also introduced a hybridization framework named LSHADE-SPACMA between LSHADE-SPA and a modified version of CMA-ES. The modified version of CMA-ES undergoes the crossover operation to improve the exploration capability of the proposed framework. In LSHADE-SPACMA both algorithms will work simultaneously on the same population, but more populations will be assigned gradually to the better performance algorithm. In order to verify and analyze the performance of both LSHADE-SPA and LSHADE-SPACMA, Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with LSHADE algorithm are executed. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHADE-SPA and LSHADE-SPACMA are better than LSHADE algorithm, especially as the dimension increases.}, keywords = {evolutionary computation, optimisation, CEC 2017 benchmark problems, CMA-ES, LSHADE algorithm, LSHADE-SPACMA, control parameter selection, crossover operation, differential evolution, exploration capability, hybridization framework, optimization performance, scaling factor, semiadaptive algorithm, semiparameter adaptation hybrid, Algorithm design and analysis, Convergence, Gaussian distribution, Optimization, Search problems, Sociology, Statistics, LSHADE, Numerical Optimization, Parameter adaptation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969307}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969307}}, } @INPROCEEDINGS{murphy:2017:CEC, author={R. Murphy and D. Fagan and M. O'Neill}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Gems: A novel method to accelerate Evolutionary Algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {153--160}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents gems, a novel method to accelerate fitness improvement in Evolutionary Algorithms (EAs). The paper develops the models, describes an experimental implementation, comments on characteristics of problem-domains that indicate where gems may be used, and suggests an explanation of the observed behavior. Experimental results show that gems accelerate the rate of fitness increase, and that the larger the problem instance, the larger the benefit. Runtime analysis shows that the method's fitness boost far outweighs its performance costs.}, keywords = {evolutionary computation, EA, evolutionary algorithms, runtime analysis, Acceleration, Biological cells, Memetics, Probability, Sociology, Statistics, TSP, algorithm, evolutionary, exploitation, exploration, gem, genetic, jewellery-box, mutation, schema}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969308}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969308}}, } @INPROCEEDINGS{holladay:2017:CEC, author={K. Holladay and K. Pickens and G. Miller}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The effect of evaluation time variance on asynchronous Particle Swarm Optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {161--168}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Optimizing computationally intensive models of real-world systems can be challenging, especially when significant wall clock time is required for a single evaluation of a model. Employing multiple CPUs is a common mitigation strategy, but algorithms that rely on synchronous execution of model instances can waste significant CPU cycles if there is variability in the model evaluation time. In this paper, we explore the effect of model run time variance on the behavior of PSO using both synchronous and completely asynchronous particle updates. Results indicate that in most cases, asynchronous updates save considerable time while not significantly impacting the probability of finding a solution.}, keywords = {particle swarm optimisation, probability, PSO, asynchronous particle swarm optimization, asynchronous particle updates, evaluation time variance, real-world systems, Algorithm design and analysis, Benchmark testing, Clocks, Computational modeling, Optimization, Particle swarm optimization, Topology, asynchronous}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969309}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969309}}, } @INPROCEEDINGS{oksanen:2017:CEC, author={K. Oksanen and Ting Hu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming}, year={2017}, editor = {Jose A. Lozano}, pages = {169--176}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Linear Genetic Programming (LGP) is an evolutionary algorithm aimed at solving computational problems, most common problem types being symbolic regression and classification. The standard method for selecting the parent individuals that get to undergo modification at each generation of the algorithm is tournament selection, which operates based on an aggregate fitness value computed on the whole training dataset. Lexicase selection, a novel parent selection method introduced by Lee Spector and his research group, works differently by randomly ordering the samples in the training dataset and using each of them in turn to eliminate parent candidates from consideration. As a result it allows for selecting specialist individuals, which perform well on some samples but badly on others, instead of generalist individuals whose average performance on all of the samples is good. Lexicase selection has previously been tested on tree-GP and PushGP, but not on LGP. In this study, we use three different benchmark problems to compare its performance to tournament selection, investigating the mean best fitness values of the test runs at each generation, as well as the effect of the parent selection operator on behavioural diversity. We conclude that lexicase selection drives the search towards good solutions more effectively than tournament selection, and that this effect correlates with improved behavioural diversity in most cases.}, keywords = {genetic algorithms, genetic programming, linear programming, LGP, Lee Spector, aggregate fitness value, behavioural diversity, evolutionary algorithm, lexicase selection, linear genetic programming, parent candidates, parent selection method, parent selection operator, standard method, symbolic regression, tournament selection, Benchmark testing, Registers, Sociology, Spirals, Standards, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969310}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969310}}, } @INPROCEEDINGS{vanneschi:2017:CECb, author={L. Vanneschi and M. Castelli and I. Gonçalves and L. Manzoni and S. Silva}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Geometric semantic genetic programming for biomedical applications: A state of the art upgrade}, year={2017}, editor = {Jose A. Lozano}, pages = {177--184}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Geometric semantic genetic programming is a hot topic in evolutionary computation and recently it has been used with success on several problems from Biology and Medicine. Given the young age of geometric semantic genetic programming, in the last few years theoretical research, aimed at improving the method, and applicative research proceeded rapidly and in parallel. As a result, the current state of the art is confused and presents some “holes”. For instance, some recent improvements of geometric semantic genetic programming have never been applied to some popular biomedical applications. The objective of this paper is to fill this gap. We consider the biomedical applications that have more frequently been used by genetic programming researchers in the last few years and we systematically test, in a consistent way, using the same parameter settings and configurations, all the most popular existing variants of geometric semantic genetic programming on all those applications. Analysing all these results, we obtain a much more homogeneous and clearer picture of the state of the art, that allows us to draw stronger conclusions.}, keywords = {genetic algorithms, genetic programming, medical computing, biomedical applications, evolutionary computation, geometric semantic genetic programming, parameter settings, Drugs, Electronic mail, GSM, Proteins, Semantics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969311}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969311}}, } @INPROCEEDINGS{cota:2017:CEC, author={L. P. Cota and F. G. Guimarães and F. B. de Oliveira and M. J. F. Souza}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An Adaptive Large Neighborhood Search with Learning Automata for the Unrelated Parallel Machine Scheduling Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {185--192}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This work deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times, with the objective of minimizing the makespan. It is proposed an Adaptive Large Neighborhood Search (ALNS) metaheuristic using Learning Automata (LA) to adapt the probabilities of using removal and insertion heuristics and methods. A computable function in the LA updates the probability vector for selecting the actions, corresponding to six removal and six insertion methods. We also propose a new insertion method based on Hungarian algorithm, which is applied to solve subproblems optimally. Computational experiments are performed to verify the performance of the proposed method. A set of instances available in the literature with problems up to 150 jobs and 10 machines is employed in the experiments. The proposed LA-ALNS is compared against three other algorithms from the literature. The results suggest that our algorithm has better performance in most of cases (88%) under the defined conditions of experiments. Statistical tests also suggest that LA-ALNS is better than the other algorithms from the literature. The proposed method is able to automatically choose the most suitable heuristics for the instance of the problem, through adaptation and learning in the Learning Automata.}, keywords = {learning automata, minimisation, scheduling, search problems, ALNS metaheuristic, Hungarian algorithm, LA-ALNS, adaptive-large neighborhood search, insertion method, makespan minimization, probability vector update, removal method, statistical tests, unrelated parallel machine scheduling problem-with-setup times, Correlation, Job shop scheduling, Mathematical model, Parallel machines, Resource management}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969312}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969312}}, } @INPROCEEDINGS{xia:2017:CEC, author={G. Xia and S. A. Ludwig}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Object tracking using Particle Swarm Optimization and Earth mover's distance}, year={2017}, editor = {Jose A. Lozano}, pages = {193--200}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Visual object tracking is an active research field in the area of computer vision. The tracking process usually includes the construction of an object appearance model and the object localization. This paper investigates the use of Particle Swarm Optimization (PSO) as the object localization method based on the Bayesian tracking framework. The widely adopted particle filter tracking technique, however, suffers from high computational cost due to the approximation requirement of the distribution of particles. Thus, PSO is applied since it can adaptively adjust the computational expenditure according to each frame in the video. Furthermore, a new appearance model based on Earth mover's distance is proposed. The experimental results show that the proposed approach enhances the accuracy of the tracking algorithm significantly compared to the basic particle filter tracking method. Furthermore, the proposed appearance model is more robust than other Earth mover's distance based tracking algorithms.}, keywords = {Bayes methods, computer vision, object tracking, particle swarm optimisation, video signal processing, Bayesian tracking, Earth mover's distance, PSO, object appearance model, object localization, particle swarm optimization, video frame, visual object tracking, Adaptation models, Histograms, Image color analysis, Target tracking}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969313}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969313}}, } @INPROCEEDINGS{fränz:2017:CEC, author={F. Fränz and J. Paredis and R. Möckel}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On the combination of coevolution and novelty search}, year={2017}, editor = {Jose A. Lozano}, pages = {201--208}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper develops a new method for coevolution, named Fitness-Diversity Driven Coevolution (FDDC). This approach builds on existing methods by a combination of a (predator-prey) Coevolutionary Genetic Algorithm (CGA) and novelty search. The innovation lies in replacing the absolute novelty measure with a relative one, called Fitness-Diversity. FDDC overcomes problems common in both CGAs (premature convergence and unbalanced coevolution) and in novelty search (construction of an archive). As a proof of principle, Spring Loaded Inverted Pendulums (SLIPs) are coevolved with 2D-terrains the SLIPs must learn to traverse.}, keywords = {convergence, genetic algorithms, nonlinear systems, pendulums, search problems, springs (mechanical), 2D-terrains, CGA, FDDC, SLIP, absolute novelty measure, coevolutionary genetic algorithm, fitness-diversity driven coevolution, novelty search, premature convergence, spring loaded inverted pendulums, unbalanced coevolution, Linear programming, Optimization, Sociology, Springs, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969314}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969314}}, } @INPROCEEDINGS{fan:2017:CEC, author={Z. Fan and Yi Fang and W. Li and Jiewei Lu and X. Cai and Caimin Wei}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A comparative study of constrained multi-objective evolutionary algorithms on constrained multi-objective optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {209--216}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Solving constrained multi-objective optimization problems is a difficult task, it needs to simultaneously optimize multiple conflicting objectives and a number of constraints. This paper first reviews a number of popular constrained multi-objective evolutionary algorithms (CMOEAs) and twenty-three widely used constrained multi-objective optimization problems (CMOPs) (including CF1-10, CTP1-8, BNH, CONSTR, OSY, SRN and TNK problems). Then eight popular CMOEAs with simulated binary crossover (SBX) and differential evolution (DE) operators are selected to test their performance on the twenty-three CMOPs. The eight CMOEAs can be classified into domination-based CMOEAs (including ATM, IDEA, NSGA-II-CDP and SP) and decomposition-based CMOEAs (including CMOEA/D, MOEA/D-CDP, MOEA/D-SR and MOEA/D-IEpsilon). The comprehensive experimental results indicate that IDEA has the best performance in the domination-based CMOEAs and MOEA/D-IEpsilon has the best performance in the decomposition-based CMOEAs. Among the eight CMOEAs, MOEA/D-IEpsilon with both SBX and DE operators has the best performance on the twenty-three test problems.}, keywords = {evolutionary computation, optimisation, CMOEA, CMOP, DE, SBX, constrained multiobjective evolutionary algorithms, constrained multiobjective optimization problems, differential evolution, simulated binary crossover, Algorithm design and analysis, Pareto optimization, Sociology, Sorting}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969315}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969315}}, } @INPROCEEDINGS{saraiva:2017:CEC, author={F. Saraiva and L. Nordström and E. Asada}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-agent systems applied to power loss minimization in distribution-level smart grid with dynamic load variation}, year={2017}, editor = {Jose A. Lozano}, pages = {217--224}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Modern distribution system are expected to provide new features such as taking advantage of Cyber-Physical Systems (CPS) - new equipment and devices embedded with sensors, network communication, and computational intelligence techniques to provide increased system performance and power quality. Among the performance improvement, the reduction of electrical losses is an important quality factor which is associated with energy efficiency. This paper presents a method based on Multi-agent Systems (MAS) that manages topology changes by switching operations to improve the system performance in dynamic scenario, where the power demand varies throughout the day. Experiments were performed allocating three different load consumer profiles (residential, commercial, and industrial) in two test systems with 12-bus and 16-bus, creating several scenarios. The agents were deployed in a set of small-sized single-board computers with low computational power to mimic CPS. The simulations has shown the success of the method on managing the decision making among different agents to provide the joint effort to manage the loss reduction on the network.}, keywords = {Q-factor, cyber-physical systems, energy conservation, load management, microcomputers, multi-agent systems, power distribution, power engineering computing, smart power grids, CPS, MAS, commercial allocation, computational intelligence techniques, decision making, distribution system, distribution-level smart grid, dynamic load variation, electrical loss reduction, energy efficiency, industrial allocation, load consumer profile allocation, multiagent systems, network communication, power demand, power loss minimization, quality factor, residential allocation, small-sized single-board computers, switching operations, topology change management, Computational modeling, Minimization, Smart grids, Switches, Distributed Computing Systems, Power Distribution Systems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969316}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969316}}, } @INPROCEEDINGS{chand:2017:CEC, author={S. Chand and H. K. Singh and T. Ray}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A heuristic algorithm for solving resource constrained project scheduling problems}, year={2017}, editor = {Jose A. Lozano}, pages = {225--232}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Resource constrained project scheduling problem (RCPSP) is one of the classical problems in the area of discrete optimization. In this paper we propose an algorithm for solving RCPSP which relies on an adaptive insertion mutation operator that targets different regions of the search space. Neighbourhoods are exploited via forward-backward iterative local search. Furthermore, the algorithm makes use of an archive to ensure better utilization of the schedule budget. The performance of the approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLib with budgets of 1,000, 5,000 and 50,000 schedules. The study provides insights on the performance of the algorithm i.e. why the performance is good for particular instances and not as good for others.}, keywords = {heuristic programming, optimisation, resource allocation, scheduling, search problems, PSPLib, RCPSP, adaptive insertion mutation operator, discrete optimization, forward-backward iterative local search, heuristic algorithm, resource constrained project scheduling problems, schedule budget, search space, Algorithm design and analysis, Job shop scheduling, Maintenance engineering, Schedules, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969317}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969317}}, } @INPROCEEDINGS{abdelkafi:2017:CEC, author={O. Abdelkafi and L. Idoumghar and J. Lepagnot and J. L. Paillaud}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={MEmory Genetic Algorithm Hybridized for Zeolites}, year={2017}, editor = {Jose A. Lozano}, pages = {233--240}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Zeolite structure determination is an interesting challenge even with the progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this problem make the use of an evolutionary algorithm significant for this challenge. In this paper, we propose a new parallel and distributed hybrid genetic algorithm called MEmory Genetic Algorithm Hybridized for Zeolite (MEGA-HZ). This experimentation shows that the proposed algorithm is able to satisfy the constraints of the objective function to determine viable zeolite structures. From the 6 unit cell parameters and density, the MEGA-HZ has found 6 different viable zeolite structures.}, keywords = {X-rays, chemistry, constraint satisfaction problems, electron diffraction, genetic algorithms, parallel algorithms, zeolites, MEGA-HZ, computational chemistry, constraints satisfaction, distributed hybrid genetic algorithm, evolutionary algorithm, memory genetic algorithm hybridized for zeolite, parallel hybrid genetic algorithm, structural resolution, zeolite structure determination, Crystals, Gold, Linear programming, Silicon, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969318}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969318}}, } @INPROCEEDINGS{iantovics:2017:CEC, author={L. B. Iantovics and A. Gligor and V. Georgieva}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Detecting Outlier Intelligence in the behavior of intelligent coalitions of agents}, year={2017}, editor = {Jose A. Lozano}, pages = {241--248}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In our research, we consider the measuring of machine intelligence based on the intelligence (ability to solve various tasks in high efficiency, with a grade of flexibility and robustness) in solving difficult problems/tasks (NP-hard and/or have different types of uncertainties). An intelligent cooperative coalition of agents (could be a whole cooperative multiagent system or a part of it) have variability in the problem-solving intelligence. It is able to solve more or less intelligently different problems. For some problems solving it could manifest even extremely low or extremely high intelligence. We call such intelligence values as outlier intelligence, which could be low outlier intelligence values or high outlier intelligence values. In this paper, we propose a novel method called OutIntDet (Outlier Intelligence Detection Method) for detecting outlier intelligence values. In order to sustain the effectiveness of the proposed method, a case study where we considered a coalition of agents which solve an NP-hard problem was performed. OutIntDet could be useful to be implemented in some intelligence metrics that are based on measuring problems-solving intelligence, being able to detect low and high outlier intelligence. This could make the metrics more accurate and robust. OutIntDet is also appropriate for the identification of the problems for whose solving the coalition manifest very low or very high intelligence. There is also presented an appropriate calculation of the MIQ (Machine Intelligence Quotient) based on the properties of measured problem-solving intelligence data.}, keywords = {artificial intelligence, computational complexity, multi-agent systems, problem solving, software agents, MIQ, NP-hard problems, OutIntDet, cooperative multiagent system, intelligence metrics, intelligent agent coalition behavior, intelligent agent cooperative coalition, machine intelligence quotient, outlier intelligence detection method, problem identification, problem-solving intelligence, problems/tasks solving, Intelligent systems, Machine intelligence, Measurement, Problem-solving, Robots, Robustness, computational intelligence, intelligent agent coalition, intelligent system, machine intelligence measure, outlier intelligence}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969319}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969319}}, } @INPROCEEDINGS{williams:2017:CEC, author={R. A. Williams and J. Timmis and E. E. Qwarnstrom}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Investigating IKK dynamics in the NF- #x03BA;B signalling pathway using X-Machines}, year={2017}, editor = {Jose A. Lozano}, pages = {249--256}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. We have developed an agent-based model of the IL-1 stimulated NF-κB signalling pathway, which has been calibrated to wet-lab data at the single-cell level. Through rigorous software engineering, we believe our model provides an abstracted view of the underlying real-world system, and can be used in a predictive capacity through in silico experimentation. In this study, we have focused on the dynamics of the IKK complex and its activation of NF-κB. Our agent-based model suggests that the pathway is sensitive to: variations in the binding probability of IKK to the inhibited NF-κB-IκBα complex; and variations in the temporal rebinding delay of IKK.}, keywords = {biology computing, diseases, genetics, multi-agent systems, probability, software engineering, IKK dynamics, IL-1 stimulated NF-κB signalling pathway, NF-κB transcription factor, X-machines, agent-based model, binding probability, biological component, gene regulation, inflammatory diseases, innate immune system, pathway dysregulation, temporal rebinding delay, Biological system modeling, Biomembranes, Computational modeling, Data models, Immune system, Proteins, Unified modeling language}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969320}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969320}}, } @INPROCEEDINGS{anyaiwe:2017:CEC, author={O. E. D. Anyaiwe and G. B. Singh and G. D. Wilson and T. J. Geddes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Weighted Manhattan Distance Classifier; SELDI data for Alzheimer's disease diagnosis}, year={2017}, editor = {Jose A. Lozano}, pages = {257--262}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Mass Spectrometry (Surface Enhanced Laser Desorption Time of Flight (SELDI-TOF) assay technique) for proteomics is based on the consistency and reproducibility of protein/peptide expressions. In this study, we opine that mining collections of mass spectra data instead of detailed study of individual ions generated in the course of Mass Spectrometer assay process, will generate discriminative factors for the diagnosis of Alzheimer's Disease (and other diseases in general). This model; Weighted Manhattan Distance Classifier (WMDC), classifies a test vector to the stage label of the most significant train vector to it using Manhattan Distance function and thereafter, classifies a test data point (a collection of test vectors) to the disease stage having the majority of most significant train vectors in it. The disease severity is categorized as normal/control, mild and acute impaired stages, each of which contained 20 SELDI-TOF analysis results. In all, the database contained 60 assay results of saliva analytes or protein source samples under 3 proteinChips; CM10, IMAC30 and Q10. Each laboratory experiment was performed with either low (1800 nJ) or high (4000 nJ) laser energy bombardment level. 90% classification result was obtained with a probability of 0.075 for committing type II error (that is, a test power of 0.925).}, keywords = {biology computing, diseases, patient diagnosis, pattern classification, proteins, proteomics, Alzheimer disease diagnosis, CM10, IMAC30, Q10, SELDI data, SELDI-TOF, WMDC, classification result, disease stage, laser energy bombardment level, mass spectra data, mass spectrometer assay process, protein source samples, protein-peptide expressions, proteinChips, saliva analytes, surface enhanced laser desorption time of flight assay technique, train vector, weighted Manhattan distance classifier, Alzheimer's disease, Biological system modeling, Databases, Ions, Peptides, Feature Matrix, Jackknifing, Mass Spectrometer Data, matrix-to-matrix discrimination}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969321}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969321}}, } @INPROCEEDINGS{cheng:2017:CEC, author={Ran Cheng and Miqing Li and X. Yao}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Parallel peaks: A visualization method for benchmark studies of multimodal optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {263--270}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multimodal optimization has attracted increasing interest recently. Despite the emergence of various multimodal optimization algorithms during the last decade, little work has been dedicated to the development of benchmark tools. In this paper, we propose a visualization method for benchmark studies of multimodal optimization, called parallel peaks. Inspired by parallel coordinates, the proposed parallel peaks method is capable of visualizing both distribution information and convergence information of a given candidate solution set inside a 2D coordinate plane. To the best of our knowledge, this is the first visualization method in the multimodal optimization area. Our empirical results demonstrate that the proposed parallel peaks method can be robustly used to visualize candidate solutions sets with a range of properties, including high-accuracy solutions sets, high-dimensional solution sets and solution sets with a large number of optima. Additionally, by visualizing the populations obtained during the optimization process, it can also be used to investigate search behaviors of multimodal optimization algorithms.}, keywords = {convergence, optimisation, search problems, 2D coordinate plane, convergence information, distribution information, high-accuracy solutions sets, high-dimensional solution sets, multimodal optimization, parallel coordinates, parallel peaks method, search behaviors, visualization method, Benchmark testing, Data visualization, Optimization, Sociology, Statistics, Two dimensional displays, Visualization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969322}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969322}}, } @INPROCEEDINGS{chuang:2017:CEC, author={C. Y. Chuang and S. F. Smith}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A study of agnostic hyper-heuristics based on sampling solution chains}, year={2017}, editor = {Jose A. Lozano}, pages = {271--278}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we study a simple hyper-heuristic that functions by sampling solution chains. A solution chain in this algorithm is formed by successively applying a randomly chosen heuristic to the previous solution to generate the next solution. Operating in this way, the algorithm can benefit from the accumulated effect of applying multiple heuristics. A key factor in this algorithm is the strategy for choosing the sampling length. We discuss a balanced strategy in a setting that contains two agnostic assumptions: First, we do not have detailed knowledge about the problem domain being solved except that we have access to the objective function and a set of predefined heuristics. Secondly, we have no information about the amount of time allocated for running our algorithm. We present a theoretical guarantee on using this strategy to choose the sampling lengths and derive some variants based on this strategy. Empirical results also confirm that these strategies deliver desired behavior. Finally, we briefly discuss the extension of incorporating a learning mechanism into the algorithm.}, keywords = {learning (artificial intelligence), optimisation, sampling methods, agnostic hyper-heuristics, learning mechanism, objective function, sampling length, sampling solution chains, Electronic mail, Heuristic algorithms, Learning systems, Linear programming, Optimization, Robots, Search problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969323}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969323}}, } @INPROCEEDINGS{paredi:2017:CEC, author={J. Paredis}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Exploring the evolution of Genotype Phenotype Mappings}, year={2017}, editor = {Jose A. Lozano}, pages = {279--285}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper investigates the evolution of two types of simple Genotype Phenotype Mappings (GPMs): a many-to-one mapping and a one-to-many mapping. Both GPMs are under genetic control. For both types of mappings different Regions Of Maximum Adaptability (ROMAs) are found. These ROMAs are the regions - in a paramterized space of GPMs - evolution leads to. The attraction towards these ROMAs increases as selection pressure increases. Finally, this paper discusses the evolution of pleiotropy and the ROMAs it leads to.}, keywords = {biology computing, genomics, GPM, ROMA, genotype phenotype mappings, regions of maximum adaptability, Bioinformatics, Complexity theory, History, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969324}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969324}}, } @INPROCEEDINGS{nguyen:2017:CEC, author={H. B. Nguyen and B. Xue and P. Andreae and M. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Particle Swarm Optimisation with genetic operators for feature selection}, year={2017}, editor = {Jose A. Lozano}, pages = {286--293}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Feature selection is an important task in machine learning, which aims to reduce the dataset dimensionality while at least maintaining the classification performance. Particle Swarm Optimisation (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. However, since feature selection is a challenging task with a complex search space, PSO easily gets stuck at local optima. This paper aims to improve the PSO's searching ability by applying genetic operators such as crossover and mutation to assist the swarm to explore the search space better. The proposed genetic operators are specifically designed for feature selection, which not only improve the quality of current feature subsets but also make the search smoother. The proposed algorithm, called CMPSO, is tested and compared with three recent PSO based feature selection algorithms. Experimental results on eight datasets show that CMPSO can adapt with different numbers of features to evolve small feature subsets, which achieve similar or better classification performance than using all features and the three PSO based algorithms. The analysis on evolutionary processes shows that genetic operators assist CMPSO to evolve better solutions than the original PSO.}, keywords = {feature selection, learning (artificial intelligence), particle swarm optimisation, CMPSO algorithm, PSO, crossover operator, dataset dimensionality reduction, genetic operators, machine learning, mutation operator, Algorithm design and analysis, Convergence, Genetic algorithms, Genetics, Optimization, Particle swarm optimization, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969325}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969325}}, } @INPROCEEDINGS{ling:2017:CEC, author={Xianyao Ling and Xinxin Feng and Zhonghui Chen and Yiwen Xu and Haifeng Zheng}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine}, year={2017}, editor = {Jose A. Lozano}, pages = {294--300}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Accurate prediction of the traffic state can help to solve the problem of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm, which is based on Multi-kernel Support Vector Machine (MSVM) and Adaptive Particle Swarm Optimization (APSO). Firstly, we explore both the nonlinear and randomness characteristic of traffic flow, and hybridize Gaussian kernel and polynomial kernel to constitute the MSVM. Secondly, we optimize the parameters of MSVM with a novel APSO algorithm by considering both the historical and real-time traffic data. We evaluate our algorithm by doing thorough experiment on a large real dataset. The results show that our algorithm can do a timely and adaptive prediction even in the rush hour when the traffic conditions change rapidly. At the same time, the prediction results are more accurate compared to four baseline methods.}, keywords = {particle swarm optimisation, support vector machines, traffic, Gaussian kernel, MSVM, adaptive particle swarm optimization, novel APSO algorithm, optimized multikernel support vector machine, polynomial kernel, short-term traffic flow prediction, Forecasting, Kernel, Particle swarm optimization, Prediction algorithms, Predictive models, Real-time systems, Multi-kernel Support Vector Machine, Traffic flow prediction}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969326}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969326}}, } @INPROCEEDINGS{liang:2017:CEC, author={Yongsheng Liang and Zhigang Ren and L. Wang and Bei Pang and M. M. Hossain}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Inferior solutions in Gaussian EDA: Useless or useful?}, year={2017}, editor = {Jose A. Lozano}, pages = {301--307}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Estimation of distribution algorithms (EDAs) are a special class of model-based evolutionary algorithms (EAs). To improve the performance of traditional EDAs, many remedies were suggested, which mainly focused on estimating a suitable probability distribution model with superior solutions. Different from existing research ideas, this paper tries to enhance EDA by exploiting the potential value of inferior solutions, where Gaussian EDA is taken as an example. It will be shown that, after a simple repair operation, inferior solutions could be surprisingly useful in adjusting the covariance matrix of Gaussian model, then a better search direction and a more proper search scale can be obtained. Since the aim of Inferior Solution Repairing (ISR) operator is not to directly improve the quality of inferior solutions, but to make them closer to superior ones, it can be implemented in a simple way. Combining ISR and traditional Gaussian EDA, a new EDA variant named ISR-EDA is developed. Comparison with existing EDAs and some other state-of-the-art EAs on benchmark functions demonstrates that ISR-EDA is efficient and competitive.}, keywords = {Gaussian processes, covariance matrices, evolutionary computation, mathematical operators, search problems, statistical distributions, Gaussian EDA, ISR operator, covariance matrix, estimation of distribution algorithms, inferior solution repairing operator, inferior solutions quality improvement, model-based EA, model-based evolutionary algorithms, probability distribution model, repair operation, search scale, Benchmark testing, Ellipsoids, Estimation, Probability distribution, Sociology, estimation of distribution algorithm}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969327}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969327}}, } @INPROCEEDINGS{przewoznicze:2017:CEC, author={M. W. Przewozniczek}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals}, year={2017}, editor = {Jose A. Lozano}, pages = {308--316}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the Evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show that the above assumption is frequently false. Therefore, the examples of evolutionary methods that use problem encoding which allows for significant optimization of the fitness computation process are pointed out and analyzed. The definition of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals (PEACh) is proposed. Another objective of the paper is to start a discussion concerning the computation load measurement in the evolutionary computation field. As shown, the Fitness Function Evaluation number is not always a fair measure and may be significantly affected by the quality of method implementation.}, keywords = {evolutionary computation, PEACh, computation load measurement, fitness function evaluation number, problem encoding-allowing-cheap fitness computation-of-mutated individuals, Bayes methods, Computational modeling, Encoding, Load modeling, Optimization, Time complexity, Dynamic Subpopulation Number Control, Evolutionary Algorithms, Fitness Computation Optimization, Irregular Fitness Computation Cost, Linkage Learning, Messy Coding, Problem Encoding}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969328}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969328}}, } @INPROCEEDINGS{datta:2017:CEC, author={R. Datta and K. Deb and A. Segev}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach}, year={2017}, editor = {Jose A. Lozano}, pages = {317--324}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Single objective evolutionary constrained optimization has been widely researched by plethora of researchers in the last two decades whereas multi-objective constraint handling using evolutionary algorithms has not been actively proposed. However, real-world multi-objective optimization problems consist of one or many non-linear and non-convex constraints. In the present work, we develop an evolutionary algorithm based on hybrid constraint handling methodology (HyCon) to deal with constraints in bi-objective optimization problems. HyCon is a combination of an Evolutionary Multi-objective Optimization (EMO) coupled with classical weighted sum approach and is an extended version of our previously developed constraint handling method for single objective optimization. A constrained bi-objective problem is converted into a tri-objective problem where the additional objective is formed using summation of constrained violation. The performance of HyCon is tested on four constrained bi-objective problems. The non-dominated solutions are compared with a standard evolutionary multi-objective optimization algorithm (NSGA-II) with respect to hypervolume and attainment surface. The simulation results illustrates the effectiveness of the HyCon method. The HyCon either outperformed or produced similar performance as compared to NSGA-II.}, keywords = {constraint handling, genetic algorithms, EMO, HyCon method, NSGA-II, biobjective hybrid constrained optimization method, classical weighted sum approach, evolutionary multiobjective optimization, multiobjective approach, penalty function approach, single objective evolutionary constrained optimization, standard evolutionary multiobjective optimization algorithm, Algorithm design and analysis, Evolutionary computation, Knowledge engineering, Linear programming, Minimization, Optimization, Search problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969329}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969329}}, } @INPROCEEDINGS{chen:2017:CEC, author={Zhonghui Chen and Yeting Lin and Xinxin Feng and Haifeng Zheng and Y. Xu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Incentive mechanism for participatory sensing: A contract-based approach}, year={2017}, editor = {Jose A. Lozano}, pages = {325--332}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Participatory sensing is a rising paradigm which utilizes mobile phones to collect data and build application on the cloud. But there are many problems to be resolved, poor quality of received information caused by task executors has been one of them. So incentive mechanism is essential for attracting users to participate in and submit high-quality data. Inspired by contract theory, we model participatory sensing as a contractual relationship and devote to design reasonable rewards for relevant results to maximize the benefit of task publisher. Under complete information scenario where task executors' efforts can be observed and incomplete information scenario where task executors' efforts can not be observed, we take advantage of maximization problem to infer the optional contract reward for task executors. In addition, based on the utility of task publisher, we propose optimal effort and optimal effort discriminant inequality (OEDI). Furthermore, we discuss the influence of noise, cost and boundary on optimal effort from aspects of theory and reality. Finally, we evaluate our contract-based approach by thorough simulations to show its effectiveness and accuracy.}, keywords = {information management, mobile computing, OEDI, cloud computing, complete information scenario, contract-based approach, incentive mechanism, incomplete information scenario, information quality, optimal effort discriminant inequality, participatory sensing, task publisher, Computational modeling, Contracts, Data acquisition, Ethics, Games, Mobile handsets, Sensors, Contract theory, Cooperative agent, Principal}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969330}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969330}}, } @INPROCEEDINGS{li:2017:CECa, author={Jianxia Li and R. Liu and Mingyang Zhang and Yangyang Li}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Ensemble-based multi-objective clustering algorithms for gene expression data sets}, year={2017}, editor = {Jose A. Lozano}, pages = {333--340}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, two multi-objective clustering ensemble algorithms are proposed named MOCLED and MOCNCD. MOCLED is different from MOCLE on three points. First, different clustering algorithms are used to produce some new individuals in evolutionary process. Second, a new screening mechanism is added. In each generation, the worst individual is replaced by the best individual. Third, a new objective function is added to ensure a diverse population. MOCNCD is the same as MOCLED except the crossover operator. We replace it with a new proposed cluster ensemble algorithm, IDICLENS. Experimental results reveal the advantages of our method on finding good partitions.}, keywords = {biology computing, genetic algorithms, genetics, mathematical operators, pattern clustering, IDICLENS, MOCLED, MOCNCD, cluster ensemble algorithm, crossover operator, diverse population, ensemble-based multiobjective clustering algorithms, evolutionary process, gene expression data sets, screening mechanism, Algorithm design and analysis, Clustering algorithms, Gene expression, Optimization, Partitioning algorithms, Sociology, Statistics, gene expression data, multi-objective clustering ensemble algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969331}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969331}}, } @INPROCEEDINGS{fu:2017:CEC, author={Xiaogang Fu and Jianyong Sun}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A new learning based dynamic multi-objective optimisation evolutionary algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {341--348}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Solving dynamic multi-objective optimisation problem means to search adaptively for the Pareto optimal solutions when the environment changes. It is important to find out the changing pattern for the efficiency of the evolutionary search. Learning techniques are thus widely used to explore the dependence structure of the changing for population re-initialisation in the evolutionary search paradigm. The learning techniques are expected to discover some useful knowledge from history information, while the learned knowledge can help improve the search speed through good initialisation when change occurs. In this paper, we propose a new learning strategy based on the incorporation of mutual information, stable matching strategy and Newton's laws of motion. Mutual information is used to identify the relationship between previously found solutions; the stable matching strategy is used to associate previous found solutions bijectively and Newton's Laws of motion is applied to re-initialise the new population. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.}, keywords = {Pareto optimisation, evolutionary computation, learning (artificial intelligence), search problems, Newton motion laws, Pareto optimal solutions, evolutionary search paradigm, history information knowledge, learned knowledge, learning based dynamic multiobjective optimisation evolutionary algorithm, learning techniques, matching strategy, mutual information, population reinitialisation, Acceleration, Computational modeling, Heuristic algorithms, Prediction algorithms, Predictive models, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969332}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969332}}, } @INPROCEEDINGS{harrison:2017:CEC, author={K. R. Harrison and B. M. Ombuki-Berman and A. P. Engelbrecht}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal parameter regions for particle swarm optimization algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {349--356}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Particle swarm optimization (PSO) is a stochastic search algorithm based on the social dynamics of a flock of birds. The performance of the PSO algorithm is known to be sensitive to the values assigned to its control parameters. While many studies have provided reasonable ranges in which to initialize the parameters based on their long-term behaviours, such previous studies fail to quantify the empirical performance of parameter configurations across a wide variety of benchmark problems. This paper specifically address this issue by examining the performance of a set of 1012 parameter configurations of the PSO algorithm over a set of 22 benchmark problems using both the global-best and local-best topologies. Results indicate that, in general, parameter configurations which are within close proximity to the boundaries of the best-known theoretically-defined convergent region lead to better performance than configurations which are further away. Moreover, results indicate that neighbourhood topology plays a far more significant role than modality and separability when determining the regions in parameter space which perform well.}, keywords = {particle swarm optimisation, search problems, topology, PSO, global-best topologies, local-best topologies, long-term behaviours, neighbourhood topology, optimal parameter regions, particle swarm optimization algorithms, social dynamics, stochastic search algorithm, theoretically-defined convergent region, Benchmark testing, Computer science, Convergence, Heuristic algorithms, Optimization, Particle swarm optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969333}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969333}}, } @INPROCEEDINGS{he:2017:CEC, author={Z. He and G. G. Yen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Comparison of visualization approaches in many-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {357--363}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In many-objective optimization, visualization of population in the high-dimensional objective space provides a critical understanding of the Pareto front. First, visualization throughout the evolutionary process can be exploited in developing effective many-objective evolutionary algorithms. Furthermore, visualization is a crucial component of multi-criteria decision making. By directly observing the performance of each solution, the trade-off between objectives, and distribution of the approximate front, the decision maker can easily decide which solution should be chosen from. In this paper, we make a detailed summary for existing visualization approaches and group them into five different categories. Then, three evaluation criteria for visualization approaches are designed, according to which, five state-of-the-arts are compared under the created data sets. Experimental results show that all approaches can satisfy each criterion to some degree but no one can fully achieve all of these criteria. There is a need to develop the new approach emphasis on fully satisfy all criteria simultaneously. Then, based on the comparison results, two future research directions for visualization approach are proposed.}, keywords = {Pareto optimisation, data visualisation, decision making, evolutionary computation, Pareto front, approximate front, created data sets, evolutionary process, high-dimensional objective space, many-objective evolutionary algorithms, many-objective optimization, multicriteria decision making, research directions, visualization approaches, Convergence, Data visualization, Optimization, Self-organizing feature maps, Shape, Visualization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969334}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969334}}, } @INPROCEEDINGS{karunakaran:2017:CEC, author={D. Karunakaran and Yi Mei and Gang Chen and Mengjie Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving dispatching rules for dynamic Job shop scheduling with uncertain processing times}, year={2017}, editor = {Jose A. Lozano}, pages = {364--371}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Dynamic Job shop scheduling (DJSS) is a complex and hard problem in real-world manufacturing systems. In practice, the parameters of a job shop like processing times, due dates, etc. are uncertain. But most of the current research on scheduling consider only deterministic scenarios. In a typical dynamic job shop, once the information about a job becomes available it is considered unchanged. In this work, we consider genetic programming based dispatching rules to generate schedules in an uncertain environment where the process time of an operation is not known exactly until it is finished. Our primary goal is to investigate methods to incorporate the uncertainty information into the dispatching rules. We develop two training approaches, namely ex-post and ex-ante to evolve the dispatching rules to generate good schedules under uncertainty. Both these methods consider different ways of incorporating the uncertainty parameters into the genetic programs during evolution. We test our methods under different scenarios and the results compare well against the existing approaches. We also test the generalization capability of our methods across different levels of uncertainty and observe that the proposed methods perform well. In particular, we observe that the proposed ex-ante training approach outperformed other methods.}, keywords = {genetic algorithms, genetic programming, dispatching, job shop scheduling, manufacturing systems, dynamic job shop scheduling, ex-ante training, ex-post training, generalization capability, genetic programming based dispatching rules, training approaches, uncertain environment, uncertainty information, uncertainty parameters, Dynamic scheduling, Optimization, Schedules, Training, Uncertainty}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969335}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969335}}, } @INPROCEEDINGS{awad:2017:CEC, author={N. H. Awad and M. Z. Ali and P. N. Suganthan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems}, year={2017}, editor = {Jose A. Lozano}, pages = {372--379}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Many Differential Evolution algorithms are introduced in the literature to solve optimization problems with diverse set of characteristics. In this paper, we propose an extension of the previously published paper LSHADE-EpSin that was ranked as the joint winner in the real-parameter single objective optimization competition, CEC 2016. The contribution of this work constitutes two major modifications that have been added to enhance the performance: ensemble of sinusoidal approaches based on performance adaptation and covariance matrix learning for the crossover operator. Two sinusoidal waves have been used to adapt the scaling factor: non-adaptive sinusoidal decreasing adjustment and an adaptive sinusoidal increasing adjustment. Instead of choosing one of the sinusoidal waves randomly, a performance adaptation scheme based on earlier success is used in this work. Moreover, covariance matrix learning with Euclidean neighborhood is used for the crossover operator to establish a suitable coordinate system, and to enhance the capability of LSHADE-EpSin to tackle problems with high correlation between the variables. The proposed algorithm, namely LSHADE-cnEpSin, is tested on the IEEE CEC2017 problems used in the Special Session and Competitions on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to other state-of-the-art algorithms.}, keywords = {covariance matrices, evolutionary computation, learning (artificial intelligence), optimisation, CEC2017 benchmark problems, Euclidean neighborhood, IEEE CEC2017 problems, LSHADE-cnEpSin, covariance matrix learning, crossover operator, differential evolution algorithms, ensemble sinusoidal differential covariance matrix adaptation, performance adaptation, real-parameter single objective optimization competition, single objective bound constrained real-parameter single objective optimization, sinusoidal waves, Benchmark testing, Indexes, Next generation networking, Optimization, Sociology, Differential Evolution, Ensemble approach, Sinusoidal Wave}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969336}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969336}}, } @INPROCEEDINGS{awad:2017:CECa, author={N. H. Awad and M. Z. Ali and P. N. Suganthan and R. G. Reynolds and A. M. Shatnawi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A novel differential crossover strategy based on covariance matrix learning with Euclidean neighborhood for solving real-world problems}, year={2017}, editor = {Jose A. Lozano}, pages = {380--386}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the search space and highly nonlinear constraints with large number of variables. The advances on this type of problems are of capital importance for many researchers to develop new efficient evolutionary algorithms to tackle such problems in an efficient manner with better solutions. For this reason, this work proposes a new crossover technique based on covariance learning with Euclidean neighborhood which has been incorporated in the basic L-SHADE algorithm. The goal of this new technique is to help L-SHADE establish a suitable coordinate system for the crossover operator. This helps enhance L-SHADE capability to solve real world problems with difficult characteristics and nonlinear constraints. The proposed algorithm, namely L-covnSHADE, is tested on one of the challenging benchmarks which is the IEEE CEC'11 on real-world numerical optimization problems. This set consists of 22 real-world problems with diverse stimulating characteristics and a dimensionality ranging from 1 to 240 dimensions. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the L-SHADE algorithm and other state-of-the-art algorithms including the winner of the CEC2011 competition.}, keywords = {covariance matrices, evolutionary computation, learning (artificial intelligence), mathematics computing, CEC2011 competition, Euclidean neighborhood, L-SHADE algorithm, L-covnSHADE, covariance matrix learning, crossover operator, efficient evolutionary algorithms, local optima, nonlinear constraints, novel differential crossover strategy, real-world optimization problems, search space, Benchmark testing, Indexes, Optimization, Sociology, Real World Numerical Optimization problems, covariance learning, evolutionary algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969337}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969337}}, } @INPROCEEDINGS{sharma:2017:CEC, author={D. Sharma and I. Tanev and K. Shimohara}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary optimization of weight coefficients of power spectrum for detection of driver-induced steering oscillations}, year={2017}, editor = {Jose A. Lozano}, pages = {387--394}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The proposed method applies genetic algorithms (GA) to optimize the weight coefficients of the power spectrum of the Fourier-transformed signal of lateral acceleration of a moving car. The evolved weighted power spectrum detects the steering oscillations caused by the delayed steering response of a human driver in normal, routine driving situations - traffic-less driving on straight and curved roads. Delayed steering response is often a result of drivers having an inadequate cognitive load due to either distraction or cognitive overload. The experimental results, conducted on a realistically simulated car and its environment, indicate that, compared to the power spectrum featuring equal (flat) weight coefficients, the evolved weighted power spectrum facilitates improved discrimination between (i) signals of the lateral acceleration of the car driven by cognitively impaired (i.e., distracted by texting on a mobile phone while driving) drivers and (ii) analog signals of the car, driven by fully attentive drivers. Moreover, for all human drivers who participated in the experiments, the weighted power spectrum of the lateral acceleration of the car driven with distraction, even on a straight section of the road (i.e., inherently featuring lower values of the power spectrum) is even higher than that of the car driven by attentive drivers along curved roads (inherently, featuring higher values of the power spectrum). These results suggest that the proposed method would be applicable for discriminating between subtle driver-induced steering oscillations on straight roads and well-manifested, yet normal steering behavior of drivers when cornering. We view the obtained results as a step towards the development of an early warning system of the inadequate cognitive load of drivers under routine driving conditions - well before any urgent reaction to an eventually dangerous traffic situation might be needed.}, keywords = {genetic algorithms, road traffic, signal processing, traffic engineering computing, Fourier-transformed signal, GA, curved road, delayed steering response, driver cognitive load, driver-induced steering oscillation detection, early warning system, evolutionary optimization, genetic algorithm, lateral acceleration, moving car, power spectrum, routine driving situation, straight road, traffic-less driving, weight coefficients, Acceleration, Automobiles, Oscillators, Roads, Wheels, Fourier transformation, TORCS, driver distraction, driver-induced steering oscillations}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969338}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969338}}, } @INPROCEEDINGS{gao:2017:CEC, author={K. Gao and Yicheng Zhang and A. Sadollah and Rong Su}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improved artificial bee colony algorithm for solving urban traffic light scheduling problem}, year={2017}, editor = {Jose A. Lozano}, pages = {395--402}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, a novel centralized traffic network model is proposed to describe the urban traffic light scheduling problem (UTLSP) in a traffic network. The objective is to minimize the network-wise total delay time of all vehicles in a fixed time window. To overcome the potentially high computational complexity involved in UTLSP, an improved artificial bee colony (IABC) algorithm is proposed. A new solution generating strategy and three local search operators corresponding to different neighbourhood structures of UTLSP are proposed to improve the performance of IABC. Extensive computational experiments are carried out using sixteen instances with different problem-scales. The IABC with and without three local search operators are evaluated and compared. The comparisons and discussions show the competitiveness of IABC for solving UTLSP.}, keywords = {search problems, traffic, IABC algorithm, UTLSP, improved artificial bee colony algorithm, local search operators, network-wise total delay time, novel centralized traffic network model, urban traffic light scheduling problem, Algorithm design and analysis, Delays, Job shop scheduling, Optimization, Sociology, Statistics, Local search, Traffic light scheduling, Traffic network, artificial bee colony}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969339}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969339}}, } @INPROCEEDINGS{parrend:2017:CEC, author={P. Parrend and P. David and F. Guigou and C. Pupka and P. Collet}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The AWA Artificial emergent aWareness Architecture model for Artificial Immune Ecosystems}, year={2017}, editor = {Jose A. Lozano}, pages = {403--410}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Artificial Immune Systems (AIS) bear two complementary radical insights for building immune properties into technical systems: AIS algorithms, which have proved their efficiency for anomaly detection, and what we call Artificial Immune Ecosystems, i.e. distributed architectures capable of decentralized sensing, analysis and reactions to these anomalies. In this paper, we propose the AWA model for Artificial aWareness Architecture, which aims at standardizing the infrastructure of Artificial Immune Ecosystems. The AWA model specifies the physical and logical architecture of Artificial Immune Ecosystems, as well as a set of data formats for anomaly information transfer between the different levels of the ecosystem. Very promising experimentation is conducted within the CASAN (Common Architecture for Sensor and Actuator Networks) IoT (Internet of Things) Platform through a focused implementation on local detection and distributed analysis features of the AWA model.}, keywords = {Internet of Things, artificial immune systems, distributed processing, knowledge based systems, learning (artificial intelligence), security of data, AIS algorithms, AWA model, CASAN, IoT platform, anomaly analysis, anomaly detection, anomaly information transfer, anomaly reactions, artificial emergent awareness architecture model, artificial immune ecosystems, common architecture for sensor and actuator networks, data formats, decentralized sensing, distributed analysis features, distributed architectures, technical system immune properties, Buildings, Computer architecture, Detectors, Ecosystems, Immune system, Pathogens, Protocols}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969340}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969340}}, } @INPROCEEDINGS{pizzuti:2017:CEC, author={C. Pizzuti and A. Socievole}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Many-objective optimization for community detection in multi-layer networks}, year={2017}, editor = {Jose A. Lozano}, pages = {411--418}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A many-objective optimization algorithm for community detection in multi-layer networks is proposed. The method exploits the modularity concept as function to be simultaneously optimized on all the network layers to uncover multi-layer communities. In addition, three different strategies to choice the best solution from the set of solutions of the Pareto front are presented. Simulations on several synthetic networks reveal that our method is able to extract high quality communities. A comparison with state-of-the-art approaches shows that the method is competitive and, in many cases, it is also able to outperform existing community detection algorithms for multi-layer networks.}, keywords = {Pareto optimisation, network theory (graphs), Pareto front, community detection, many-objective optimization, modularity concept, multilayer communities, multilayer networks, synthetic networks, Detection algorithms, Image edge detection, Linear programming, Multiplexing, Pareto optimization, Social network services, multi-layer networks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969341}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969341}}, } @INPROCEEDINGS{ribaric:2017:CEC, author={T. Ribaric and S. Houghten}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic programming for improved cryptanalysis of elliptic curve cryptosystems}, year={2017}, editor = {Jose A. Lozano}, pages = {419--426}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Public-key cryptography is a fundamental component of modern electronic communication that can be constructed with many different mathematical processes. Presently, cryptosystems based on elliptic curves are becoming popular due to strong cryptographic strength per small key size. At the heart of these schemes is the intractability of the elliptic curve discrete logarithm problem (ECDLP). Pollard's Rho algorithm is a well known method for solving the ECDLP and thereby breaking ciphers based on elliptic curves. It has the same time complexity as other known methods but is advantageous due to smaller memory requirements. This paper considers how to speed up the Rho process by modifying a key component: the iterating function, which is the part of the algorithm responsible for determining what point is considered next when looking for a collision. It is replaced with an alternative that is found through an evolutionary process. This alternative consistently and significantly decreases the number of iterations required by Pollard's Rho Algorithm to successfully find a solution to the ECDLP.}, keywords = {genetic algorithms, genetic programming, public key cryptography, ECDLP, Pollard Rho algorithm, cryptanalysis, cryptographic strength, elliptic curve cryptosystem, elliptic curve discrete logarithm problem, evolutionary process, mathematical process, time complexity, Ciphers, Elliptic curve cryptography, Elliptic curves, Partitioning algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969342}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969342}}, } @INPROCEEDINGS{sun:2017:CEC, author={Y. Sun and G. G. Yen and Z. Yi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Global view-based selection mechanism for many-objective evolutionary algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {427--434}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In traditional many-objective evolutionary algorithms (MaOEAs), solutions survived to the next generation are individually selected which leads to the favorable quality upon the population composed of these selected solutions not necessarily to be gained. However, MaOEAs which are widely used in solving many-objective optimization problems (MaOPs) are considerably preferred due to their population-based nature. In this paper, a global view-based selection mechanism has been proposed to concern the quality of the selected solutions from a global prospective, which is capable of simultaneously facilitating the performance of the entire selected solutions. Indeed, the proposed selection mechanism is equivalent to solve a linear assignment problem whose cost matrix is constructed by the entries concurrently measuring the convergence and diversity of each solution. In addition, this design principle is also utilized for the mating selection to guarantee the convergence and diversity of the selected parents to generate promising offspring. As a case study, the proposed global view-based selection mechanism is integrated into NSGA-III (i.e., GS-NSGA-III). To validate the proposed selection mechanism, extensive experiments are performed by GS-NSGA-III against four state-of-the-art MaOEAs over 8-, 10-, and 15-objective DTLZ1-DTLZ7 test problems. The results measured by the selected performance metric reveal that GS-NSGA-III shows considerable competitiveness in addressing MaOPs.}, keywords = {evolutionary computation, matrix algebra, DTLZ1-DTLZ7 test problems, GS-NSGA-III, MaOEAs, MaOPs, convergence, cost matrix, global view-based selection mechanism, linear assignment problem, many-objective evolutionary algorithms, many-objective optimization problems, mating selection, population-based nature, solution diversity, Algorithm design and analysis, Next generation networking, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969343}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969343}}, } @INPROCEEDINGS{li:2017:CECb, author={Juan Li and J. Chen and B. Xin and Lu Chen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Efficient multi-objective evolutionary algorithms for solving the multi-stage weapon target assignment problem: A comparison study}, year={2017}, editor = {Jose A. Lozano}, pages = {435--442}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The weapon target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. The multi-stage weapon target assignment (MWTA) problem is the basis of the dynamic weapon target assignment (DWTA) problem which commonly exists in practice. The MWTA problem considered in this paper is formulated as a multi-objective constrained combinatorial optimization problem with two competing objectives. Apart from maximizing the damage to hostile targets, this paper follows the principle of minimizing the ammunition consumption. Decomposition and Pareto dominance both are efficient and prevailing strategies for solving multi-objective optimization problems. Three competitive multi-objective optimizers: DMOEA-εC, NSGA-II, and MOEA/D-AWA are adopted to solve multi-objective MWTA problems efficiently. Then comparison studies among DMOEA-εC, NSGA-II, and MOEA/D-AWA on solving three different-scale MWTA instances are done. Three common used performance metrics are used to evaluate the performance of each algorithm. Numerical results demonstrate that NSGA-II performs best on small-scale and medium-scale instances compared with DMOEA-εC and MOEA/D-AWA, while DMOEA-εC shows advantages over the other two algorithms on solving the large-scale instance.}, keywords = {Pareto optimisation, combinatorial mathematics, evolutionary computation, operations research, weapons, DMOEA-εC, DWTA problem, MOEA/D-AWA, NSGA-II, Pareto dominance, ammunition consumption minimization, damage maximization, decomposition, defense-related applications, dynamic weapon target assignment, hostile targets, multiobjective MWTA problems, multiobjective constrained combinatorial optimization problem, multiobjective evolutionary algorithms, multiobjective optimization problems, multistage weapon target assignment problem, Decision making, Discrete wavelet transforms, Optimization, Upper bound, ε-constraint, combinatorial optimization, multi-objective constrained optimization problem, multi-objective optimization, multi-stage weapon target assignment (MWTA)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969344}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969344}}, } @INPROCEEDINGS{liu:2017:CEC, author={Wenfeng Liu and Shanfeng Wang and M. Gong and Mingyang Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An improved multiobjective evolutionary approach for community detection in multilayer networks}, year={2017}, editor = {Jose A. Lozano}, pages = {443--449}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The detection of shared community structure in multilayer network is an interesting and important issue that has attracted many researches. Traditional methods for community detection of single layer networks are not suitable for that of multilayer networks. In a previous work, the authors modeled the community discovery problem in multilayer network as a multiobjective one and devised a genetic algorithm to carry out it. In this paper, based on their model, we propose an improved multiobjective evolutionary approach MOEA-MultiNet for community detection in multilayer networks. The proposed MOEA-MultiNet is based on the framework of NSGA-II which employs the string-based representation scheme and synthesizes the genetic operation and local search to perform individual refinement. Experimental results on two real-world networks both demonstrate the ability and efficiency of the proposed MOEA-MultiNet in detecting community structure in multilayer networks.}, keywords = {genetic algorithms, network theory (graphs), search problems, MOEA-MultiNet, NSGA-II, community detection, community discovery problem, genetic algorithm, genetic operation, local search, multilayer networks, multiobjective evolutionary approach, shared community structure detection, string-based representation scheme, Biological cells, Evolutionary computation, Nonhomogeneous media, Optimization, Social network services, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969345}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969345}}, } @INPROCEEDINGS{chen:2017:CECa, author={Feng Chen and Jiao Shi and Yunhong Ma and Yu Lei and M. Gong}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Differential evolution algorithm with learning selection strategy for SAR image change detection}, year={2017}, editor = {Jose A. Lozano}, pages = {450--457}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Image change detection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes. To tackle the problem, a novel differential evolution algorithm with an automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly pre-classifies all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the non-selected population with a low probability into selected ones. Finally, the samples including partial individuals from the selected and non-selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategies in every generation, which will significantly accelerate the convergence speed. The proposed change detection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.}, keywords = {convergence, evolutionary computation, feature extraction, fuzzy set theory, optimisation, pattern clustering, radar imaging, synthetic aperture radar, unsupervised learning, SAR image change detection, convergence speed, differential evolution algorithm, fuzzy clustering, learning selection strategy, neural network training, optimization processes, selection strategies, synthetic aperture radar images, unsupervised change detection, Algorithm design and analysis, Change detection algorithms, Clustering algorithms, Neural networks, Next generation networking, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969346}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969346}}, } @INPROCEEDINGS{li:2017:CECc, author={H. Li and Jingjing Ma and Jia Liu and M. Gong and Mingyang Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective endmember extraction for hyperspectral images}, year={2017}, editor = {Jose A. Lozano}, pages = {458--465}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Endmember extraction is a critical step of spectral unmixing. In this paper, a novel endmember extraction algorithm based on evolutionary multi-objective optimization is proposed for hyperspectral remote sensing images. In the proposed method, endmember extraction is modeled as a multi-objective optimization problem. Then the root mean square error between the original image and its remixed image and the number of endmembers are chosen as two conflicting objective functions, which are simultaneously optimized by particle swarm optimization algorithm to find the best tradeoff solutions. In order to promote diversity and speed up the convergence of the algorithm, a new particle status updating strategy and a novel method for selecting leaders are designed. The experimental results on both simulated and real hyperspectral remote sensing images confirm the performance of the proposed approach over some existing methods.}, keywords = {evolutionary computation, geophysical image processing, hyperspectral imaging, mean square error methods, particle swarm optimisation, remote sensing, spectral analysis, evolutionary multiobjective optimization, hyperspectral remote sensing images, multiobjective endmember extraction, particle status updating strategy, particle swarm optimization, root mean square error, spectral unmixing, Algorithm design and analysis, Indexes, Pareto optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969347}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969347}}, } @INPROCEEDINGS{li:2017:CECd, author={Na Li and Yu Lei and Jiao Shi and M. Gong}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Fuzzy multi-objective sparse feature learning}, year={2017}, editor = {Jose A. Lozano}, pages = {466--473}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Neural networks are currently popular learning models to represent and analyze data. We address two issues about that in this paper. On the one hand, the parameters between neurons are often restricted to be constants, which greatly limits the learning ability and reduces the robustness of the neural network. For that, it is necessary to make the parameters fuzzy. In this paper, we introduce the fuzzy set theory to neural networks where the parameters are expressed by fuzzy numbers. Meanwhile, the loss term and sparsity of the network become fuzzy. On the other hand, a user-defined weighting parameter need to be determined to keep the trade-off between the fuzzy loss term and fuzzy sparsity. In order to solve the two issues simultaneously, the main contribution of this paper is to combine fuzzy set theory with multi-objective sparsity to apply to neural networks, for the first time, and propose a fuzzy multi-objective sparse feature learning (FMSFL) model, where a multi-objective optimization model is established, and reconstruction error and sparsity of fuzzy model are considered as two objectives. In the experiments, we demonstrate the effectiveness of our model, and both learning capability and robustness of the neural networks based on the proposed model are improved.}, keywords = {fuzzy set theory, learning (artificial intelligence), neural nets, optimisation, FMSFL model, fuzzy loss term, fuzzy multiobjective sparse feature learning, fuzzy numbers, fuzzy sparsity, multiobjective optimization model, neural networks, reconstruction error, user-defined weighting parameter, Biological neural networks, Feature extraction, Neurons, Pareto optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969348}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969348}}, } @INPROCEEDINGS{tang:2017:CEC, author={Zedong Tang and Maoguo Gong and Mingyang Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary multi-task learning for modular extremal learning machine}, year={2017}, editor = {Jose A. Lozano}, pages = {474--479}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary multi-tasking is a novel concept where algorithms utilize the implicit parallelism of population-based search to solve several tasks efficiently. In last decades, multi-task learning, which harnesses the underlying similarity of the learning tasks, has proved efficient in many applications. Extreme learning machine is a distinctive learning algorithm for feed-forward neural networks. Because of its similarity and low computational complexity comparing with the convenient neural network training algorithms, it has been used in many cases of data analyses. In this paper, a modular training technique by employing evolutionary multi-task paradigm is used to evolve the modular topologies of extreme learning machine. Though, extreme learning machine is much faster than the convenient gradient-based method, it needs more hidden neurons due to the random determination of input weights. In proposed method, we combine the evolutionary extreme learning machine and multi-task modular training. Each task is defined by an evolutionary extreme learning machine with different number of hidden neurons. This method produces a modular extreme learning machine which needs less number of hidden units and could be effective even if some hidden neurons and connections are removed. Experiment results show effectiveness and generalization of the proposed method for benchmark classification problems.}, keywords = {feedforward neural nets, learning (artificial intelligence), distinctive learning algorithm, evolutionary multitask learning, feedforward neural networks, learning task similarity, modular extremal learning machine, modular training technique, population-based search, Biological neural networks, Multitasking, Network topology, Neurons, Sociology, Topology, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969349}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969349}}, } @INPROCEEDINGS{ser:2017:CEC, author={J. Del Ser and A. I. Torre-Bastida and I. Laña and M. N. Bilbao and C. Perfecto}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria}, year={2017}, editor = {Jose A. Lozano}, pages = {480--487}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in an efficient fashion, especially over the so-called last mile of the distribution chain. In this context the selective pickup and delivery problem focuses on determining the optimal subset of packets that should be picked from its origin city and delivered to their corresponding destination within a given time frame, often driven by the maximization of the total profit of the courier service company. This manuscript tackles a realistic variant of this problem where the transportation fleet is composed by more than one vehicle, which further complicates the selection of packets due to the subsequent need for coordinating the delivery service from the command center. In particular the addressed problem includes a second optimization metric aimed at reflecting a fair share of the net benefit among the company staff based on their driven distance. To efficiently solve this optimization problem, several nature-inspired metaheuristic solvers are analyzed and statistically compared to each other under different parameters of the problem setup. Finally, results obtained over a realistic scenario over the province of Bizkaia (Spain) using emulated data will be explored so as to shed light on the practical applicability of the analyzed heuristics.}, keywords = {Internet, goods distribution, production engineering computing, retail data processing, scheduling, vehicle routing, Bizkaia, command center, courier service company, delivery service, driven distance, incentive fairness criteria, maximum profit, multiple-vehicle selective pickup and delivery problem, nature-inspired heuristics, net benefit, on-line shopping stores, optimization metric, selling channel, transportation fleet, transportation resources scheduling, wide-scale freight transportation logistics, Companies, Logistics, Mathematical model, Optimization, Transportation, Urban areas}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969350}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969350}}, } @INPROCEEDINGS{wang:2017:CECb, author={Qixiang Wang and M. Gong and Chao Song and Shanfeng Wang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Discrete particle swarm optimization based influence maximization in complex networks}, year={2017}, editor = {Jose A. Lozano}, pages = {488--494}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The aim of influence maximization problem is to mine a small set of influential individuals in a complex network which could reach the maximum influence spread. In this paper, an efficient fitness function based on local influence is designed to estimate the influence spread. Then, we propose a discrete particle swarm optimization based algorithm to find the final set with the maximum value of the fitness function. In the proposed algorithm, discrete position and velocity are redefined and problem-specific update rules are designed. In order to accelerate the convergence, we introduce a degree-based population initialization method and a mutation learning based local search strategy. Experimental results compared with four comparison algorithms show that our proposed algorithm is able to efficiently find good-quality solutions.}, keywords = {complex networks, convergence, learning (artificial intelligence), particle swarm optimisation, search problems, social sciences, degree-based population initialization, discrete particle swarm optimization, fitness function, influence maximization, influential individuals, local search strategy, maximum influence spread, mutation learning, problem-specific update rules, Algorithm design and analysis, Greedy algorithms, Integrated circuit modeling, Social network services, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969351}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969351}}, } @INPROCEEDINGS{zhang:2017:CEC, author={M. Zhang and Jingjing Ma and M. Gong and Hao Li and Jia Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Memetic algorithm based feature selection for hyperspectral images classification}, year={2017}, editor = {Jose A. Lozano}, pages = {495--502}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Band selection is a crucial preprocessing step for hyperspectral image classification, which is a classic feature selection method. Feature selection is designed to select feature subsets to represent the whole feature space. For feature selection, two crucial issues need to be handled: preserving information and redundancy reducing. In this paper, a novel feature selection method for hyperspectral image classification is proposed, which is based on a newly designed memetic algorithm. In the proposed method, a suitable objective function is designed, which can measure the contained crucial information and redundancy information in the selected feature subsets. To optimize this objective function efficiently, a novel memetic algorithm is designed. The genetic operator and local search strategy are newly designed according to the characteristic of hyperspectral images. Experiments are implemented on three real data sets compared with some state of arts. The experimental results show that the proposed method can obtain stable and superior feature subsets for classification.}, keywords = {feature selection, genetic algorithms, hyperspectral imaging, image classification, search problems, band selection, genetic operator, hyperspectral image classification, local search strategy, memetic algorithm based feature selection, objective function optimization, Algorithm design and analysis, Biological cells, Linear programming, Memetics, Redundancy, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969352}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969352}}, } @INPROCEEDINGS{yu:2017:CEC, author={Haibo Yu and Ying Tan and Chaoli Sun and Jianchao Zeng}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Clustering-based evolution control for surrogate-assisted particle swarm optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {503--508}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={When using a fixed number of neighbors for training a local surrogate model in surrogate assisted evolutionary optimization algorithms, it may suffer from the large uncertainty because the actual distribution of candidate's neighborhood may be neglected. In this paper, we propose to firstly analyze the distribution characteristics of candidate's neighborhood through a modified overlapping clustering method before training a local surrogate, and then use the clustering based evolution control strategy or model management strategy to facilitate the evolutionary algorithm to converge to the right optimum. Simulation results on four widely used benchmark functions demonstrate the efficacy of the proposed method.}, keywords = {evolutionary computation, learning (artificial intelligence), particle swarm optimisation, pattern clustering, candidate neighborhood, clustering-based evolution control, distribution characteristics, local surrogate model training, model management strategy, modified overlapping clustering method, surrogate assisted evolutionary optimization algorithms, surrogate-assisted particle swarm optimization, Computational modeling, Databases, Linear programming, Optimization methods, Particle swarm optimization, Training, evolution control, model management, overlapping clustering, surrogate model}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969353}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969353}}, } @INPROCEEDINGS{wang:2017:CECc, author={G. G. Wang and G. S. Hao and Shi Cheng and Y. Shi and Z. Cui}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An improved Brain Storm Optimization algorithm based on graph theory}, year={2017}, editor = {Jose A. Lozano}, pages = {509--515}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Recently, inspired by the human brainstorming process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing graph theory into it. In IBSO, new individuals will be generated to replace some old individuals when the BSO algorithm is in a poor status. Whether a BSO algorithm is in a poor status is determined by the length of Hamiltonian cycle, which can be obtained by transferring all the individuals into an undirected weight graph. A Hamiltonian cycle and its length will be computed according to a modified cycle algorithm. The proposed IBSO algorithm is tested on twelve benchmarks, and the experimental results illustrate its effectiveness.}, keywords = {cognition, graph theory, optimisation, Hamiltonian cycle, IBSO, brain storm optimization algorithm, global optimization, human brainstorming process, improved BSO algorithm, metaheuristic algorithm, modified cycle algorithm, undirected weight graph, Algorithm design and analysis, Clustering algorithms, Optimization, Sociology, Statistics, Storms, Brain storm optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969354}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969354}}, } @INPROCEEDINGS{swa:2017:CEC, author={J. Swan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Harmonic analysis and resynthesis of Sliding-Tile Puzzle heuristics}, year={2017}, editor = {Jose A. Lozano}, pages = {516--524}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Combinatorial optimization problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Sliding-Tile Puzzle have structure that can be described algebraically and exploited to improve search quality. In particular, heuristic functions for these problems can be described in terms of symmetric groups. By employing techniques from the emerging field of algebraic machine learning, we investigate the harmonic decomposition of popular sliding-tile puzzle heuristics via the extension of the Fourier Transform to noncommutative groups. The Fourier-space representation for these heuristics can then be manipulated by multiplication with a real-valued weight vector. By performing such a reweighting and then taking the inverse transform, we obtain a `filtered' version of the original heuristic. Taking the 8-puzzle as a case study, we demonstrate that heuristic accuracy is related to harmonic spectral density and further that the heuristic functions given by the Hamming and Manhattan distance metrics on puzzle state can be transformed into significantly better heuristics by performing a search in the weight space.}, keywords = {Fourier transforms, algebra, combinatorial mathematics, inverse transforms, learning (artificial intelligence), optimisation, search problems, travelling salesman problems, Fourier transform, Fourier-space representation, Hamming distance metrics, Manhattan distance metrics, algebraic machine learning, combinatorial optimization problems, harmonic analysis, harmonic decomposition, harmonic spectral density, inverse transform, noncommutative groups, puzzle state, quadratic assignment problem, real-valued weight vector, search quality, sliding-tile puzzle heuristics resynthesis, travelling salesman problem, Approximation algorithms, Databases, Law, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969355}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969355}}, } @INPROCEEDINGS{ferreira:2017:CEC, author={A. S. Ferreira and R. A. Gonçalves and A. Pozo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A Multi-Armed Bandit selection strategy for Hyper-heuristics}, year={2017}, editor = {Jose A. Lozano}, pages = {525--532}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Meta-heuristics have emerged as an efficient way to solve NP-hard problems even without the guaranteed of optimal values. The main issue of meta-heuristics is that they are built using domain-specific knowledge. Therefore, they require a great effort to be adapted to a new domain. The concept of Hyper-heuristic was proposed to solve this problem. Hyper-heuristics are search methods that aim to solve optimization problems by selecting or generating heuristics. Selection hyper-heuristics choose from a pool of heuristics a good one to be applied at the current stage of the optimization process. Although there are several works focused on selection hyper-heuristics, there is no consensus about which is the best way to define a selection strategy. In this work, a deterministic selection strategy based on the concepts of the Multi-Armed Bandit (MAB) problem is proposed for combinatorial optimization. Multi-armed bandit approaches define a selection function with two components; the first is based on the performance of an operator and the second based on the number of times that the operator was used. In this work, three MAB algorithms were implemented using the HyFlex framework. An empirical parameter configuration was performed to each algorithm, and the best setup was compared to the top ten CHeSC 2011 algorithms using the same methodology adopted during the competition. The results obtained were comparable to those attained by the literature. Moreover, it was concluded that the behavior of MAB selection is heavily affected by its parameters. As this is not a desirable behavior for hyper-heuristics, future research will investigate ways to better deal with the parameter setting.}, keywords = {computational complexity, optimisation, probability, CHeSC 2011 algorithms, HyFlex framework, MAB selection, NP-hard problems, deterministic selection strategy, domain-specific knowledge, hyper-heuristics, meta-heuristics, multiarmed bandit selection strategy, optimization problems, optimization process, Computer science, Context, Electronic mail, Heuristic algorithms, Mathematical model, Optimization, Space exploration}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969356}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969356}}, } @INPROCEEDINGS{bermejo:2017:CEC, author={E. Bermejo and M. Chica and S. S. Sanz and O. Cordón}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Coral Reef Optimization for intensity-based medical image registration}, year={2017}, editor = {Jose A. Lozano}, pages = {533--540}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations of traditional IR methods have boomed the application of evolutionary and metaheuristic-based approaches to solve the problem, aiming to improve the performance of existing methods both in terms of accuracy and efficiency. In this contribution, we consider the use of a recently proposed bio-inspired meta-heuristic: the Coral Reef Optimization Algorithm (CRO). This novel algorithm simulates the natural phenomena underlying a coral reef, where different corals grow, reproduce and fight with other corals for space in the colony. CRO has recently obtained promising results in different real-world applications and we think its operation mode can properly cope with the 3D intensity-based medical IR problem. We adapt the algorithm to the real-coding problem nature and run an experimental setup tackling sixteen real-world problem instances. The new proposal is benchmarked with recent, state-of-the-art IR techniques. The results show that the CRO-based overcomes the state-of-the-art results in terms of its robustness and time efficiency.}, keywords = {computer vision, image registration, medical image processing, optimisation, 3D intensity-based medical IR, CRO, bio-inspired metaheuristic, coral reef optimization algorithm, image intensity distribution, intensity-based medical image registration, Biomedical imaging, Feature extraction, Measurement, Optimization, Robustness, Space exploration}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969357}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969357}}, } @INPROCEEDINGS{contreras-cruz:2017:CEC, author={M. A. Contreras-Cruz and J. J. Lopez-Perez and V. Ayala-Ramirez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Distributed path planning for multi-robot teams based on Artificial Bee Colony}, year={2017}, editor = {Jose A. Lozano}, pages = {541--548}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we propose a distributed planner method for multi-robot systems based on Swarm Intelligence. The method uses a distributed version of a priority based planner to compute coordinated motions of multiple robots in parallel. The Artificial Bee Colony algorithm is used to find velocity profiles that avoid collisions between robots and that minimize the time of the path execution. The proposed planner is tested in some transportation problems with scenarios as warehouses, offices, etc. We compare the proposed method to a classic priority method that uses the proposed coordination scheme to observe the advantages of our distributed method.}, keywords = {collision avoidance, distributed algorithms, mobile robots, multi-robot systems, swarm intelligence, artificial bee colony algorithm, coordinated multiple robot motions, coordination scheme, distributed priority based path planner, multirobot teams, path execution time minimization, priority method, transportation problems, velocity profiles, Oscillators, Path planning, Planning, Robot kinematics, decentralized planner, multi-robot system, path coordination, prioritized planning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969358}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969358}}, } @INPROCEEDINGS{mashrouteh:2017:CEC, author={S. Mashrouteh and S. Rahnamayan and E. Esmailzadeh}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal vibration control and innovization for rectangular plate}, year={2017}, editor = {Jose A. Lozano}, pages = {549--556}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Vibration control of flexible structures has always been one of the most important issues and Among variant available control methods, active vibration control using piezoelectric sensors and actuators has become popular due to its high efficiency and flexibility for designing a control system. The main concern in designing a control system with piezoelectric patches is finding best position for patches. On the other hand, number of used sensors and actuators is another important issue which affects the costs of the project as well as the performance. The main goal of the present study is to control oscillation of a rectangular plate using minimum number of piezoelectric sensors and actuators (i.e., objective one) and finding their optimum placement to get the maximum possible performance (i.e., objective two); the mentioned two objectives are in conflict. The plate have been mathematically modeled using the Kirchhoff-Love theory. By considering the piezoelectric sensor-actuators effects, the control equation of the cantilever plate has been obtained. In order to find the optimum number and placement of the sensors and actuators, the multi-objective genetic algorithm (GA) has been used and the objective functions have been defined based on maximization of observability and countability indexes of the cantilever plate. After conducting the optimization process, a few thumb rules have been extracted using the innovization technique. The results have been verified by implementing the designed controller using the optimum solution found by optimization method. The importance of the rules found by innovization technique have been illustrated in the numerical discussion.}, keywords = {cantilevers, flexible structures, genetic algorithms, innovation management, optimal control, piezoelectric actuators, plates (structures), vibration control, Kirchhoff-Love theory, cantilever plate, innovization, multiobjective genetic algorithm, optimal vibration control, optimization, piezoelectric patches, piezoelectric sensors, rectangular plate, Actuators, Linear programming, Mathematical model, Sensors, Evolutionary Computation, Fuzzy Logic Controller, Genetic Algorithm, Kirchhoff-Love Plate, Multi-objective Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969359}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969359}}, } @INPROCEEDINGS{shi:2017:CEC, author={Jialong Shi and Qingfu Zhang and B. Derbel and A. Liefooghe}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A Parallel Tabu Search for the Unconstrained Binary Quadratic Programming problem}, year={2017}, editor = {Jose A. Lozano}, pages = {557--564}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Although several sequential heuristics have been proposed for dealing with the Unconstrained Binary Quadratic Programming (UBQP), very little effort has been made for designing parallel algorithms for the UBQP. This paper propose a novel decentralized parallel search algorithm, called Parallel Elite Biased Tabu Search (PEBTS). It is based on D^2 TS, a state-of-the-art sequential UBQP metaheuristic. The key strategies in the PEBTS algorithm include: (i) a lazy distributed cooperation procedure to maintain diversity among different search processes and (ii) finely tuned bit-flip operators which can help the search escape local optima efficiently. Our experiments on the Tianhe-2 supercomputer with up to 24 computing cores show the accuracy of the efficiency of PEBTS compared with a straightforward parallel algorithm running multiple independent and non-cooperating D^2 TS processes.}, keywords = {computational complexity, parallel machines, quadratic programming, search problems, NP hard problem, PEBTS algorithm, Tianhe-2 supercomputer, bit-flip operators, lazy distributed cooperation procedure, multiple independent D^2 TS processes, noncooperating D^2 TS processes, novel decentralized parallel search algorithm, parallel elite biased tabu search, search processes, sequential UBQP metaheuristic, sequential heuristics, unconstrained binary quadratic programming problem, Algorithm design and analysis, Heuristic algorithms, Memetics, Parallel algorithms, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969360}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969360}}, } @INPROCEEDINGS{scheepers:2017:CEC, author={C. Scheepers and A. P. Engelbrecht}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Vector evaluated particle swarm optimization: The archive's influence on performance}, year={2017}, editor = {Jose A. Lozano}, pages = {565--572}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-objective optimization (MOO) algorithms often use external archives to keep track of the Pareto-optimal solutions. Vector evaluated particle swarm optimization (VEPSO) is one such algorithm. In contrast to other MOO algorithms, VEPSO does not clearly define how to implement the archive. In this paper, the performance of various archive implementations, as found throughout the literature, are evaluated using the well-known Inverted Generational Distance (IGD) measure. A new archive implementation based on the hypersurface contribution is proposed and evaluated. The results show that overall the well-known crowding distance archive outperformed all other archive implementations. The hypersurface contribution archive also showed promise. Finally, it is shown that the distance metric and nearest neighbor archives perform worse than even the random archive.}, keywords = {Pareto optimisation, particle swarm optimisation, vectors, IGD measure, MOO algorithms, Pareto-optimal solutions, VEPSO, archive implementations, crowding distance archive, hypersurface contribution archive, inverted generational distance measure, multiobjective optimization, vector evaluated particle swarm optimization, Algorithm design and analysis, Computer science, Hypercubes, Measurement, Optical fibers, Optimization, Particle swarm optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969361}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969361}}, } @INPROCEEDINGS{garcia-martinez:2017:CEC, author={C. Garcia-Martinez and S. Ventura}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-view semi-supervised learning using genetic programming interpretable classification rules}, year={2017}, editor = {Jose A. Lozano}, pages = {573--579}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However to our knowledge, the multi-view learning paradigm has not been applied to produce interpretable rule-based classifiers before. In this work, we present a multi-view extension of a grammar-based genetic programming model for inducing rules for semi-supervised contexts. Its idea is to evolve several populations, and their corresponding views, favouring both the accuracy of the predictions for the labelled patterns and the prediction agreement with the other views for unlabelled ones. We have carried out experiments with two to five views, on six common datasets for fully-supervised learning that have been partially anonymised for our semi-supervised study. Our results show that the multi-view paradigm allows to obtain slightly better rule-based classifiers, and that two views becomes preferred.}, keywords = {genetic algorithms, genetic programming, learning (artificial intelligence), pattern classification, grammar-based genetic programming model, interpretable classification rules, multiview semi-supervised learning, rule-based classifiers, semi-supervised contexts, Context, Kernel, Semisupervised learning, Sociology, Statistics, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969362}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969362}}, } @INPROCEEDINGS{silva:2017:CEC, author={I. R. M. Silva and E. F. G. Goldbarg and E. B. de Carvalho and M. C. Goldbarg}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={MO-MAHM: A parallel Multi-agent Architecture for Hybridization of Metaheuristics for multi-objective problems}, year={2017}, editor = {Jose A. Lozano}, pages = {580--587}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Several researches have pointed the hybridization of metaheuristics as an effective way to deal with combinatorial optimization problems. Hybridization allows the combination of different techniques, exploiting the strengths and compensating the weakness of each of them. MAHM is a promising adaptive framework for hybridization of metaheuristics, originally designed for single objective problems. This framework is based on the concepts of intelligent agents and Particle Swarm. In this study we propose an extension of MAHM to the multi-objective scenario. The proposed framework is called MO-MAHM. To adapt MAHM to the multi-objective context, we redefine some concepts such as particle position and velocity. In this study the proposed framework is applied to the multi-objective Symmetric Travelling Salesman Problem. Four methods are hybridized: PAES, GRASP, NSGA-II and Anytime-PLS. Experiments with 12 bi-objective instances were performed and the results show that MO-MAHM is able to provide better non-dominated sets in comparison to the ones obtained by each of the hybridized algorithms.}, keywords = {multi-agent systems, particle swarm optimisation, travelling salesman problems, GRASP, MO-MAHM, NSGA-II, PAES, anytime-PLS, combinatorial optimization problems, intelligent agents, multiagent architecture for hybridization of metaheuristics, multiobjective problems, multiobjective symmetric travelling salesman problem, parallel multiagent architecture, particle swarm optimization, Computer architecture, Context, Linear programming, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969363}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969363}}, } @INPROCEEDINGS{gonzález:2017:CEC, author={M. González and J. J. López-Espín and J. Aparicio and D. Giménez and E. G. Talbi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A parameterized scheme of metaheuristics with exact methods for determining the Principle of Least Action in Data Envelopment Analysis}, year={2017}, editor = {Jose A. Lozano}, pages = {588--595}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Data Envelopment Analysis (DEA) is a nonparametric methodology for estimating technical efficiency of a set of Decision Making Units (DMUs) from a dataset of inputs and outputs. This paper is devoted to computational aspects of DEA models under the application of the Principle of Least Action. This principle guarantees that the efficient closest targets are determined as benchmarks for each assessed unit. Usually, these models have been addressed in the literature by applying unsatisfactory techniques, based fundamentally on combinatorial NP-hard problems. Recently, some heuristics have been developed to partially solve these DEA models. This paper improves the heuristic methods used in previous works by applying a combination of metaheuristics and an exact method. Also, a parameterized scheme of metaheuristics is developed in order to implement metaheuristics and hybridations/combinations, adapting them to the particular problem proposed here. In this scheme, some parameters are used to study several types of metaheuristics, like Greedy Random Adaptative Search Procedure, Genetic Algorithms or Scatter Search. The exact method is included inside the metaheuristic to solve the particular model presented in this paper. A hyperheuristic is used on top of the parameterized scheme in order to search, in the space of metaheuristics, for metaheuristics that provide solutions close to the optimum. The method is competitive with exact methods, obtaining fitness close to the optimum with low computational time.}, keywords = {combinatorial mathematics, computational complexity, data envelopment analysis, decision making, DEA, DMU, combinatorial NP-hard problems, decision making units, genetic algorithms, greedy random adaptative search procedure, hyperheuristic, metaheuristics, nonparametric methodology, parameterized scheme, principle of least action, scatter search, technical efficiency estimation, Computational modeling, Mathematical model, Mathematical programming, Operations research, Production}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969364}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969364}}, } @INPROCEEDINGS{delgado-pérez:2017:CEC, author={P. Delgado-Pérez and I. Medina-Bulo and M. Núñez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Using Evolutionary Mutation Testing to improve the quality of test suites}, year={2017}, editor = {Jose A. Lozano}, pages = {596--603}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary algorithm to produce a reduced subset of mutants but with a high proportion of mutants that can help the tester derive new test cases (strong mutants). In this paper, we go a step further in estimating the ability of this technique to induce the generation of test cases. Instead of measuring the percentage of strong mutants within the subset of generated mutants, we compute how much the test suite is actually improved thanks to those mutants. In our experiments, we have compared the extent to which EMT and the random selection of mutants help to find missing test cases in C++ object-oriented systems. We can conclude from our results that the percentage of mutants generated with EMT is lower than with the random strategy to obtain a test suite of the same size and that the technique scales better for complex programs.}, keywords = {C++ language, evolutionary computation, fault diagnosis, object-oriented methods, program testing, random processes, sampling methods, C++ object-oriented systems, EMT, complex programs, evolutionary algorithm, evolutionary mutation testing, fault detection capability, mutants, random selection, subset, test suite quality, C++ languages, Computational efficiency, Electronic mail, Encoding, Genetic algorithms, Testing, C++, Mutation testing, object-oriented programming}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969365}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969365}}, } @INPROCEEDINGS{juárez-castillo:2017:CEC, author={E. Juárez-Castillo and H. G. Acosta-Mesa and E. Mezura-Montes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Empirical study of bound constraint-handling methods in Particle Swarm Optimization for constrained search spaces}, year={2017}, editor = {Jose A. Lozano}, pages = {604--611}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents an empirical study comparing the performance of thirty-five boundary constraint-handling methods (BCHM) for PSO in constrained optimization, which were tested in a set of thirty-six well-known constrained problems. Each BCHM is composed as an hybrid consisting of one position update techniques and one velocity update strategy. Results show that the hybrid method that relocates the particles through a position update technique called Centroid and modifies its velocity through the Deterministic Back strategy is able to promote better final results and improving both, the approach to the feasible region and the ability to generate better feasible solutions.}, keywords = {constraint handling, mathematics computing, particle swarm optimisation, search problems, BCHM, PSO, bound constraint-handling methods, boundary constraint-handling methods, centroid, constrained optimization, constrained search spaces, deterministic back strategy, particle swarm optimization, position update technique, Algorithm design and analysis, Linear programming, Mathematical model, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969366}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969366}}, } @INPROCEEDINGS{zhu:2017:CEC, author={Ziming Zhu and Xiong Xu and Li Jiao}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improved evolutionary generation of test data for multiple paths in search-based software testing}, year={2017}, editor = {Jose A. Lozano}, pages = {612--620}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Search-based software testing has achieved great attention recently, but the efficiency is still the bottleneck of it. This paper focuses on improving the efficiency of generating test data for multiple paths. Genetic algorithms are chosen as the heuristic algorithms in search-based software testing in this paper. First, we propose an improved grouping strategy of target paths to balance the load of each calculation resource. This work makes a contribution to the parallel execution in search-based software testing. Then, common constraints of the target paths in the same group are collected to reduce the search space of test data. Symbolic execution technique is used in this phase. Based on the reduced search space, we can accelerate the convergence of search process and improve the efficiency of search-based software testing. Finally, our method is applied to some study cases to compare with other methods.}, keywords = {genetic algorithms, parallel processing, program testing, resource allocation, search problems, calculation resource, heuristic algorithms, improved grouping strategy, load balancing, multiple paths, parallel execution, search process convergence, search space reduction, search-based software testing, target paths, test data evolutionary generation, Optimization, Software, Software algorithms, Software testing, symbolic execution}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969367}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969367}}, } @INPROCEEDINGS{palar:2017:CEC, author={P. S. Palar and K. Shimoyama}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On multi-objective efficient global optimization via universal Kriging surrogate model}, year={2017}, editor = {Jose A. Lozano}, pages = {621--628}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper investigates the capability of universal Kriging (UK), or Kriging with a trend, approximator enhanced with the efficient global optimization (EGO) method to solve expensive multi-objective design optimization problem. Engineering optimization problems typically can be well described with smooth and polynomial-like behavior, which is the main rationale to apply UK over the ordinary Kriging (OK) as the approximator. The UK with orthogonal polynomials and basis selection based on least-angle-regression is utilized for this purpose. Results and demonstration on three synthetic functions using expected hypervolume improvement (EHVI) and Euclidean-based expected improvement (EEI) criterions show the increased quality of the optimized non-dominated solutions when UK is coupled with EHVI criterion. On the other hand, the coupling of UK with EEI does not lead to any improvement and might produce an adverse effect. We also observed that the use of UK mainly improves the proximity to the true Pareto front, with smaller but notable effect on the diversity of the solutions when EHVI is applied as the criterion. As expected, optimization using the UK shows the greatest improvement if all objective functions can be sufficiently approximated by the UK. Based on the results, we suggest that coupling of UK and EHVI criterion is a potential approach to solve the expensive real-world multi-objective optimization problem.}, keywords = {Pareto optimisation, polynomials, regression analysis, EEI criterions, EGO method, EHVI criterion, Euclidean-based expected improvement, Pareto front, engineering optimization problems, expected hypervolume improvement, least-angle-regression, multiobjective design optimization problem, multiobjective efficient global optimization, optimized nondominated solutions, ordinary Kriging, orthogonal polynomials, universal Kriging surrogate model, Correlation, Design optimization, Linear programming, Market research, Standards, Uncertainty}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969368}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969368}}, } @INPROCEEDINGS{sam:2017:CEC, author={M. Sam and S. Boddhu and J. Gallagher}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A dynamic search space approach to improving learning on a simulated Flapping Wing Micro Air Vehicle}, year={2017}, editor = {Jose A. Lozano}, pages = {629--635}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Those employing Evolutionary Algorithms (EA) are constantly challenged to engineer candidate solution representations that balance expressive power (I.E. can a wide variety of potentially useful solutions be represented?) and meta-heuristic search support (I.E. does the representation support fast acquisition and subsequent fine-tuning of adequate solution candidates). In previous work with a simulated insect-like Flapping-Wing Micro Air Vehicle (FW-MAV), an evolutionary algorithm was employed to blend descriptions of wing flapping patterns to restore correct flight behavior after physical damage to one or both of the wings. Some preliminary work had been done to reduce the overall size of the search space as a means of improving time required to acquire a solution. This of course would likely sacrifice breadth of solutions types and potential expressive power of the representation. In this work, we focus on methods to improve performance by augmenting EA search to dynamically restrict and open access to the whole space to improve solution acquisition time without sacrificing expressive power of the representation. This paper will describe some potential restriction/access control methods and provide preliminary experimental results on the efficacy of these methods in the context of adapting FW-MAV wing gaits.}, keywords = {aerospace components, aerospace simulation, autonomous aerial vehicles, evolutionary computation, gait analysis, learning (artificial intelligence), microrobots, search problems, EA search, FW-MAV, FW-MAV wing gaits, acquisition time, candidate solution representations, dynamic search space approach, evolutionary algorithms, flight behavior, learning, meta-heuristic search support, physical damage, restriction/access control methods, simulated insect-like flapping-wing microair vehicle, wing flapping patterns, Atmospheric modeling, Computational modeling, Force, Frequency control, Oscillators, Table lookup}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969369}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969369}}, } @INPROCEEDINGS{chen:2017:CECb, author={L. Chen and B. Xin and J. Chen and Juan Li}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A virtual-decision-maker library considering personalities and dynamically changing preference structures for interactive multiobjective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {636--641}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Interactive multiobjective optimization (IMO) methods aim at supporting human decision makers (DMs) to find their most preferred solutions in solving multiobjective optimization problems. Due to the subjectivity of human DMs, human fatigue, or other limiting factors, it is hard to design experiments involving human DMs to evaluate and compare IMO methods. In this paper, we propose a framework of a virtual-DM library consisting of a variety of virtual DMs which reflect characteristics of different types of human DMs. The virtual-DM library is used to replace human DMs to interact with IMO methods. The virtual DMs in the library can express different types of preference information and their most preferred solutions are known. When interacting with an IMO method, the library can select an appropriate virtual DM to provide preference information that the method asks for based on solutions offered by the method. Four types of hybrid virtual DMs are constructed to emulate human DMs with different personalities and dynamically changing preference structures. They can be used to test the ability of IMO methods to adapt to different human DMs and capture DMs' preferences. The usage of these four types of virtual DMs are demonstrated by comparing two IMO algorithms.}, keywords = {behavioural sciences computing, decision making, optimisation, IMO, dynamically changing preference structures, human decision makers, human fatigue, interactive multiobjective optimization, personalities, preference information, virtual-DM library, virtual-decision-maker library, Computational complexity, Convergence, Fatigue, Libraries, Measurement, Pareto optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969370}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969370}}, } @INPROCEEDINGS{tao:2017:CEC, author={Yanyun Tao and Qinyu Wang and Yuzhen Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Fuzzy c-mean clustering-based decomposition for low power FSM synthesis}, year={2017}, editor = {Jose A. Lozano}, pages = {642--648}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Most published results show that power reduction of the finite-state machines (FSMs) is achieved by decomposition. In order to achieve a low power FSM implementation, a Genetic Fuzzy c-mean clustering-based decomposition method, called GFCM-D, is proposed for FSM partition in this study. GFCM-D used Fuzzy c-mean clustering (FCM) to partition a set of states of FSM into a collection of c fuzzy clusters, then a FSM is decomposed into several sub machines. For achieving low power, the objective function of GFCM-D is to minimize the cross state transition probability between sub machines and increase the inner state transition probability within the submachine. Genetic algorithm (GA) is used as a shell, which applies selection, crossover and mutation for generating better centers and more appropriate clusters. We have tested our approach, GFCM-D, extensively on fifteen benchmarks, comparing it with previous FSM synthesis methods from various aspects. The experimental results show that GFCM-D has achieved a significant cost reduction of both dynamic power and leakage power dissipation over the previous publications.}, keywords = {finite state machines, fuzzy set theory, genetic algorithms, logic design, pattern clustering, probability, GA, GFCM-D method, cost reduction, cross state transition probability, crossover operation, dynamic power dissipation, fuzzy clusters, genetic algorithm, genetic fuzzy c-mean clustering-based decomposition, leakage power dissipation, low power FSM synthesis, mutation operation, selection operation, Encoding, Genetics, Linear programming, Optimization, Power dissipation, Switches, dynamic power, finite-state machine, fuzzy c-mean clustering}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969371}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969371}}, } @INPROCEEDINGS{wang:2017:CECd, author={H. Wang and Y. Jin}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Efficient nonlinear correlation detection for decomposed search in evolutionary multi-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {649--656}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The mapping relation between decision variables and objective functions is complicated in multi-objective optimization problems. Dimension reduction-based memetic optimization strategy was proposed to decompose a multi-objective optimization problem into several easier subproblems in decision subspaces by detecting the correlation between decision variables and objective functions. In this work, the process of optimizing the original problem by separately searching the decision space of the subproblems is termed decomposed search. We embed the decomposed search strategy in existing multi-objective evolutionary algorithms to improve their performance. However, it is highly time-consuming to detect the mapping relation and select solutions for decomposed search. To improve the computational efficiency of the strategy, we adopt nonlinear correlation information entropy to measure the correlation between the decision variables and objective functions and suggest a probabilistic similarity measurement to select solutions for the decomposed search, which is shown to be effective by experimental results. Finally, the correlation detection and solution selection strategies proposed in this paper are embedded in both Pareto- and non-Pareto-based multi-objective evolutionary algorithms to compare them with existing ones. Our experimental results demonstrate that the proposed strategies have significantly improved the computational efficiency at the expense of slightly degraded performance.}, keywords = {Pareto optimisation, decision theory, entropy, evolutionary computation, probability, search problems, decision subspaces, decision variables, decomposed search strategy, dimension reduction-based memetic optimization strategy, evolutionary multiobjective optimization, nonPareto-based multiobjective evolutionary algorithms, nonlinear correlation detection, nonlinear correlation information entropy, objective functions, probabilistic similarity measurement, solution selection strategies, Correlation, Linear programming, Optimization, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969372}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969372}}, } @INPROCEEDINGS{mahdavi:2017:CEC, author={S. Mahdavi and S. Rahnamayan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Schematic study on interaction and imbalance effects of variables for Large-Scale Optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {657--664}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the recent years, Large-Scale Global Optimization (LSGO) algorithms attempt to solve real-world problems efficiently. The imbalance in the contribution of variables and the interaction among variables pose major challenges for LSGO algorithms. This paper proposes mapping schemes based on the interaction among variables and the imbalance in the contribution of variables. The proposed mapping schemes present the different relations between the constructed class of variables according to the interaction feature and the constructed class of variables according to the imbalance feature. Covering a wide range of real-world problems is considered in the mapping schemes; therefore it can provide some insights to design LSGO benchmark suites. By developing LSGO benchmark suites with the ability of representing many-real world problems, researchers will be motivated to realize the success or failure level of LSGO algorithms for tackling various types of LSGO problems. Also, a preliminary set of experiments is conducted to present the importance of considered features in each scheme.}, keywords = {optimisation, LSGO algorithms, LSGO benchmark suites, failure level, imbalance feature, interaction feature, large-scale global optimization algorithms, mapping schemes, success level, variable contribution, Algorithm design and analysis, Benchmark testing, Computers, Linear programming, Optimization, Software algorithms, Software engineering}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969373}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969373}}, } @INPROCEEDINGS{toriyama:2017:CEC, author={N. Toriyama and K. Ono and Y. Orito}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Adaptive GA-based AR-hidden Markov model for time series forecasting}, year={2017}, editor = {Jose A. Lozano}, pages = {665--672}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The optimization of a model that expresses time series data for a given period is a problem associated with the development of a regression model that estimates future data on the extension of the past data time series. This is a two-step optimization problem where the order of past data used in the regression model (number of orders of the solution space) is decided, and weighted coefficients for observed data at each point in time (design variables) are determined. Such a two-step optimization problem cannot calculate the evaluation function after the model is developed despite the fact that design variables cannot be determined during the second step until the orders of solution space are determined in the first step. Thus, it is a problem where simultaneous optimization of both the first and second steps is difficult, and a regression model with the smallest evaluation function is chosen as the optimal model after comparison with all the solutions. However, the self-regressing hidden Markov model used in this study for regression requires a large amount of calculation during design variable determination due to the use of the hidden Markov. Furthermore, the calculation and comparison of all the solutions is inefficient. For such an optimization problem, this study proposes an actual-value genetic algorithm (GA) with a framework capable of simultaneous optimization of the first and second steps. The proposed method takes an approach where in individuals that represent the solution space of different orders in the same group are generated. Further, it retains individuals with an order that has a large number of good solutions at a high probability through the transition of the generations. Numerical tests will involve performance validation using estimated artificial data of several states, realized volatility (RV) of the stock, and artificial as well as actual data of inbound visitors to Japan, and they will demonstrate that the optimization of the regression mo- el by the proposed method is more effective than that by the conventional method.}, keywords = {forecasting theory, genetic algorithms, hidden Markov models, regression analysis, time series, Japan, RV, actual-value genetic algorithm, adaptive GA-based AR-hidden Markov model, data time series, estimated artificial data, evaluation function, future data, inbound visitors, model optimization, performance validation, realized stock volatility, regression model, self-regressing hidden Markov model, solution space, time series forecasting, two-step optimization problem, Computational modeling, Data models, Estimation, Mathematical model, Optimization, Time series analysis}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969374}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969374}}, } @INPROCEEDINGS{fahy:2017:CEC, author={C. Fahy and S. Yang and M. Gongora}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Finding Multi-Density Clusters in non-stationary data streams using an Ant Colony with adaptive parameters}, year={2017}, editor = {Jose A. Lozano}, pages = {673--680}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters.}, keywords = {ant colony optimisation, pattern clustering, ACMDC, adaptive parameters, ant colony multidensity clustering, density based algorithm, density based stream-clustering algorithm, nonstationary data stream clustering, sensitive data dependent parameters, Algorithm design and analysis, Clustering algorithms, Euclidean distance, Heuristic algorithms, Partitioning algorithms, Robustness}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969375}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969375}}, } @INPROCEEDINGS{marzullo:2017:CEC, author={A. Marzullo and C. Stamile and G. Terracina and F. Calimeri and S. Van Huffel}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A tensor-based mutation operator for Neuroevolution of Augmenting Topologies (NEAT)}, year={2017}, editor = {Jose A. Lozano}, pages = {681--687}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the population throughout the evolutionary process. Various kinds of mutation may occur over time, typically depending on a fixed probability value called mutation rate. In this work we make use of a novel data-science approach in order to adaptively generate mutation rates for each locus to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. The trail of high quality candidate solutions obtained during the search process is represented as a third-order tensor; factorization of such a tensor reveals the latent relationship between solutions, determining the mutation probability which is likely to yield improvement at each locus. The single pole balancing problem is used as case study to analyze the effectiveness of the proposed approach. Results show that the tensor approach improves the performance of the standard NEAT algorithm for the case study.}, keywords = {genetic algorithms, neural nets, probability, search problems, tensors, NEAT, data-science approach, evolutionary process, fixed probability value, genetic diversity, high quality candidate solutions, latent relationship, mutation probability, mutation rate, neuroevolution of augmenting topologies, search process, single pole balancing problem, tensor-based mutation operator, third-order tensor, Biological cells, Biological neural networks, Neurons, Sociology, Tensile stress, Topology, Genetic Algorithm, Mutation, Tensor decomposition}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969376}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969376}}, } @INPROCEEDINGS{arrieta:2017:CEC, author={A. Arrieta and S. Wang and U. Markiegi and G. Sagardui and L. Etxeberria}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Search-based test case generation for Cyber-Physical Systems}, year={2017}, editor = {Jose A. Lozano}, pages = {688--697}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The test case generation of Cyber-Physical Systems (CPSs) face critical challenges that traditional methods such as Model-Based Testing cannot deal with. As a result, simulation-based testing is one of the most commonly used techniques for testing CPSs despite sometimes being computationally too expensive. This paper proposes a search-based approach which is implemented on top of Non-dominated Sorting Genetic Algorithm II (NSGA-II), the most commonly applied multi-objective search algorithm for cost-effectively generating executable test cases in order to test CPSs. With the aim of guiding the generation of the optimal set of so-called reactive test cases, the approach formally defines three cost-effectiveness measures: requirements coverage, test case similarity and test execution time. Furthermore, we design one crossover operator and three mutation operators (i.e., mutation at test suite level named Mu TS, mutation at test case level named Mu TC and mutation at both levels named Mu BO) for test case generation. We evaluate our approach by comparing with Random Search (RS) using four case studies (one of them is an industrial system). Moreover, we evaluate the three mutation operators using the four case studies. The results of the experiment (with a rigorous statistical analysis) indicated that our approach in conjunction with the crossover operator operation and three mutation operators significantly outperformed RS. In general, Mu BO achieved the best performance among the three mutation operators and managed to improve on average the test execution time by 14%, the requirements coverage by 34%, and the test similarity by 75% as compared with RS.}, keywords = {cyber-physical systems, genetic algorithms, program testing, statistical analysis, CPS, NSGA-II, RS, crossover operator operation, model-based testing, multiobjective search algorithm, mutation operators, non-dominated sorting genetic algorithm II, random search, rigorous statistical analysis, search-based approach, search-based test case generation, simulation-based testing, test case level, test case similarity, test execution time, test suite level, Automobiles, Brakes, Computational modeling, Engines, Mathematical model, Testing, Tools}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969377}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969377}}, } @INPROCEEDINGS{bošković:2017:CEC, author={B. Bošković and J. Brest}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Clustering and differential evolution for multimodal optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {698--705}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a new differential evolution algorithm for multimodal optimization that uses self-adaptive parameter control, clustering and crowding methods. The algorithm includes a new clustering mechanism that is based on small subpopulations with the best strategy and, as such, improves the algorithm's efficiency. Each subpopulation is generated according to the best individual from a population that is not added to any other subpopulation. These small subpopulations are also used to determine population size and to replace `bad' individuals. Because of the small subpopulation size and crowding mechanism, bad individuals prevent the best individuals from converging to the optimum. Therefore, the algorithm is trying to replace bad individuals with the individuals that are close to the best individuals. The population size expansion is used within the algorithm according to the number of generated subpopulations and located optima. The proposed algorithm was tested on benchmark functions for CEC'2013 special session and competition on niching methods for multimodal function optimization. The performance of the proposed algorithm was comparable with the state-of-the-art algorithms.}, keywords = {evolutionary computation, differential evolution algorithm, multimodal function optimization, population size, Algorithm design and analysis, Clustering algorithms, Clustering methods, Euclidean distance, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969378}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969378}}, } @INPROCEEDINGS{laña:2017:CEC, author={I. Laña and J. Del Ser and M. Vélez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting}, year={2017}, editor = {Jose A. Lozano}, pages = {706--713}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver.}, keywords = {computational complexity, forecasting theory, optimisation, road traffic, FWA-WID, contributed heuristic optimization method, convergence speed, fireworks algorithm, naive FWA solver, parameter optimization, road traffic short-term predictive model, traffic forecasting, wind inertia dynamics, Convergence, Explosions, Heuristic algorithms, Next generation networking, Optimization, Prediction algorithms, Sparks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969379}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969379}}, } @INPROCEEDINGS{kim:2017:CEC, author={J. H. Kim and H. M. Lee and Donghwi Jung and A. Sadollah}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Engineering benchmark generation and performance measurement of evolutionary algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {714--717}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Various evolutionary algorithms are being developed to search the optimal solution of various problems in the real world. Evolutionary algorithms search solutions showing the optimal fitness to given problem using their own operators. Engineering benchmark problems can be used for performance measurement of evolutionary algorithms, and the water distribution network design problem is one of the widely used benchmark problems. In this study, the water distribution network design problems are generated by modifications of five problem characteristic factors. Generated benchmark problems are applied to quantitatively evaluate the performance among evolutionary algorithms. Each algorithm shows its own strength and weakness. Optimization results show that the engineering benchmark generation method suggested in this study can be served as a reliable framework for comparison of performances on various water distribution network design problems.}, keywords = {benchmark testing, evolutionary computation, water supply, engineering benchmark generation method, evolutionary algorithms, water distribution network design problems, Algorithm design and analysis, Genetic algorithms, Optimization, Water, Water resources, engineering approach, performance comparison, water distribution networks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969380}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969380}}, } @INPROCEEDINGS{lima:2017:CEC, author={R. H. R. de Lima and A. T. R. Pozo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {718--725}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Researches point out to the importance of automatic design of multi-objective evolutionary algorithms. Because in general, algorithms automatically designed outperform traditional multi-objective evolutionary algorithms from the literature. Nevertheless, until fairly recently, most of the researches have been focused on a small group of algorithms, often based on evolutionary algorithms. On the other hand, mono-objective Particle Swarm Optimization algorithm (PSO) have been widely used due to its flexibility and competitive results in different applications. Besides, as PSO performance depends on different aspects of design like the velocity equation, its automatic design has been targeted by many researches with encouraging results. Motivated by these issues, this work studies the automatic design of Multi-Objective Particle Swarm Optimization (MOPSO). A framework that uses a context-free grammar to guide the design of the algorithms is implemented. The framework includes a set of parameters and components of different MOPSOs, and two design algorithms: Grammatical Evolution (GE) and Iterated Racing (IRACE). Evaluation results are presented, comparing MOPSOs generated by both design algorithms. Furthermore, the generated MOPSOs are compared to the Speed-constrained MOPSO (SMPSO), a well-known algorithm using a set of Multi-Objective problems, quality indicators and statistical tests.}, keywords = {context-free grammars, evolutionary computation, particle swarm optimisation, statistical analysis, GE, IRACE, MOPSO algorithm, PSO performance, SMPSO, autoconfiguration study, context-free grammar, grammatical evolution, iterated racing, monoobjective particle swarm optimization algorithm, multiobjective evolutionary algorithms automatic design, multiobjective particle swarm optimization algorithm, speed-constrained MOPSO, statistical tests, velocity equation, Algorithm design and analysis, Genetic programming, Grammar, Particle swarm optimization, Production, Space exploration}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969381}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969381}}, } @INPROCEEDINGS{tasgetiren:2017:CEC, author={M. F. Tasgetiren and Q. K. Pan and D. Kizilay and M. C. Vélez-Gallego}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A variable block insertion heuristic for permutation flowshops with makespan criterion}, year={2017}, editor = {Jose A. Lozano}, pages = {726--733}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution, it replaces the current solution. It retains the same block size as long as it improves. Otherwise, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition, we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature.}, keywords = {flow shop scheduling, search problems, simulated annealing, PVBIH algorithm, acceptance criterion, local search, makespan criterion, permutation flowshop scheduling problem, populated variable block insertion heuristic algorithm, Cost function, Fitting, Heuristic algorithms, Job shop scheduling, Variable block insertion heuristic, flowshop scheduling, heuristic optimization, randomized profile fitting heuristic}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969382}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969382}}, } @INPROCEEDINGS{passos:2017:CEC, author={F. Passos and E. Roca and R. Castro-López and F. V. Fernández}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An algorithm for a class of real-life multi-objective optimization problems with a sweeping objective}, year={2017}, editor = {Jose A. Lozano}, pages = {734--740}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper describes a class of real-life optimization problems that has not been addressed before: a multi-objective optimization in which one objective is neither minimized nor maximized but uniformly swept over a wide range. The limitations of conventional multi-objective optimization algorithms to deal with this kind of problems are illustrated via the optimization of radiofrequency inductors. For the first time, an algorithm is proposed that provides sets of solutions for this kind of problems.}, keywords = {optimisation, radiofrequency inductors, real-life multiobjective optimization problems, Algorithm design and analysis, Inductance, Inductors, Minimization, Optimization, Q-factor, Radio frequency, multi-objective optimization, real-life optimization problem, sweeping objective}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969383}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969383}}, } @INPROCEEDINGS{silveira:2017:CEC, author={L. Â. da Silveira and J. L. Soncco-Álvarez and M. Ayala-Rincón}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Parallel genetic algorithms with sharing of individuals for sorting unsigned genomes by reversals}, year={2017}, editor = {Jose A. Lozano}, pages = {741--748}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Rearrangement by reversals is a suitable global operation when treating genomes with a single chromosome. Sorting unsigned genomes by reversals is an NP-hard optimization problem. Several approximation algorithms were proposed, among them, in previous work, a competitive genetic algorithm and its standard parallel version, that provides a substantial speedup, were introduced. In this paper, two approaches using island models to parallelize such algorithm are presented. The first approach uses the unidirectional ring communication topology to exchange individuals between neighboring islands and, the second uses a complete graph scheme for the distribution of individuals among islands. Both approaches were proposed with the objective of improving precision (that is, for reducing the number of reversals) and decreasing the runtime regarding the sequential GA. Experiments were performed with randomly generated synthetic genomes and the results show that the parallel approach using the ring communication topology outperforms the previously proposed GA and its parallel version in terms of accuracy, providing solutions with less reversals and, that the parallel approach using the complete graph topology does not provide significant improvements. Both the new parallel GA approaches get competitive speedups regarding the speedup achieved by the standard parallel version of the genetic algorithm.}, keywords = {genetic algorithms, genomics, graph theory, parallel algorithms, sorting, NP-hard optimization problem, complete graph topology, global operation, island models, parallel GA approach, parallel genetic algorithm, randomly-generated synthetic genomes, reversal rearrangement, sequential GA, unidirectional ring communication topology, unsigned genome sorting, Approximation algorithms, Sociology, Statistics, Topology, Transforms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969384}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969384}}, } @INPROCEEDINGS{garcía-valdez:2017:CEC, author={M. García-Valdez and J. C. Romero and A. Mancilla and J. J. M. Guervós}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Exploiting the social graph: Increasing engagement in a collaborative Interactive Evolution application}, year={2017}, editor = {Jose A. Lozano}, pages = {749--756}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Interactive evolution, where users' preferences guide the search, is one of the techniques employed by Evolutionary Art researchers. It can be implemented as a web application to lower the access threshold since it often depends on volunteers who visit the system for fitness assignment. However, several drawbacks limit user participation: human fatigue and boredom result from evaluating a large number of phenotypes. To tackle these issues, in this paper we propose an IEC system designed using a human-centered approach, with a framework consisting of a social network of volunteers interacting with a population also consisting of a network of phenotypes. The use of a graph model is proposed as a practical and efficient tool for mapping the relationships between actors and objects in the system. A case study is presented as proof-of-concept, providing both conceptual and implementation details of the graph model as it is applied in the implementation of an IEC system. Our experiments show that the data model can be successfully used to implement a gamification technique developed to increase users' engagement, which implies that this technique can be successfully used to decrease user fatigue and thus increase the performance of the interactive system.}, keywords = {Web sites, ergonomics, evolutionary computation, human computer interaction, IEC system, Web application, collaborative interactive evolution application, evolutionary art researchers, fitness assignment, human fatigue, human-centered approach, interactive system, phenotypes, proof-of-concept, social graph, user fatigue, Collaboration, Fatigue, IEC, Social network services, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969385}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969385}}, } @INPROCEEDINGS{bolufé-röhler:2017:CEC, author={A. Bolufé-Röhler and D. Tamayo-Vera and S. Chen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An LaF-CMAES hybrid for optimization in multi-modal search spaces}, year={2017}, editor = {Jose A. Lozano}, pages = {757--764}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Optimization in multi-modal search spaces requires both exploration and exploitation. The role of exploration is to find promising attraction basins, and the role of exploitation is to find the best solutions (i.e. the local optima) within these attraction basins. In many search techniques, the balance between exploration and exploitation can be adjusted by various parameter settings. An alternative approach is to develop (hybrid) techniques with distinct mechanisms for the task of exploration and the task of exploitation. We believe this second approach can be simpler and more effective. The presented LaF-CMAES hybrid involves relatively few design decisions (e.g. parameter selections), and it delivers highly competitive performance across a benchmark set of multi-modal functions.}, keywords = {optimisation, search problems, LaF-CMAES hybrid, attraction basins, design decisions, multimodal functions, multimodal search spaces, optimization, parameter settings, search techniques, Covariance matrices, Simulated annealing, Sociology, Space exploration, CMA-ES, Leaders and Followers, hybrid search techniques, multi-modal search spaces}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969386}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969386}}, } @INPROCEEDINGS{stor:2017:CEC, author={R. Storn}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Real-world applications in the communications industry - when do we resort to Differential Evolution?}, year={2017}, editor = {Jose A. Lozano}, pages = {765--772}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper describes various real-world design applications from the communications & measurement industry that have been solved by Differential Evolution (DE). The commonalities of these applications are discussed, and the characteristics are identified that gave rise to a DE-based design. It is further laid out which requirements need to be fulfilled to make DE more widespread and which expectations towards DE-research exist in the communications & measurement industry.}, keywords = {evolutionary computation, telecommunication industry, DE-based design, communication & measurement industry, differential evolution, real-world application, Cost function, Digital filters, ISDN, Poles and zeros, Quantization (signal), Standards, communications, measurement, real-world applications}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969387}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969387}}, } @INPROCEEDINGS{esmaelian:2017:CEC, author={M. Esmaelian and F. J. Santos-Arteaga and M. Tavana and M. Vali}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Subdividing Labeling Genetic Algorithm: A new method for solving continuous nonlinear optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {773--780}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope built on a n-dimensional space. After calculating the fitness of each point composing the polytope, SLGA implements a mutation operator to generate offspring and computes an integer label for the population of the polytope. Then, after completely labeling the polytope, a crossover operator is implemented so as to approach the optimum point by reducing the search space. In this regard, new population is generated by subdividing the search space and further implementing the mutation operator. SLGA has been used to optimize the De Jong functions, as well as nonlinear constrained and unconstrained problems with discrete, continuous and mixed variables. It has also been compared with other well-known algorithms. Experimental results show that the SLGA method has good performance and reduces the number of generations within the solution space, which enhances its convergence capability.}, keywords = {convergence, genetic algorithms, integer programming, nonlinear programming, search problems, De Jong functions, SLGA, continuous nonlinear optimization problems, convergence capability, crossover operator, integer label, multidimensional search space, mutation operator, n-dimensional space, nonlinear constrained problems, polytope, subdividing labeling genetic algorithm, Economics, Labeling, Optimization, Sociology, Statistics, fitness function, genetic algorithm, nonlinear optimization, subdividing labeling}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969388}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969388}}, } @INPROCEEDINGS{ono:2017:CEC, author={K. Ono and J. i. Kushida}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Landscape estimation of decision-tree induction based on grammatical Evolution using rank correlation}, year={2017}, editor = {Jose A. Lozano}, pages = {781--788}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The type of evolutionary machine learning known as grammatical Evolution (GE) is currently receiving a great deal of attention. GE is particularly suitable for developing decision-tree classifiers because of a framework, in which candidate solutions are generated via production rules. Various decision-tree classifier methods based on GE have been proposed. In general, the performance of GE systems is improved by enhancing the genetic diversity of the candidate solutions. Therefore, most GE methods are focused on the initialization of solutions. However, it is known that an effective search bias based on a landscape is also essential for evolutionary computation methods. Unfortunately, because of their solution structures, GE-based decision-tree classifiers can not form a unique landscape in terms of an objective function as can real-valued optimization problems. In this paper, we present a method for estimating a landscape using rank correlation based on two types of features extracted from GE solutions, and we apply it to well-known benchmark problems. We show that the proposed method can capture a landscape effectively. To the best of the authors' knowledge, this is the first study to report about a landscape estimation method based on GE solutions. The results in this paper help with understanding how to establish suitable a search bias for GE-based decision-tree classifiers.}, keywords = {decision trees, evolutionary computation, knowledge based systems, learning (artificial intelligence), pattern classification, GE systems, GE-based decision-tree classifiers, benchmark problems, candidate solution genetic diversity, decision-tree induction, evolutionary computation methods, evolutionary machine learning, grammatical evolution, landscape estimation, production rules, rank correlation, real-valued optimization problems, search bias, solution initialization, Correlation, Estimation, Feature extraction, Linear programming, Optimization, Production}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969389}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969389}}, } @INPROCEEDINGS{kabadurmus:2017:CEC, author={O. Kabadurmus and M. S. Erdogan and M. F. Tasgetiren}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Design of multi-product multi-period two-echelon supply chain network to minimize bullwhip effect through differential evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {789--796}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A supply chain network consists of facilities located in dispersed geographical locations. This network structure can be optimized to minimize total cost or total inventory by deciding the order quantities and distribution of links connecting the facilities. However, bullwhip effect (i.e., amplification of order fluctuations) is an important performance metric for supply chains because as the order variance increases in the downstream of the supply chain (e.g., distributors), the demand variance in the upstream (e.g., manufacturer) amplifies and causes inefficiencies in the supply chain. In this study, we optimize supply chain network structure for multi-product multi-period two-echelon supply chain networks to minimize bullwhip. Due to nonlinear structure of the objective function, i.e., bullwhip effect, this paper proposes a differential evolution (DE) algorithms employing variable neighborhood search (VNS) and constraint handling methods to optimize supply chain network structure. The proposed algorithm is tested over randomly generated test instances and its effectiveness is demonstrated.}, keywords = {constraint handling, evolutionary computation, facility location, minimisation, search problems, supply chain management, VNS, bullwhip effect minimization, constraint handling methods, demand variance, differential evolution algorithms, dispersed geographical locations, links distribution, multiproduct multiperiod two-echelon supply chain network design, order fluctuations amplification, order quantities, order variance, total cost minimization, total inventory minimization, variable neighborhood search, Adaptation models, Analytical models, Information management, Lead, Linear programming, Mathematical model, Supply chains, bullwhip effect, ensemble of differential evolution, supply chain}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969390}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969390}}, } @INPROCEEDINGS{chen:2017:CECc, author={Xiaoji Chen and Chuan Shi and A. Zhou and Bin Wu and Zixing Cai}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A decomposition based multiobjective evolutionary algorithm with semi-supervised classification}, year={2017}, editor = {Jose A. Lozano}, pages = {797--804}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In multiobjective evolutionary algorithms, how to select the optimal solutions from the offspring candidate set significantly affects the optimization process. Usually, the selection process is largely based on the real objective values or surrogate model estimating objective values. However, these selection processes are very time consuming sometimes, especially for some real optimization problems. Recently, some researches began to employ supervised classification to assist offspring selection, but these works are difficult to prepare the exact positive and negative samples or time consuming of parameter tuning problems. In order to solve these disadvantages, we propose a decomposition based multiobjective evolutionary algorithm with semi-supervised classification. This approach using random sampling and non-dominated sorting to construct semi supervised classifier. In each generation, a set of candidate solutions are generated for each subproblem and only good solutions are reserved by classifier. If there is more than one good solutions, we calculate each of good solutions by real objective function and choose the best one as the offspring solution. Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, we design algorithm framework through integrating the novel offspring selection process based on semi-supervised classification. Experiments show that the proposed algorithm performs best in most test cases and improves the performance of MOEA/D.}, keywords = {evolutionary computation, learning (artificial intelligence), pattern classification, MOEA/D, decomposition based multiobjective evolutionary algorithm, nondominated sorting, random sampling, semi-supervised classification, Algorithm design and analysis, Optimization, Predictive models, Sociology, Sorting, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969391}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969391}}, } @INPROCEEDINGS{molina:2017:CEC, author={D. Molina and F. Moreno-García and F. Herrera}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement?}, year={2017}, editor = {Jose A. Lozano}, pages = {805--812}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={For years, there have been organized single objective real-parameter optimization competitions on the IEEE Congress on Evolutionary Computation, in which the organizer define a common experimental, the researchers carry out the experiments with their proposals using it, and the obtained results are compared. It is a excellent way to know which algorithms (and ideas) can improve others, creating guidelines to improve the field. However, in several competitions the benchmark can change and the winners of previous benchmarks are not always introduced into the comparisons. Due to that, it could be not clear the improvement that new proposals offer against proposals of previous years. In this paper, we compare the winners in different years among them using the different proposed benchmarks, and we analyse the results obtained by all of them to observe whether there is an real improvement or not by the winner proposals of these competitions through the years.}, keywords = {evolutionary computation, IEEE CEC competitions, IEEE Congress, objective real-parameter optimization competitions, real-parameters optimization, Algorithm design and analysis, Benchmark testing, Genetic algorithms, Optimization, Proposals, Sociology, Statistics, Comparisons, Continuous Optimization, state-of-art}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969392}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969392}}, } @INPROCEEDINGS{liu:2017:CECa, author={J. Liu and P. Li and Xiaoning Ma and Jiaxun Xie}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Modeling, analysis and simulation on searching for global optimum region of particle swarm optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {813--821}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the case of particle swarm optimization, this paper mainly analyzes and discusses the mathematical model and the analysis on searching for the global optimum region. Firstly, the global optimum region Θ is defined and calculated in the convergence step and the divergence step. Furthermore, the rate μ of locating into the global optimum region is mathematically related to the number of particles, the number of generations, the fitness landscape, the ratio between exploration ability and exploitation ability, etc. Simulation results on Schaffer f6 function can help to understand the obtained results. Finally, those corresponding results and several remarks in the paper are helpful for the tradeoff between exploration ability and exploitation ability, together with the suitable searching strategy of particle swarm optimization algorithm.}, keywords = {particle swarm optimisation, convergence step, divergence step, exploitation ability, exploration ability, global optimum region, particle swarm optimization, Algorithm design and analysis, Convergence, Linear programming, Mathematical model, Optimization, Trajectory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969393}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969393}}, } @INPROCEEDINGS{strub:2017:CEC, author={O. Strub and N. Trautmann}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A genetic algorithm for the UCITS-constrained index-tracking problem}, year={2017}, editor = {Jose A. Lozano}, pages = {822--829}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We consider the problem of replicating the returns of a financial index as accurately as possible by selecting a subset of the assets that constitute the index and determining the portfolio weight of each selected asset subject to various constraints that are relevant in practice, including the UCITS III (Undertakings for Collective Investments in Transferable Securities) 5/10/40 concentration rule. For this problem, we present a genetic algorithm, in which the individuals correspond to subsets of the index constituents. The fitness of the individuals is determined by applying mixed-integer quadratic programming. Two main features of the presented genetic algorithm are novel. First, we use a representation of subsets which is the first that exhibits all of the four desirable properties feasibility, efficiency, locality, and heritability. The representation also allows to incorporate problem-specific knowledge in a very simple way. Second, to reduce the CPU time for the fitness evaluations, we first estimate the fitness of the individuals in an efficient way and then evaluate the fitness of promising individuals only. The results of a computational experiment based on real-world data demonstrate that in particular for large instances, the presented genetic algorithm devises very good solutions in short CPU time.}, keywords = {genetic algorithms, integer programming, investment, quadratic programming, CPU time reduction, UCITS-constrained index-tracking problem, asset subject, financial index returns, fitness evaluations, genetic algorithm, index funds, mixed-integer quadratic programming, portfolio weight, problem-specific knowledge, subsets representation, undertakings for collective investments in transferable securities, Indexes, Linear programming, Optimization, Portfolios, Probability}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969394}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969394}}, } @INPROCEEDINGS{myburgh:2017:CEC, author={C. Myburgh and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Use of derived heuristics in improved performance of evolutionary optimization: An application to gold processing plant}, year={2017}, editor = {Jose A. Lozano}, pages = {830--837}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The importance of using heuristics in an optimization algorithm is well established, particularly in solving complex real-world problems. It is then expected that users know certain key problem information a priori and are able to implement the information in a suitable optimization algorithm. However, in many problems, such problem information may not be available before an optimization task is performed, thereby making the heuristics-based algorithms difficult to be implemented. In this paper, we suggest a `derived heuristics' based optimization methodology for this purpose. In such a method, past results from an optimization algorithm are utilized to derive problem heuristics and then used in a future applications to achieve a faster and more accurate optimization task. Heuristics can also be derived from the optimization run and used in subsequent iterations. In a particular gold processing plant optimization problem, we demonstrate the use of derived heuristics by developing a customized evolutionary optimization procedure which is capable of handling various complexities offered by the problem, in a way which is much better than a classical point-based method and a population-based generic approach. The results of this paper is motivating for evolutionary computation researchers to apply the methodology to other more complex real-world problems.}, keywords = {evolutionary computation, industrial plants, mineral processing, classical point-based method, customized evolutionary optimization procedure, derived heuristics, derived heuristics based optimization methodology, gold processing plant, heuristics-based algorithm, optimization algorithm, population-based generic approach, Complexity theory, Gold, Heuristic algorithms, Linear programming, Optimization, Oxidation, Sulfur}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969395}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969395}}, } @INPROCEEDINGS{garcía-ródenas:2017:CEC, author={R. García-Ródenas and L. J. Linares and J. A. López-Gómez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A cooperative Brain Storm Optimization Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {838--845}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the last few years, hybridization has spread as an effective technique to solve hard optimization problems where metaheuristics algorithms have been unable to find global optima in a computational cost given. In this article, we propose the so-called cooperation strategy. This way is an alternative to hybridization in which different algorithms work together in order to find a global optimum following an intrinsically parallel approach. Different homogeneous and heterogeneous strategies using CEC benchmark functions have been designed using Brain Storm Optimization (BSO) metaheuristic in comparison with a hybrid BSO, showing that cooperation improves significantly hybridization results and the original BSO.}, keywords = {evolutionary computation, BSO metaheuristic, CEC benchmark functions, cooperation strategy, cooperative brain storm optimization algorithm, hybridization technique, metaheuristics algorithm, Algorithm design and analysis, Clustering algorithms, Computational efficiency, Optimization, Sociology, Statistics, Storms, Brain Storm Optimization, Cooperative Strategies, Fuzzy Rules, Hybridization, Metaheuristics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969396}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969396}}, } @INPROCEEDINGS{camacho:2017:CEC, author={A. Camacho and M. G. Merayo and M. Núñez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Using fuzzy automata to diagnose and predict heart problems}, year={2017}, editor = {Jose A. Lozano}, pages = {846--853}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper we introduce a formalism to specify the behavior of biological systems. Our formalism copes with uncertainty, via fuzzy logic constraints, an important characteristic of these systems. We present the formal syntax and semantics of our variant of fuzzy automata. The bulk of the paper is devoted to present an application of our formalism: a formal specification of the heart that can help to detect abnormal patterns of behavior. Specifically, our model analyzes the heartbeats per minute and the longitude of the RR waves of a patient. The model takes into account the age and gender of the patient, where age is considered to be a fuzzy parameter. Finally, we use real data to analyze the reliability of the model concerning the diagnosis and prediction of potential illnesses.}, keywords = {automata theory, cardiology, formal specification, fuzzy logic, fuzzy set theory, medical computing, patient diagnosis, biological systems, fuzzy automata, fuzzy logic constraints, heart problem diagnosis, heart problem prediction, Analytical models, Automata, Biological system modeling, Data models, Electrocardiography, Heart, formal methods, fuzzy systems, modelling and simulation of biological systems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969397}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969397}}, } @INPROCEEDINGS{price:2017:CEC, author={S. R. Price and D. T. Anderson}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic prOgramming for image feature descriptor learning}, year={2017}, editor = {Jose A. Lozano}, pages = {854--860}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={It is widely accepted that feature extraction is quite possibly the most critical step in computer vision. Typically, feature extraction is performed using a method such as the histogram of oriented gradients. In recent years, a shift has occurred from human to machine learned features, e.g., convolutional neural networks (CNNs) and Evolution-Constructed (ECO) features. An advantage of our improved ECO (iECO) framework is it optimizes features on a per-descriptor basis. Herein, iECO is extended in order to represent a richer class of features, namely arithmetic combinations and compositions of iECOs. This extension, called Genetic programming Optimal Feature Descriptor (GOOFeD) is based on genetic programming (GP). Three experiments are performed on data from a U.S. Army test site that contains multiple target and clutter types, burial depths, and times of day for automatic buried explosive hazard detection. The first two experiments focus on GOOFeD initialization and parameter selection. The last experiment demonstrates that GOOFeD is superior to iECO in terms of the fitness of evolved individuals.}, keywords = {genetic algorithms, genetic programming, computer vision, feature extraction, CNN, ECO features, GOOFeD initialization, GP, Genetic programming Optimal Feature Descriptor, automatic buried explosive hazard detection, burial depths, convolutional neural networks, evolution-constructed features, histogram of oriented gradients, iECO framework, image feature descriptor learning, parameter selection, Computers, Histograms, Transforms, feature learning, image processing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969398}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969398}}, } @INPROCEEDINGS{yang:2017:CEC, author={Y. Yang and Peng Li and S. Wang and B. Liu and Yongliang Luo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Scatter search for distributed assembly flowshop scheduling to minimize total tardiness}, year={2017}, editor = {Jose A. Lozano}, pages = {861--868}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The distributed assembly permutation flowshop problem (DAPFSP) is a typical NP-hard combinatorial optimization problem and represents an important area in multiple distributed production systems, where a series of jobs are to be processed on machines in one of specific factories (processing stage) and then assembled into final products by a single machine (assembly stage). In this study, a class of complex DAPFSP with respect to minimizing the total tardiness is investigated by considering real-practice constraints, including no-wait, no-idle and due dates, labeled as DAPFSP-T hereafter. And an effective scatter search based memetic algorithm (SS-MA) is proposed to address the difficulties of proposed DAPFSP-T model. Specifically, in the proposed SS-MA, a few compositive heuristics are proposed by comprehensively deploying several well-known constructive heuristics, and serve as the initialization method. Subset generation mechanism and solution combination methods are deliberatively designed to implement the global coarse exploration. Meanwhile, the improvement procedure in conventional SS is incarnated by a three-stage simulated annealing (3SSA) with three kinds of neighborhood structures to perform the local fine exploitation. Through the above sophisticated combination of multiple operators, it is expected in the proposed SS-MA the balance between global and local search abilities could be well achieved. And it is demonstrated by our experimental studies and comparisons that the proposed SS-MA could yield satisfactory searching performances, where solutions are significantly improved compared with multiple heuristic methods.}, keywords = {assembling, combinatorial mathematics, computational complexity, flow shop scheduling, job shop scheduling, search problems, simulated annealing, single machine scheduling, 3SSA, NP-hard combinatorial optimization problem, SS-MA, distributed assembly permutation flowshop problem DAPFSP, due dates, machine assembly, machine jobs, multiple distributed production systems, no-idle, no-wait, scatter search based memetic algorithm, solution combination, subset generation mechanism, three-stage simulated annealing, total tardiness minimization, Memetics, Production facilities, Schedules, distributed assembly permutation flowshop (DAPFSP), scatter search (SS), total tardiness}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969399}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969399}}, } @INPROCEEDINGS{liu:2017:CECb, author={Fangqing Liu and Han Huang and Zhifeng Hao}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary algorithm with convergence speed controller for automated software test data generation problem}, year={2017}, editor = {Jose A. Lozano}, pages = {869--875}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Software testing is an important process of software development. One of the challenges in testing software is to generate test cases which help to reveal errors. Automated software test data generation problem is hard because it needs to search the whole feasible area to find test cases covering all possible paths under acceptable time consumption. In this paper, evolutionary algorithm with convergence speed controller (EA-CSC) is presented for using the least test case overhead in solving automated test case generation problem. EA-CSC is designed as a framework which have fast convergence speed and capability to jump out of the local optimal solution over a range of problems. There are two critical steps in EA-CSC. The adaptive step size searching method accelerates the convergence speed of EA. The mutation operator can disrupt the population distribution and slows down the convergence process of EA. Moreover, the EA-CSC results are compared to the algorithms tested on the same benchmark problems, showing strong competitive.}, keywords = {evolutionary computation, program testing, search problems, EA-CSC, adaptive step size searching method, automated software test data generation problem, convergence speed controller, evolutionary algorithm, least test case overhead, mutation operator, population distribution, software development, software testing, test cases generation, Convergence, Sociology, Software, Statistics, Testing, Convergence Speed Controller(CSC), Evolutionary Algorithm(EA), Test Data Generation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969400}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969400}}, } @INPROCEEDINGS{kessentini:2017:CEC, author={S. Kessentini and D. Barchiesi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Convergence criteria for the particle swarm optimization in a full iterative process}, year={2017}, editor = {Jose A. Lozano}, pages = {876--881}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Although the theoretical aspects of the particle swarm optimization (PSO) seem to be forsaken, the few previous modeling studies -even with some assumptions- enlarged our knowledge of the PSO process. Here, we suggest a new model of PSO where all the N particles of the swarm and their components are considered. The iterative process is formulated by a 3N×3N block triangular matrix and its spectral radius is evaluated and displayed. Besides, the convergence related parametrization criteria are derived. Compared to previous results, a more restrictive acceleration coefficients criterion is found. Simulations are then carried out on CEC 2017 benchmark functions using eight PSO variants and show better results when considering the more restrictive criterion.}, keywords = {convergence, iterative methods, matrix algebra, particle swarm optimisation, PSO, convergence criteria, iterative process, particle swarm optimization, triangular matrix, Acceleration, Benchmark testing, Computational modeling, Eigenvalues and eigenfunctions, Manganese, Mathematical model}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969401}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969401}}, } @INPROCEEDINGS{nguyen:2017:CECa, author={S. Nguyen and M. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A PSO-based hyper-heuristic for evolving dispatching rules in job shop scheduling}, year={2017}, editor = {Jose A. Lozano}, pages = {882--889}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Automated heuristic design for job shop scheduling has been an interesting and challenging research topic in the last decade. Various machine learning and optimising techniques, usually referred to as hyper-heuristics, have been applied to facilitate the design task. Two main approaches are either to utilise a general structure for dispatching rules and optimise its parameters or to simultaneously search for suitable structures and their parameters. Each approach has its own advantages and disadvantages. In this paper, we focus on the first approach and develop new representations that are flexible enough to represent diverse rules and powerful enough to cope with complex shop conditions. Particle swarm optimisation is used in the proposed hyper-heuristic to find optimal rules based on the representations. The results suggest that the new representations are effective for different shop conditions and obtained rules are very competitive as compared to those evolved by genetic programming. Analyses also show that the proposed hyper-heuristic is significantly faster than genetic programming based hyper-heuristic.}, keywords = {genetic algorithms, genetic programming, dispatching, job shop scheduling, particle swarm optimisation, PSO-based hyperheuristic, automated heuristic design, dispatching rules, machine learning, optimising techniques, Neural networks, Optimization methods, Particle swarm optimization, Processor scheduling, evolutionary design, hyper-heuristic, scheduling}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969402}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969402}}, } @INPROCEEDINGS{zhou:2017:CECa, author={L. Zhou and L. Feng and A. Gupta and Y. S. Ong and K. Liu and C. Chen and E. Sha and B. Yang and B. W. Yan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving dynamic vehicle routing problem via evolutionary search with learning capability}, year={2017}, editor = {Jose A. Lozano}, pages = {890--896}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.}, keywords = {evolutionary computation, learning (artificial intelligence), search problems, vehicle routing, DVRP, customer-vehicle assignment, dynamic customer requests scheduling, dynamic vehicle routing problem, evolutionary search, learning capability, metaheuristic methods, optimized routing solution, Dynamic scheduling, Heuristic algorithms, Routing, Schedules, Vehicle dynamics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969403}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969403}}, } @INPROCEEDINGS{georgoulakos:2017:CEC, author={K. Georgoulakos and K. Vergidis and G. Tsakalidis and N. Samaras}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Multi-Objective Optimization of business process designs with pre-processing}, year={2017}, editor = {Jose A. Lozano}, pages = {897--904}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper discusses the problem of business process optimization within a multi-objective evolutionary framework. Business process optimization (BPO) is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The proposed approach involves a pre-processing stage and the application of a series of Evolutionary Multi-Objective Optimization Algorithms (EMOAs) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The work presented in this paper is aimed to investigate the benefits that come from the utilization of a pre-processing stage in the execution process of the EMOAs. The experimental results demonstrate that the proposed optimization framework is capable of producing a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility. The addition of the pre-processing stage appears to have a positive effect on the framework by producing more non-dominated solutions in reduced time frames.}, keywords = {business data processing, evolutionary computation, optimisation, BPO, EMOA, business process designs, business process optimization problem, cost, duration, evolutionary multiobjective optimization algorithms, optimum attribute values, preprocessing stage, process attributes, Business, Informatics, Mathematical model, Optimization, Principal component analysis, Process design, Transmission line measurements, business process, evolutionary algorithms, pre-processing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969404}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969404}}, } @INPROCEEDINGS{izumiya:2017:CEC, author={K. Izumiya and M. Munetomo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective Evolutionary optimization based on Decomposition with Linkage Identification considering monotonicity}, year={2017}, editor = {Jose A. Lozano}, pages = {905--912}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We propose a Decomposition-based Multi-objective Evolutionary Algorithm (MOEA/D) that incorporates a linkage identification technique to enhance the ability to solve difficult multi-objective optimization problems that have complex interactions among genes. Ensuring tight linkages is essential for genetic recombination operators to work effectively by preserving building blocks. For problems which are difficult to ensure tight linkages in encoding, the dependencies among loci have to be analyzed to identify the linkages for each building block. The proposed MOEA/D employs Linkage Identification with non-Monotonicity Detection (LIMD) to identify the linkages among pairs of loci by checking the non-monotonicity of fitness differences caused by pairwise perturbations for each scalar function in the MOEA/D. The results of numerical experiments conducted using a difficult multi-objective test function in which each building block is loosely encoded over the strings indicate that the proposed MOEA/D-LIMD outperforms the original MOEA/D and MOEA/D with tree-based graphical model (MOEA/D-GM).}, keywords = {evolutionary computation, identification, MOEA/D-LIMD, building blocks, complex interactions, decomposition-based multiobjective evolutionary optimization, encoding, fitness differences, genetic recombination operators, linkage identification with nonmonotonicity detection, monotonicity, pairwise perturbations, scalar function, Couplings, Linear programming, Optimization, Search problems, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969405}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969405}}, } @INPROCEEDINGS{li:2017:CECe, author={Zhenhua Li and Qingfu Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An efficient rank-1 update for Cholesky CMA-ES using auxiliary evolution path}, year={2017}, editor = {Jose A. Lozano}, pages = {913--920}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Covariance matrix adaptation evolution strategies (CMA-ES) is a powerful optimizer. In this paper, we propose an efficient rank-1 update for the Cholesky covariance matrix adaptation evolution strategy (Cholesky CMA-ES) using an auxiliary evolution path. It accumulates the average mutation vector corresponding to the current search direction, which is used to update the evolution path. It is used to update the Cholesky factor. It avoids to maintain the additional inverse Cholesky factor, and reduces the computational complexity in the update procedure to a half. Further, we experimentally show that the auxiliary evolution path approximates to the inverse vector of the evolution path in terms of inverse Cholesky factor in the procedure. We experimentally show that the proposed method achieves comparative or even better performances on the test problems.}, keywords = {computational complexity, evolutionary computation, matrix algebra, search problems, vectors, Cholesky CMA-ES, Cholesky covariance matrix adaptation evolution strategy, auxiliary evolution path, average mutation vector, inverse Cholesky factor, optimization, rank-1 update, search direction, Covariance matrices, Current distribution, Linear programming, Matrix decomposition}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969406}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969406}}, } @INPROCEEDINGS{feng:2017:CEC, author={L. Feng and W. Zhou and L. Zhou and S. W. Jiang and J. H. Zhong and B. S. Da and Z. X. Zhu and Y. Wang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An empirical study of multifactorial PSO and multifactorial DE}, year={2017}, editor = {Jose A. Lozano}, pages = {921--928}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting the latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge between them. In [1], the efficacy of MFO has been studied by a specific mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. Here we further explore the generality of MFO when diverse population based search mechanisms are employed. In particular, in this paper, we present the first attempt to conduct MFO with the popular particle swarm optimization and differential evolution search. Two specific multi-tasking paradigms, namely multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE) are proposed. To evaluate the performance of MFPSO and MFDE, comprehensive empirical studies on 9 single objective MFO benchmark problems are provided.}, keywords = {convergence, evolutionary computation, particle swarm optimisation, search problems, MFDE, MFPSO, chromosomal crossover, convergence characteristics, differential evolution search, diverse population based search mechanisms, empirical study, evolutionary multitasking, implicit genetic transfer, knowledge transfer, latent synergies, multifactorial DE, multifactorial PSO, multifactorial differential evolution, multifactorial optimization, multifactorial particle swarm optimization, particle swarm optimization, single objective MFO benchmark problems, unified representation space, Cultural differences, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969407}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969407}}, } @INPROCEEDINGS{wei:2017:CEC, author={Hao Wei and J. Timmis and R. Alexander}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving test environments to identify faults in swarm robotics algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {929--935}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications.}, keywords = {genetic algorithms, multi-robot systems, search problems, swarm intelligence, automated unintended behavior identification, evolving test environment, fault identification, flocking type algorithm, search based approach, simulated robotic swarm, swarm robotics algorithms, Algorithm design and analysis, Biological cells, Measurement, Robots, Taxonomy, Testing, genetic testing method, swarm robotics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969408}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969408}}, } @INPROCEEDINGS{bittermann:2017:CEC, author={M. S. Bittermann and Ö. Ciftcioglu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Situated color aesthetics by evolutionary computation}, year={2017}, editor = {Jose A. Lozano}, pages = {936--943}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In a previous publication by the same authors, a computational design system was described that identifies aesthetical color combinations. In that work the type of color aesthetics pursued was to be determined prior to the design representing a given design objective. Complementing the previous study, in this work, the type of color aesthetics that is most suitable for a given scene at hand is pursued taking into account the color and geometry of an existing situation. This is accomplished by bringing the parameter that characterizes the type of aesthetics into computational design, i.e. treating it as one of the components of the decision variable vector subject to identification by multi-objective evolutionary search. The parameter's influence on aesthetics is investigated theoretically, as well as by means of computer experiments. The contribution of the study to Architecture is provision of a firm base for some common architectural knowledge as to the color aesthetics of buildings. In particular light is shed on the aesthetical dependence of a building's color to the color of its environment.}, keywords = {architecture, buildings (structures), colour graphics, computational geometry, evolutionary computation, aesthetical color combinations, building color aesthetics, computational design system, environment color, multiobjective evolutionary search, Buildings, Color, Computational modeling, Computer architecture, Image color analysis, Mathematical model, Visual systems, Pareto optimal front, aesthetics, architectural, design, fuzzy neural tree, visual perception}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969409}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969409}}, } @INPROCEEDINGS{ashlock:2017:CEC, author={D. Ashlock and A. McEachern}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary design of FRAX decks}, year={2017}, editor = {Jose A. Lozano}, pages = {944--951}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A deck-based game is a game derived from a mathematical game by placing instances of its moves on a deck of cards. Initial work on deck-based games demonstrated that imposing the deck formalism can completely change the nature of the game. In this study, an evolutionary algorithm is used to design decks for a deck-based version of John Nash's classic game divide-the-dollar. The deck-based game is called FRAX and is used for helping students learn the arithmetic of fractions. The game includes a version that uses fraction multiplication, but focuses on addition since this is the more difficult of the basic operations with fractions. The deck design algorithm uses a novel evolutionary strategy called the horde of dumb agents technique to compare and evaluate different decks. Evolution, within the horde of dumb agents strategy, is a uniquely valuable tool for understanding how naive players might approach a new game. It is shown that the techniques presented in this study are able to obtain useful information about decks and new design principles for FRAX decks are discovered. These discoveries include an heuristic for deck difficulty based on strategically matching and non-matching subsets of the cards.}, keywords = {arithmetic, education, evolutionary computation, game theory, FRAX decks, John Nash classic game divide-the-dollar, card subset matching, deck design algorithm, deck formalism, deck-based game, dumb agents technique, evolutionary algorithm, evolutionary design, evolutionary strategy, fraction arithmetic, fraction multiplication, mathematical game, Algorithm design and analysis, Games, Sociology, Statistics, Tools}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969410}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969410}}, } @INPROCEEDINGS{zille:2017:CEC, author={H. Zille and A. Kottenhahn and S. Mostaghim}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Dynamic Distance Minimization Problems for dynamic multi-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {952--959}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this article we propose a new dynamic multi-objective optimization problem. This dynamic Distance Minimization Problem (dDMP) functions as a benchmark problem for dynamic multi-objective optimization and is based on the static versions from the literature. The dDMP introduces a useful property and challenge for dynamic multi-objective algorithms. Not only the positions of the Pareto-optimal solutions in the search space change over time, but also the complexity of the problem can be adjusted dynamically. In addition the problem is based on a simple geometric structure, which makes it useful to visualize the search behaviour of algorithms. We describe the basic principles of the problem, and introduce the possible dynamic changes and their implementation and effects of the Pareto-optimal areas. Our experiments show how a possible instance of the dynamic DMP can be defined and how different algorithms react to the dynamic changes.}, keywords = {minimisation, Pareto-optimal solutions, dDMP, dynamic distance minimization problems, dynamic multiobjective optimization, simple geometric structure, Benchmark testing, Euclidean distance, Heuristic algorithms, Minimization, Optimization, Search problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969411}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969411}}, } @INPROCEEDINGS{situ:2017:CEC, author={Xin Situ and W. N. Chen and Y. J. Gong and Ying Lin and Wei-Jie Yu and Zhiwen Yu and J. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A parallel Ant Colony System based on region decomposition for Taxi-Passenger Matching}, year={2017}, editor = {Jose A. Lozano}, pages = {960--967}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Taxi dispatch is a critical issue for taxi company to consider in modern life. This paper formulates the problem into a taxi-passenger matching model and proposes a parallel ant colony optimization algorithm to optimize the model. As the search space is large, we develop a region-dependent decomposition strategy to divide and conquer the problem. To keep the global performance, a critical region is defined to deal with the communications and interactions between the subregions. The experimental results verify that the proposed algorithm is effective, efficient, and extensible, which outperforms the traditional global perspective greedy algorithm in terms of both accuracy and efficiency.}, keywords = {ant colony optimisation, dispatching, divide and conquer methods, public transport, search problems, critical region, divide and conquer, global perspective greedy algorithm, parallel ant colony optimization algorithm, parallel ant colony system, region-dependent decomposition strategy, search space, taxi company, taxi dispatch, taxi-passenger matching, Ant colony optimization, Companies, Global Positioning System, Optimization, Public transportation, Sun, Vehicles, Taxi-Passenger Matching (TPM), ant colony system (ACS), region-dependent decomposition (RDD)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969412}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969412}}, } @INPROCEEDINGS{lima:2017:CECa, author={M. P. Lima and R. F. Alexandre and R. H. C. Takahashi and E. G. Carrano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A comparative study of Multiobjective Evolutionary Algorithms for Wireless Local Area Network design}, year={2017}, editor = {Jose A. Lozano}, pages = {968--975}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This manuscript presents a comparative study between three Multiobjective Evolutionary Algorithms (NSGA-II, GDE3, and MOEA/D-DE) on Wireless Local Area Networks design. The considered problem consists on defining the positions, quantity, channels, and load balance of access points to be installed. Problem features such as equipment limitations, traffic demand, and minimum coverage level required are modeled as constraints. The used algorithms were tested in two scenarios, considering different network profiles. The results show that the developed approach for WLAN planning can help a network designer to define good Wi-Fi projects, improving the signal level, network balance, and reducing interference.}, keywords = {evolutionary computation, wireless LAN, GDE3, MOEA/D-DE, NSGA-II, WLAN planning, Wi-Fi projects, load balance, multiobjective evolutionary algorithms, wireless local area network design, Channel allocation, Interference, Mathematical model, Optimization, Signal to noise ratio, Wireless fidelity}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969413}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969413}}, } @INPROCEEDINGS{zhang:2017:CECa, author={Hanwei Zhang and A. Zhou and G. Zhang and H. K. Singh}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Accelerating MOEA/D by Nelder-Mead method}, year={2017}, editor = {Jose A. Lozano}, pages = {976--983}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The multiobjective evolutionary algorithm based on decomposition (MOEA/D) converts a multiobjective optimization problem into a set of single-objective subproblems, and tackles them simultaneously. In MOEA/D, the offspring generation is a crucial part to increase the convergence of the algorithm and maintain the diversity of the solution set. Currently, the majority of reproduction operators consider the quality of neighborhood exploration, i.e., the capability to distribute along the population structure, while few operators have good capability for subproblem exploitation, i.e., the ability to push solutions forward along the subproblems. To address this issue in this paper, we introduce one of the derivative-free optimization methods, Nelder-Mead simplex (NMS) method, to MOEA/D to accelerate the algorithm convergence. The NMS operator is combined with a differential evolution (DE) operator in the offspring generation. The comparison study demonstrates that calling the NMS operator occasionally can help to accelerate the convergence.}, keywords = {convergence, evolutionary computation, optimisation, DE operator, MOEA/D acceleration, NMS method, NMS operator, Nelder-Mead method, algorithm convergence, derivative-free optimization methods, differential evolution operator, multiobjective evolutionary algorithm based on decomposition, multiobjective optimization problem, neighborhood exploration quality, offspring generation, reproduction operators, single-objective subproblems, subproblem exploitation, Acceleration, Optimization methods, Sociology, Statistics, Evolutionary multiobjective optimization, MOEA/D, Nelder-Mead simplex method}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969414}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969414}}, } @INPROCEEDINGS{rakshit:2017:CEC, author={P. Rakshit and A. Konar and A. K. Nagar}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning automata induced artificial bee colony for noisy optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {984--991}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We propose two extensions of the traditional artificial bee colony algorithm to proficiently optimize noisy fitness. The first strategy is referred to as stochastic learning automata induced adaptive sampling. It is employed with an aim to judiciously select the sample size for the periodic fitness evaluation of a trial solution, based on the fitness variance in its local neighborhood. The local neighborhood fitness variance is here used to capture the noise distribution in the local surrounding of a candidate solution of the noisy optimization problem. The second strategy is concerned with determining the effective fitness estimate of a trial solution using the distribution of its noisy fitness samples, instead of direct averaging of the samples. Computer simulations undertaken on the noisy versions of a set of 28 benchmark functions reveal that the proposed algorithm outperforms its contenders with respect to function error value in a statistically significant manner.}, keywords = {learning automata, optimisation, stochastic processes, adaptive sampling, artificial bee colony, local neighborhood fitness variance, noisy fitness, noisy optimization, stochastic learning automata, Benchmark testing, Learning (artificial intelligence), Linear programming, Noise measurement, Optimization, Pollution measurement, noise-handling, probability distribution function, weighted averaging}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969415}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969415}}, } @INPROCEEDINGS{duan:2017:CEC, author={W. Duan and Z. Li and Y. Yang and B. Liu and Keyao Wang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={EDA based probabilistic Memetic Algorithm for distributed blocking permutation flowshop scheduling with sequence dependent setup time}, year={2017}, editor = {Jose A. Lozano}, pages = {992--999}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Distributed permutation flowshop scheduling problem (DPFSP) is a typical NP-hard combinatorial optimization problem and represents an important area in multiple distributed production systems. In this study, both machine blocking and job sequence dependent setup time constraints are considered in DPFSP, which make the conventional model more suitable to the realistic situation. The combination with new constraints means a substantial increase in the complexity of the problem and the volatility of landscape, which sharply increase the solving difficulty. Probabilistic memetic framework (PrMF) is a novel MA framework which balances the exploration and exploitation by controlling the learning intensity of each individual. In this paper, an EDA-based PrMF algorithm, called EDAPrMF, is proposed to solve the DBPFSP with SDST, in which PrMF is modified and extended for distributed scheduling problems with two-layer encoding. Specifically, a novel solution matrix based distance matrix is defined for DBPFSP with SDST, which serves as a suitable measure between two feasible solutions and can also be connected with the probability matrix of EDA, the global search meme in PrMF. Meta-Lamarckian learning strategy is also equipped in PrMF to guide the local search direction. The experimental results and comparisons with existing algorithms show the efficiency of the proposed EDAPrMF in solving both small-scale and large-scale DBPFSP with SDSP and the effectiveness of PrMF in improve the search ability.}, keywords = {combinatorial mathematics, computational complexity, flow shop scheduling, matrix algebra, optimisation, probability, search problems, DBPFSP, DPFSP, EDA based probabilistic memetic algorithm, EDA probability matrix, EDA-based PrMF algorithm, EDAPrMF, NP-hard combinatorial optimization problem, SDST, distributed blocking permutation flowshop scheduling, global search meme, job sequence dependent setup time constraints, local search, machine blocking, matrix based distance matrix, metaLamarckian learning strategy, multiple distributed production systems, two-layer encoding, Job shop scheduling, Measurement, Memetics, Optimization, Production facilities, algorithm, distributed blocking permutation flowshop scheduling problem, distributed permutation flowshop scheduling problem, estimation distribution algorithm, probabilistic memetic, sequence dependent setup time}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969416}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969416}}, } @INPROCEEDINGS{brglez:2017:CEC, author={F. Brglez and B. Bošković and J. Brest}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On asymptotic complexity of the optimum Golomb ruler problem: From established stochastic methods to self-avoiding walks}, year={2017}, editor = {Jose A. Lozano}, pages = {1000--1007}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Optimum or near-optimum solutions of the Golomb ruler (gr) problem have applications in information theory, error correction, current transformers, radio frequency selection, placement of antenna arrays in astronomy, among others. In mathematics, there is a well-defined relationship of Golomb rulers and graceful graphs. A massively parallel computing project on gr has been on-going for more than 10 years: the order-24 ruler was claimed as optimum in November 2004 (after a 4-year computational effort), followed by order-25, -26, -27 rulers in 2008, 2009, 2014. The order-28 ruler is work-in-progress. The distribution of waiting times, in years, such as {4, 4, 1, 5} may be impossible to predict under a variable number of processors running simultaneously. This paper proposes a model to experimentally predict the asymptotic runtime complexity of any gr solver that returns the best-known-value (BKV) Golomb ruler defined by the paired list (L = length, M = order). A subset of this list includes {(6,4), (11,5), (17,6), (25,7), (34,8), (44,9),..., (680,30)}. Given the number of processors N and the runtime limit t_lmt , we observe at least N_u ≥ 100 processors reaching the target BKV with the first-passage-time <; t_lmt and say that each such observation is uncensored. In other words, the mean runtime value we measure is based strictly on at least 100 uncensored observations from the experiment. Experiments in this paper focus on a stochastic gr-solver that implements a variation of a self-avoiding walk. In the total number of steps, the solver asymptotic walkLength complexity is 409.2 × 1.0762^L . When measuring the number of CPU seconds on a loaded grid of 100 processors, the solver asymptotic runtime complexity is 0.000206 × 1.08711^L . This solver significantly outperforms the alternative stochastic gr-solvers reported to date.}, keywords = {computational complexity, information theory, mathematics computing, parallel processing, BKV, CPU seconds, antenna arrays, astronomy, asymptotic runtime complexity, asymptotic walkLength complexity, best-known-value Golomb ruler, current transformers, error correction, massively parallel computing project, near-optimum solutions, optimum Golomb ruler problem, radio frequency selection, self-avoiding walk, self-avoiding walks, stochastic gr-solver, stochastic methods, Complexity theory, Electronic publishing, Encyclopedias, Internet, Program processors, Runtime}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969417}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969417}}, } @INPROCEEDINGS{yufka:2017:CEC, author={M. Yufka and B. Ekici and C. Cubukcuoglu and I. Chatzikonstantinou and I. S. Sariyildiz}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Objective skylight optimization for a healthcare facility foyer space}, year={2017}, editor = {Jose A. Lozano}, pages = {1008--1014}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically, a foyer space has been considered as a gathering area that answers people's needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason, objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results, jE_DEMO algorithm presents satisfactory solutions as well as NSGA-II.}, keywords = {architecture, genetic algorithms, health care, lighting, minimisation, architectural design field, architectural perspective, computational intelligence, daylight performance maximization, healthcare facility foyer space, interior space, jE_DEMO algorithm, multiobjective self-adaptive ensemble differential evolution algorithm, multiobjective skylight optimization, nondominated sorting genetic algorithm-II, objective functions, skylight cost minimization, skylight design, Buildings, Glass, Medical services, Optimization, Sociology, Statistics, computational design, multi-objective optimization, performance based design}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969418}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969418}}, } @INPROCEEDINGS{pintea:2017:CEC, author={C. M. Pintea and S. A. Ludwig and P. C. Pop and O. Matei}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Similarities and sensitivity: Immune and ant algorithms applied towards robotics}, year={2017}, editor = {Jose A. Lozano}, pages = {1015--1020}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Over the last five and a half decades, the focus of mainstream artificial intelligence was on creating computers and algorithms that display some human cognitive abilities. Over time, bio-inspired artificial intelligence has shown great success. The ideas of bio-inspired artificial intelligence are taken from biological systems and applied to solve artificial intelligence problems. The future robots and computational devices will have diverse artificial systems including immune systems. The current paper studies the similarities between Ant-based algorithms and Artificial Immune Systems and their further steps in the development of robots. We study the sensitive approaches and several related robotic applications solved by means of both presented algorithms.}, keywords = {artificial immune systems, ant-based algorithms, bio-inspired artificial intelligence, Artificial intelligence, Heuristic algorithms, Immune system, Robot sensing systems, Sensitivity, Signal processing algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969419}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969419}}, } @INPROCEEDINGS{karaman:2017:CEC, author={S. Karaman and B. Ekici and C. Cubukcuoglu and B. K. Koyunbaba and I. Kahraman}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Design of rectangular fa #x00E7;ade modules through computational intelligence}, year={2017}, editor = {Jose A. Lozano}, pages = {1021--1028}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents an implementation of multiobjective optimization for a rectangular façade design proposal in a healthcare building's common space. Objectives are to maximize daylight performance and to minimize façade construction cost. The aim of this study is to enhance indoor comfort of an existing healthcare building by concerning cost-effective façade design alternatives subject to several constraints. To handle the problem, we formulate a multi-objective real-parameter constraint problem. In order to solve this, Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Self-Adaptive Ensemble Differential Evolution (jE_DEMO) algorithms are used. Finally, both algorithms are capable to discover desirable set of design alternatives.}, keywords = {architecture, buildings (structures), cost reduction, daylighting, design, genetic algorithms, health care, NSGA-II, computational intelligence, cost-effective façade design alternatives, daylight performance maximization, façade construction cost minimization, healthcare building common space, indoor comfort enhancement, jE_DEMO algorithms, multiobjective optimization, multiobjective real-parameter constraint problem, multiobjective self-adaptive ensemble differential evolution, nondominated sorting genetic algorithm II, rectangular façade module design, Buildings, Evolutionary computation, Glass, Lighting, Mathematical model, Medical services, Optimization, Healthcare building, differential evolution, façade design, multi-objective optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969420}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969420}}, } @INPROCEEDINGS{tahmassebi:2017:CEC, author={A. Tahmassebi and A. H. Gandomi and I. McCann and M. H. Schulte and L. Schmaal and A. E. Goudriaan and A. Meyer-Baese}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary approach for fMRI big data classification}, year={2017}, editor = {Jose A. Lozano}, pages = {1029--1036}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40% of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74%, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.}, keywords = {genetic algorithms, genetic programming, Big Data, biomedical MRI, brain, data reduction, drugs, image classification, learning (artificial intelligence), medical image processing, patient treatment, principal component analysis, N-acetylcysteine drug, brain image slices, data reduction algorithm, evolutionary approach, fMRI big data classification, fMRI images, function magnetic resonance imaging, genetic programming classifier, image masking, machine learning, nicotine-dependent patients, placebo drug, relapse classification, smoking cessation treatment, Blood, Correlation, Feature extraction, Machine learning algorithms, Magnetic resonance imaging}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969421}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969421}}, } @INPROCEEDINGS{verma:2017:CEC, author={S. Verma and P. Hadjidoukas and P. Wirth and P. Koumoutsakos}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective optimization of artificial swimmers}, year={2017}, editor = {Jose A. Lozano}, pages = {1037--1046}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A fundamental understanding of how various biological traits and features provide organisms with a competitive advantage can help us improve the design of several mechanical systems. Numerical optimization can be invaluable for this purpose, by allowing us to scrutinize the evolution of specific biological adaptations. Importantly, the use of numerical optimization can help us overcome limiting constraints that restrict the evolutionary capability of biological species. Thus, we couple high-fidelity simulations of self-propelled swimmers with evolutionary optimization algorithms, to examine peculiar swimming patterns observed in a number of fish species. More specifically, we investigate the intermittent form of locomotion referred to as `burst-and-coast' swimming, which involves a few quick flicks of the fish's tail followed by a prolonged unpowered glide. This mode of swimming is believed to confer energetic benefits, in addition to several other advantages. We discover a range of intermittent-swimming patterns, the most efficient of which resembles the swimming-behaviour observed in live fish. We also discover patterns which lead to a marked increase in swimming-speed, albeit with a significant increase in energy expenditure. Notably, the use of multi-objective optimization reveals locomotion patterns that strike the perfect balance between speed and efficiency, which can be invaluable for use in robotic applications. The analyses presented may also be extended for optimal design and control of airborne vehicles. As an additional goal of the paper, we highlight the ease with which disparate codes can be coupled via the software framework used, without encumbering the user with the details of efficient parallelization.}, keywords = {autonomous underwater vehicles, biomimetics, evolutionary computation, marine control, optimal control, optimisation, airborne vehicles, artificial swimmers, biological adaptations, biological traits, burst-and-coast swimming, evolutionary optimization algorithms, fish species, high-fidelity simulations, intermittent-swimming patterns, locomotion patterns, multiobjective optimization, numerical optimization, optimal design, pattern discovery, peculiar swimming patterns, robotic applications, self-propelled swimmers, software framework, unpowered glide, Computational modeling, Fish, Mathematical model, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969422}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969422}}, } @INPROCEEDINGS{li:2017:CECf, author={W. Li and E. Özcan and R. John and J. H. Drake and A. Neumann and M. Wagner}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A modified indicator-based evolutionary algorithm (mIBEA)}, year={2017}, editor = {Jose A. Lozano}, pages = {1047--1054}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations.}, keywords = {Pareto optimisation, evolutionary computation, DTLZ1-7, Pareto-dominance based component, benchmark functions, epsilon approximation values, hypervolume indicator values, mIBEA, modified indicator-based evolutionary algorithm, Benchmark testing, Computer science, Electronic mail, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969423}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969423}}, } @INPROCEEDINGS{jackson:2017:CEC, author={W. G. Jackson and E. Özcan and R. I. John}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Tuning a Simulated Annealing metaheuristic for cross-domain search}, year={2017}, editor = {Jose A. Lozano}, pages = {1055--1062}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Simulated Annealing is a well known local search metaheuristic used for solving computationally hard optimisation problems. Cross-domain search poses a higher level issue where a single solution method is used with minor, preferably no modification for solving characteristically different optimisation problems. The performance of a metaheuristic is often dependant on its initial parameter settings, hence detecting the best configuration, i.e. parameter tuning is crucial, which becomes a further challenge for cross-domain search. In this paper, we investigate the cross-domain search performance of Simulated Annealing via tuning for solving six problems, ranging from personnel scheduling to vehicle routing under a stochastic local search framework. The empirical results show that Simulated Annealing is extremely sensitive to the initial parameter settings leading to sub-standard performance when used as a single solution method for cross-domain search. Moreover, we demonstrate that cross-domain parameter tuning is inferior to domain-level tuning highlighting the requirements for adaptive parameter configurations when dealing with cross-domain search.}, keywords = {computational complexity, personnel, scheduling, search problems, simulated annealing, stochastic processes, vehicle routing, adaptive parameter configuration, computationally hard optimisation problem, cross-domain parameter tuning, cross-domain search performance, domain-level tuning, local search metaheuristic, personnel scheduling, simulated annealing metaheuristic tuning, stochastic local search framework, Cooling, Schedules, Tuning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969424}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969424}}, } @INPROCEEDINGS{latorre:2017:CEC, author={A. LaTorre and J. M. Peña}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A comparison of three large-scale global optimizers on the CEC 2017 single objective real parameter numerical optimization benchmark}, year={2017}, editor = {Jose A. Lozano}, pages = {1063--1070}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The scalability of optimization algorithms is an important issue that has been thoroughly studied in the past. However, these studies were normally conducted by gradually increasing the dimensionality of the benchmark and analyzing how an algorithm exhibiting a good performance on low-dimensional problems degrades as the problem size increases. In this contribution we follow the opposite approach: we take some well-known large-scale global optimizers based on the MOS framework and specifically designed for problems of thousands of variables and evaluate them on much smaller problems (up to 100 dimensions). The results show that, surprisingly, these algorithms are able to find good solutions to many of the functions of the benchmark, systematically reaching the global optimum for some of them. Furthermore, the differences in performance among the three considered algorithms are also analyzed and compared with statistical methods. Finally, several hypothesis are given to explain these differences in performance among the three algorithms.}, keywords = {optimisation, statistical analysis, MOS framework, large-scale global optimizers, low-dimensional problems, real parameter numerical optimization benchmark, statistical methods, Algorithm design and analysis, Benchmark testing, Complexity theory, Heuristic algorithms, Optimization, Scalability, Tuning, MOS-CEC2012, MOS-CEC2013, MOS-SOCO2011, Multiple Offspring Sampling, Single Objective Real Parameter Numerical Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969425}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969425}}, } @INPROCEEDINGS{latorre:2017:CECa, author={A. LaTorre and J. M. Peña}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On the scalability of population restart mechanisms on large-scale global optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1071--1078}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Population restart mechanisms are a popular method to avoid premature convergence in Evolutionary Algorithms. Many different methods have used these mechanisms in the past in different scenarios. However, most of these works tend to design an ad-hoc population restart approach for the problem under consideration. Furthermore, the effects of the alternative restart strategies and the scalability of the method are rarely analyzed in the literature. In this paper, we conduct a comparative study of 36 population restart strategies (37 if we account for the baseline of not restarting the population) on the SOCO 2011 benchmark, a testbed of 19 continuous scalable functions widely accepted in the continuous optimisation community and that allow an analysis at different problem sizes which is not possible with other existing benchmarks. The results obtained clearly show that there is a relationship between the particular strategy considered and the effectiveness of the method. Moreover, this effectiveness tends to decrease as the dimensionality (complexity) of the problem grows.}, keywords = {evolutionary computation, large-scale systems, optimisation, ad-hoc population restart approach, continuous optimisation, evolutionary algorithms, large-scale global optimization, Benchmark testing, Convergence, Heuristic algorithms, Iron, Optimization, Sociology, Statistics, Large Scale Global Optimization, MOS-CEC2013, Multiple Offspring Sampling, Population Restart}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969426}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969426}}, } @INPROCEEDINGS{montgomery:2017:CEC, author={J. Montgomery and D. Ashlock}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Applying the biased form of the adaptive generative representation}, year={2017}, editor = {Jose A. Lozano}, pages = {1079--1086}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This study is the second using real-coded representation for problems usually solved with a discrete coding. The adaptive generative representation is able to adapt itself on the fly to prior parts of the construction of an object as it assembles it. In the initial study the ability of the representation to take user supplied or problem supplied biases that change its behavior was demonstrated but not explored. In this study the bias is used to change the way evolution explores a fitness landscape for both an RFID antenna design problem and small instances of the traveling salesman problem. Addition of a bias to two different generative representations promotes the evolution of longer antenna designs (a heuristic objective associated with good antennas) while leading the algorithm to generate designs with distinctive shape characteristics. For the traveling salesman, a simple inverse-distance bias for the adaptive generative representation causes a large improvement in performance over a random key representation in 99 of 100 instances studied.}, keywords = {antennas, evolutionary computation, radiofrequency identification, travelling salesman problems, RFID antenna design problem, adaptive generative representation, discrete coding, fitness landscape, heuristic objective, inverse-distance bias, random key representation, shape characteristics, traveling salesman problem, Algorithm design and analysis, Dipole antennas, Encoding, Shape, Surface acoustic waves}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969427}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969427}}, } @INPROCEEDINGS{hamze:2017:CEC, author={N. Hamze and P. Collet and C. Essert}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary approaches for surgical path planning: A quantitative study on Deep Brain Stimulation}, year={2017}, editor = {Jose A. Lozano}, pages = {1087--1094}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Path planning for surgical tools in minimally invasive surgery is a multi-objective optimization problem consisting in searching the best compromise between multiple placement constraints to find an optimal insertion point for the tool. Many works have been proposed to automate the decision-making process. Most of them use an aggregative approach that transforms the problem into a mono-objective problem. However, despite its intuitiveness, this approach is known for its incapacity to find all optimal solutions. After a previous clinical study in which we pointed out the interest of introducing MOEAs to neurosurgery [12], in this work, we aim at maximizing the range of optimal solutions proposed to the surgeon. Our study compares three different optimization approaches: an aggregative method using a weighted sum of the multiple constraints, an evolutionary multi-objective method, and an exhaustive dominance-based method used as ground truth. For each approach, we extract the set of all optimal insertion points based on dominance rules, and analyze the common and differing solutions by comparing the surfaces they cover. The experiments have been performed on 30 images datasets from patients who underwent a Deep Brain Stimulation electrode implant in the brain. It can be observed that the areas covered by the optimal insertion points obtained by the three methods differ significantly. The obtained results show that the traditional weighted sum approach is not sufficient to find the totality of the optimal solutions. The Pareto-based approaches provide extra solutions, but neither of them could find the complete optimal solution space. Further works should investigate either hybrid or extended methods such as adaptive weighted sum, or hybrid visualization of the solutions in the GUI.}, keywords = {Pareto optimisation, brain, evolutionary computation, path planning, surgery, Pareto-based approaches, aggregative method, deep brain stimulation, electrode implant, evolutionary approaches, evolutionary multiobjective method, exhaustive dominance-based method, minimally invasive surgery, multiobjective optimization, optimal insertion points, placement constraints, surgical path planning, surgical tools, weighted sum, Optimization, Planning, Sociology, Statistics, Tools, Trajectory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969428}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969428}}, } @INPROCEEDINGS{oliveira:2017:CEC, author={M. Oliveira and B. Borguesan and M. Dorn}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={SADE-SPL: A Self-Adapting Differential Evolution algorithm with a loop Structure Pattern Library for the PSP problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1095--1102}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The knowledge about the conformation of a protein molecule allows the inference and study of its biological function. Because protein function is determined by its shape and the physio-chemical properties of its exposed surface, it is extremely important to predict accurate protein models. One of the hardest problems in Structural Bioinformatics is associated with the prediction of the three-dimensional structure of a protein only from its amino acid sequence (primary structure). Coils and turns are both elements of secondary structure in proteins where the polypeptide chain reverses its overall direction; These structures are considered the most difficult secondary structure to be predicted. In this paper, we propose a loop Structure Pattern Library (SPL) which was created using experimental information extracted from Protein Data Bank aiming to constrain the conformational search space of proteins. The Self-Adapting Differential Evolution (SADE) meta-heuristic was implemented for the tertiary protein structure prediction problem using the Structure Pattern Library as knowledge. The SADE algorithm was tested with (SADE-SPL) and without the Structure Pattern Library. Archived results show that the lowest Root Mean Square Deviation values were obtained when the Structure Pattern Library was employed. Average GDT TS were higher in all SADE-SPL cases. Thereby, our results allow us to state that SPL application knowledge in SADE meta-heuristic is capable of predicting three-dimensional protein structures closer to experimental structures than SADE application without SPL.}, keywords = {bioinformatics, evolutionary computation, proteins, search problems, 3D protein structures, PSP problem, Root Mean Square Deviation values, SADE algorithm, SADE application, SADE meta-heuristic, SADE-SPL, Self-Adapting Differential Evolution, amino acid sequence, biological function, coils, conformational search space, loop structure pattern library, polypeptide chain, protein data bank, protein function, protein models, protein molecule, secondary structure, self-adapting differential evolution algorithm, structural bioinformatics, tertiary protein structure prediction problem, Amino acids, Databases, Libraries, Prediction algorithms, Sociology, Three-dimensional displays}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969429}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969429}}, } @INPROCEEDINGS{sanhueza:2017:CEC, author={C. Sanhueza and F. Jiménez and R. Berretta and P. Moscato}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={PasMoQAP: A parallel asynchronous memetic algorithm for solving the Multi-Objective Quadratic Assignment Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1103--1110}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-Objective Optimization Problems (MOPs) have attracted growing attention during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have been extensively used to address MOPs because are able to approximate a set of non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP which has been extensively studied, and used in several real-life applications. The mQAP is defined as having as input several flows between the facilities which generate multiple cost functions that must be optimized simultaneously. In this study, we propose PASMOQAP, a parallel asynchronous memetic algorithm to solve the Multi-Objective Quadratic Assignment Problem. PASMOQAP is based on an island model that structures the population by creating subpopulations. The memetic algorithm on each island individually evolve a reduced population of solutions, and they asynchronously cooperate by sending selected solutions to the neighboring islands. The experimental results show that our approach significatively outperforms all the island-based variants of the multi-objective evolutionary algorithm NSGA-II. We show that PASMOQAP is a suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.}, keywords = {algorithm theory, genetic algorithms, MOEA, MOP, Multi-Objective Evolutionary Algorithms, Multi-Objective Optimization Problems, Multi-Objective Quadratic Assignment Problem, PASMOQAP, island model, mQAP, multi-objective evolutionary algorithm NSGA-II, parallel asynchronous memetic algorithm, Algorithm design and analysis, Evolutionary computation, Memetics, Optimization, Sociology, Statistics, Topology, Memetic Algorithms, Multi-Objective Optimization, Parallel Island Model}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969430}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969430}}, } @INPROCEEDINGS{corrêa:2017:CEC, author={L. de Lima Corrêa and M. Inostroza-Ponta and M. Dorn}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures}, year={2017}, editor = {Jose A. Lozano}, pages = {1111--1118}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Tertiary protein structure prediction in silico is one of the most challenging problems in Structural Bioinformatics. The challenge arises due to the combinatorial explosion of plausible shapes, where a long amino acid chain ends up in one out of a vast number of three-dimensional conformations. The rules that govern the biological process are partially known, which difficult the development of robust prediction methods. Many computational methods and strategies were proposed over the last decades. Nevertheless, the problem remains open. The agent-based paradigm has been shown a useful technique for the applications that have repetitive and time-consuming activities, knowledge share and management, such as the integration of different knowledge sources and modeling of complex biological systems. In this paper, we propose a first principle method with database information for the 3-D protein structure prediction problem. We do so by designing a multi-agent approach that uses concepts of evolutionary algorithms to speed up the search phase by improving local candidate solutions from the protein conformational space. To validate our method, we tested our computational strategy on a test bed of eight protein sequences. Predicted structures were analyzed regarding root-mean-square deviation, global distance total score test and secondary structure arrangement. The obtained results were topologically compatible with their correspondent experimental structures, thus corroborating the effectiveness of our proposed method. As observed, the evolutionary multi-agent approach achieved good results in terms of the evaluted measures and was able to efficiently search the roughness of protein energy landscape.}, keywords = {bioinformatics, evolutionary computation, mean square error methods, proteins, amino acid chain, biological systems, computational methods, evolutionary multiagent algorithm, root-mean-square deviation, selectivity, silico, structural bioinformatics, tertiary protein structure prediction, three-dimensional protein structures, Amino acids, Computational modeling, Databases, Multi-agent systems, Shape}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969431}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969431}}, } @INPROCEEDINGS{párraga-álava:2017:CEC, author={J. Párraga-Álava and M. Dorn and M. Inostroza-Ponta}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1119--1126}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Finding a solution to the Multi-Criteria Minimum Spanning Tree (mc-MST) problem has direct benefit on real world problems. The Multi-objective Evolutionary Algorithm (MOEA) called NSGA-II (Non-Dominated Sorting Genetic Algorithm) has demonstrated to be the most promising approach to tackle mc-MST problem because of their efficiency and simplicity of implementation. However, it often reaches premature convergence and gets stuck at local optima causing the non-diversity of the population. To tackle this situation, the use local search strategies together with MOEAs has shown to be a good alternative. In this paper, we investigate the potential of local search methods to improve the overall effectiveness of NSGA-II to settle the mc-MST problem. We evaluate the performance of three general purpose local searches (Pareto Local Search, Tabu Search and Path Relinking) adapted to the multi-objective approach. Experimental results show that using Pareto Local Search (PLS) into the NSGA-II offers a better performance in terms of diversity and search space covered to settle the mc-MST problem.}, keywords = {Pareto optimisation, genetic algorithms, operations research, search problems, sorting, trees (mathematics), MOEA, NSGA-II, Pareto local search, general purpose local searches, local search strategies, mc-MST problem, multicriteria minimum spanning tree problem, multiobjective evolutionary algorithm, nondominated sorting genetic algorithm, path relinking, tabu search, Biological cells, Encoding, Evolutionary computation, Optimization, Sociology, Statistics, Multi-criteria minimum spanning tree, comparative study, multi-objective optimization problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969432}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969432}}, } @INPROCEEDINGS{tanabe:2017:CEC, author={R. Tanabe and A. Oyama}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A note on constrained multi-objective optimization benchmark problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1127--1134}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We investigate the properties of widely used constrained multi-objective optimization benchmark problems. A number of Multi-Objective Evolutionary Algorithms (MOEAs) for Constrained Multi-Objective Optimization Problems (CMOPs) have been proposed in the past few years. The C-DTLZ functions and Real-World-Like Problems (RWLPs) have frequently been used for evaluating the performance of MOEAs on CMOPs. In this paper, however, we show that the C-DTLZ functions and widely-used RWLPs have some unnatural problem features. The experimental results show that an MOEA without any Constraint Handling Techniques (CHTs) can successfully find well-approximated nondominated feasible solutions on the C1-DTLZ1, C1-DTLZ3, and C2-DTLZ2 functions. It is widely believed that RWLPs are MOEA-hard problems, and finding the feasible solutions on them is a very hard task. However, we show that the MOEA without any CHTs can find feasible solutions on widely-used RWLPs such as the speed reducer design problem, the two-bar truss design problem, and the water problem. Also, it is seldom that the infeasible solution simultaneously violates multiple constraints in the RWLPs. Due to the above reasons, we conclude that constrained multi-objective optimization benchmark problems need a careful reconsideration.}, keywords = {constraint handling, evolutionary computation, C-DTLZ functions, CHT, CMOP, MOEA, RWLP, constrained multiobjective optimization benchmark problems, constrained multiobjective optimization problems, constraint handling techniques, multiobjective evolutionary algorithms, real-world-like problems, speed reducer design problem, Benchmark testing, Electronic mail, Linear programming, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969433}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969433}}, } @INPROCEEDINGS{duarte:2017:CEC, author={G. Duarte and A. Lemonge and L. Goliatt}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A dynamic migration policy to the Island Model}, year={2017}, editor = {Jose A. Lozano}, pages = {1135--1142}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Island Model is a mechanism that promotes improvement in the quality of the results produced by evolutionary algorithms and speed up their executions. A reason of the impact caused by the Island Model in the quality of results is the migration of solutions between islands that occurs periodically during the search process. The migration process depends on decisions such as the choice of solutions that will be send, the destination islands etc. This set of decisions is known as migration policy. This paper proposes a migration policy to the Island Model in which the destination island for an emigrant solution is defined according to the attractiveness of the islands in the model. In the proposed model the attractiveness between islands also influences the connection between them and affect the topology of the model. This paper evaluated if the proposed model is able to maintain the two main characteristics of the Island Model. The movement of solutions and the states of the connections were evaluated too.}, keywords = {evolutionary computation, parallel algorithms, dynamic migration policy, evolutionary algorithms, island model, Biological system modeling, Computational modeling, Genetic algorithms, Partitioning algorithms, Sociology, Statistics, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969434}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969434}}, } @INPROCEEDINGS{mu:2017:CEC, author={C. Mu and Huiwen Cheng and Wei Feng and Y. Liu and R. Qu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems}, year={2017}, editor = {Jose A. Lozano}, pages = {1143--1149}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation.}, keywords = {combinatorial mathematics, evolutionary computation, optimisation, recommender systems, EA-EP, combinatorial optimization problem, core users, evolutionary algorithm with elite population, information core optimization, online recommendation, ordered crossover, personality recommendation, Collaboration, Optimization, Prediction algorithms, Sociology, Statistics, elite population, evolutionary algorithm, recommender system}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969435}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969435}}, } @INPROCEEDINGS{mu:2017:CECa, author={C. Mu and Chengzhou Li and Y. Liu and Menghua Sun and Licheng Jiao and R. Qu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1150--1157}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA.}, keywords = {feature extraction, fuzzy set theory, genetic algorithms, pattern clustering, radar imaging, synthetic aperture radar, FCM clustering algorithm, S-aGA, SAR images, accelerated genetic algorithm, change detection algorithm, fuzzy c-means clustering algorithm, image region extraction, logarithm ratio operator, saliency detection model, salient map guidance, synthetic aperture radar images, Acceleration, Change detection algorithms, Classification algorithms, Computational efficiency, Linear programming, Saliency map, Synthetic Aperture Radar (SAR) image, change detection, fuzzy c-means (FCM), genetic algorithm (GA)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969436}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969436}}, } @INPROCEEDINGS{li:2017:CECg, author={Lin Li and Y. Li}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Particle filter track-before-detect algorithm with Lamarckian inheritance for improved dim target tracking}, year={2017}, editor = {Jose A. Lozano}, pages = {1158--1164}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Particle filter track-before-detect (PF-TBD) algorithms offer improvements over track-after-detect algorithms in detecting and tracking dim targets. However, it suffers from the particle collapsing problem, which can lead to deteriorated detection and tracking performance. To address this issue, a Lamarckian particle filter track-before-detect (LPF-TBD) algorithm is developed in this paper. In the LPF-TBD, before a TBD resampling process, a particle update strategy is applied, which is based on Lamarckian overriding and elitist operators designed to improve the particle diversity and efficiency. The effectiveness of the LPF-TBD algorithm is demonstrated using a widely adopted experiment on a target with a low signal-to-noise ratio in an image sequence. Compared with the currently-popular multinomial resampling PF-TBD method, the posterior distribution in the LPF-TBD can be more sufficiently approximated by the particles. Test results show that the LPF-TBD offers higher detection and tracking performance, while strengthening the algorithmic efficiency of particle filtering and evolutionary algorithms.}, keywords = {image sampling, image sequences, object detection, particle filtering (numerical methods), sampling methods, target tracking, LPF-TBD algorithm, Lamarckian particle filter track-before-detect algorithm, TBD resampling process, dim target tracking, image sequence, particle collapsing problem, particle update strategy, signal-to-noise ratio, Algorithm design and analysis, Approximation algorithms, Atmospheric measurements, Heuristic algorithms, Particle filters, Radar tracking, Lamarckian inheritance, evolution algorithm, particle filter, track-before-detect}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969437}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969437}}, } @INPROCEEDINGS{hou:2017:CEC, author={L. Hou and Gang Zhao and Yue Yang and Zhaohui Hou}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective optimization with Proper Orthogonal Decomposition and Gaussian predictive distribution}, year={2017}, editor = {Jose A. Lozano}, pages = {1165--1172}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A numerical study of multi-objective optimization with Proper Orthogonal Decomposition (POD) and Gaussian predictive distribution on the well known ZDT and DLZT benchmark set, is presented and discussed. Based on the algorithm, design optimization of the Global Trajectory Optimization Problems (GTOP) database based on trajectory models of real-world interplanetary space mission, Cassini is presented. The trajectory models are formulated as nonlinear optimization problems and are known to be difficult to solve. In this contribution, for part of the standard ZDT and DTLZ series problems, the proposed algorithm is able to solve these benchmarks to their optimal solutions within 20 generations.}, keywords = {Gaussian distribution, nonlinear programming, space vehicles, Cassini, DLZT benchmark set, GTOP, Gaussian predictive distribution, POD, ZDT benchmark set, design optimization, global trajectory optimization problems, multiobjective optimization, nonlinear optimization problems, proper orthogonal decomposition, real-world interplanetary space mission, standard DTLZ series problem, standard ZDT series problem, trajectory models, Algorithm design and analysis, Ellipsoids, Measurement, Optimization, Sociology, Statistics, Trajectory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969438}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969438}}, } @INPROCEEDINGS{hammer:2017:CEC, author={H. L. Hammer and A. Yazidi and B. J. Oommen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On using novel #x201C;Anti-Bayesian #x201D; techniques for the classification of dynamical data streams}, year={2017}, editor = {Jose A. Lozano}, pages = {1173--1182}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compare the testing sample with the distribution's central points, AB techniques are based on the information in the distant-from-the-mean samples. Most Bayesian approaches can be naturally extended to dynamical systems by dynamically tracking the mean of each class using, for example, the exponential moving average based estimator, or a sliding window estimator. The AB schemes introduced by Oommen et al., on the other hand, work with a radically different approach and with the non-central quantiles of the distributions. Surprisingly and counter-intuitively, the reported AB methods work equally or close-to-equally well to an optimal supervised Bayesian scheme on a host of accepted PR problems. This thus begs its natural extension to the unexplored arena of classification for dynamical data streams. Naturally, for such an AB classification approach, we need to track the non-stationarity of the quantiles of the classes. To achieve this, in this paper, we develop an AB approach for the online classification of data streams by applying the efficient and robust quantile estimators developed by Yazidi and Hammer [3], [13]. Apart from the methodology itself, in this paper, we compare the Bayesian and AB approaches. Th- results demonstrate the intriguing and counter-intuitive results that the AB approach shows competitive results to the Bayesian approach. Furthermore, the AB approach is much more robust against outliers, which is an inherent property of quantile estimators [3], [13], which is a property that the Bayesian approach cannot match, since it rather tracks the mean.}, keywords = {pattern classification, AB classification approach, AB techniques, Bayesian paradigm, anti-Bayesian techniques, data stream distribution, dynamical data stream classification, exponential moving average based estimator, sliding window estimator, Bayes methods, Computational modeling, Computer science, Estimation, Robustness, Testing, Training, Anti-Bayesian Classification, Classification With delay, Data Streams, Incremental Quantile Estimation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969439}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969439}}, } @INPROCEEDINGS{ghannem:2017:CEC, author={A. Ghannem and M. S. Hamdi and M. Kessentini and H. H. Ammar}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Search-based requirements traceability recovery: A multi-objective approach}, year={2017}, editor = {Jose A. Lozano}, pages = {1183--1190}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Software systems nowadays are complex and difficult to maintain due to the necessity of continuous change and adaptation. One of the challenges in software maintenance is keeping requirements traceability up to date automatically. The process of generating requirements traceability is time-consuming and error-prone. Currently, most available tools do not support the automated recovery of traceability links. In some situations, companies accumulate the history of changes from past maintenance experiences. In this paper, we consider requirements traceability recovery as a multi objective search problem in which we seek to assign each requirement to one or many software elements (code elements, API documentation, and comments) by taking into account the recency of change, the frequency of change, and the semantic similarity between the description of the requirement and the software element. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the best compromise between these three objectives. We report the results of our experiments on three open source projects.}, keywords = {genetic algorithms, software maintenance, multiobjective approach, nondominated sorting genetic algorithm, requirements traceability, search-based requirements traceability recovery, software elements, software systems, traceability links recovery, Frequency measurement, History, Maintenance engineering, Search problems, Semantics, Software, Tools, NSGA-II, Pareto Front, Requirements Engineering, Search based Software Engineering}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969440}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969440}}, } @INPROCEEDINGS{vigneysh:2017:CEC, author={T. Vigneysh and N. Kumarappan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Enhanced operation of grid-connected photovoltaic system using interval type-2 fuzzy control}, year={2017}, editor = {Jose A. Lozano}, pages = {1191--1198}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a novel intelligent control technique based on interval type-2 fuzzy logic controller (IT-2 FLC) for three-phase photovoltaic (PV) power generation system connected to a weak utility grid. In the proposed control technique, the grid-connected PV system is effectively controlled to inject high quality sinusoidal current into the utility grid despite various uncertainties in system operating conditions. The presence of third dimension in the membership function of IT-2 FLC provides an additional degree of freedom and hence it offers excellent control performance than the conventional PI controller and type-1 FLC (T-1 FLC) during system uncertainties. In addition, the proposed control strategy does not require any mathematical model of the system and hence the controller design task is simple. The effectiveness of the proposed controller in reducing the total harmonic distortion of the grid current is verified using extensive simulation studies carried out under different scenarios. Additionally, the results are also compared with existing controller reported in the literature to show the superior performance of the proposed controller under disturbances.}, keywords = {fuzzy control, photovoltaic power systems, power generation control, IT-2 FLC, grid-connected photovoltaic system, interval type-2 fuzzy logic controller, membership function, novel intelligent control technique, three-phase photovoltaic power generation system, Fuzzy logic, Fuzzy sets, Phase locked loops, Power generation, Uncertainty, Voltage control, Grid-connected mode, Microgrid, Photovoltaic system, Type-2 fuzzy logic}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969441}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969441}}, } @INPROCEEDINGS{vigneysh:2017:CECa, author={T. Vigneysh and N. Kumarappan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Stability analysis and dynamic performance enhancement of autonomous microgrid using adaptive fuzzy PI controller}, year={2017}, editor = {Jose A. Lozano}, pages = {1199--1206}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, an adaptive fuzzy proportional-integral (AFPI) controller is proposed to improve the dynamic performance of the inverter interfaced autonomous microgrid. For this purpose, an accurate small signal state space model of the microgrid with electronically interfaced distributed generation (DG) units is developed. The developed model includes the network dynamics, controller dynamics, dynamics of LCL filter and load dynamics. In addition, the effect of damping resistor is also included in the proposed model to show its effect on damping high frequency oscillatory modes. After that, eigenvalue analysis is carried out to determine the optimal ranges of critical control parameters, which greatly affects the small signal stability of the microgrid. Finally based on the analysis, an AFPI controller is designed here to enhance the dynamic performance of the autonomous microgrid during disturbances. To validate the effectiveness of the proposed controller, nonlinear time domain simulation is carried out using Matlab/Simulink and results are compared with conventional proportional integral (PI) controller and also with other existing intelligent controllers to show the superiority of the proposed controller.}, keywords = {PI control, adaptive control, control system synthesis, damping, distributed power generation, eigenvalues and eigenfunctions, fuzzy control, invertors, power system stability, state-space methods, AFPI controller design, LCL filter dynamics, Matlab, Simulink, adaptive fuzzy PI controller, adaptive fuzzy proportional-integral controller, controller dynamics, damping high frequency oscillatory modes, damping resistor, dynamic performance enhancement, eigenvalue analysis, electronically interfaced distributed generation units, inverter interfaced autonomous microgrid, load dynamics, microgrid signal stability, network dynamics, nonlinear time domain simulation, small signal state space model, stability analysis, Load modeling, Mathematical model, Microgrids, Power system dynamics, Voltage control, Adaptive fuzzy logic control, Autonomous microgrid, Distributed generation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969442}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969442}}, } @INPROCEEDINGS{fagiani:2017:CEC, author={M. Fagiani and M. Severini and S. Squartini and L. Ciabattoni and F. Ferracuti and A. Fonti and G. Comodi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A new open-source Energy Management framework: Functional description and preliminary results}, year={2017}, editor = {Jose A. Lozano}, pages = {1207--1214}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, a new open-source SW framework for energy management is presented. Its name is rEMpy, which stands for residential Energy Management in python. The framework has a modular structure and it is composed by an optimal scheduler, a user interface, a prediction module and the building thermal model. Unlike most of the EMs in literature, rEMpy is open-source, can be fully customized (in terms of tasks, modules and algorithms) and integrates in real-time a thermal modelling software. In this contribution, an overview of the rEMpy and its constitutive parts is given first, followed by a detailed description of the rEMpy modules and the communication system. The Computational Intelligence algorithms which perform forecasting, thermal modelling and optimal scheduling are also presented. The performance of rEMpy is finally evaluated in two case studies with different heating technologies and the results are reported and discussed.}, keywords = {energy management systems, public domain software, scheduling, user interfaces, Python, building thermal model, communication system, computational intelligence algorithms, functional description, open-source SW framework, open-source energy management framework, optimal scheduler, optimal scheduling, prediction module, rEMpy modules, residential energy management, thermal modelling software, user interface, Databases, Dictionaries, Energy management, Predictive models}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969443}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969443}}, } @INPROCEEDINGS{harada:2017:CEC, author={T. Harada and K. Takadama}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony}, year={2017}, editor = {Jose A. Lozano}, pages = {1215--1222}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a parallel asynchronous evolutionary algorithm (EA) with different asynchrony and verifies its effectiveness on multi-objective optimization problems. We represent such EA with different asynchrony as semi-asynchronous EA. The semi-asynchronous EA continuously evolves solutions whenever a part of solutions in the population completes their evaluations in the master-slave parallel computation environment, unlike a conventional synchronous EA, which waits for evaluations of all solutions to generate next population. To establish the semi-asynchronous EA, this paper proposes the asynchrony parameter to decide how many solutions are waited, and clarifies the effectual asynchrony related to the number of slave nodes. In the experiment, we apply the semi-asynchronous EA to NSGA-II, which is a well-known multi-objective evolutionary algorithm, and the semi-asynchronous NSGA-IIs with different asynchrony are compared with synchronous one on the multi-objective optimization benchmark problems with several variances of evaluation time. The experimental result reveals that the semi-asynchronous NSGA-II with low asynchrony has possibility to perform the best search ability than the complete asynchronous and the synchronous NSGA-II in the optimization problems with large variance of evaluation time.}, keywords = {evolutionary computation, parallel algorithms, search problems, asynchrony parameter, master-slave parallel computation environment, multiobjective optimization problems, nondominated sorting genetic algorithm, parallel asynchronous multiobjective evolutionary algorithm, search ability, semiasynchronous EA, semiasynchronous NSGA-II, slave nodes, Computational modeling, Master-slave, Mathematical model, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969444}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969444}}, } @INPROCEEDINGS{zheng:2017:CEC, author={Hong-Kun Zheng and J. J. Li and Y. J. Gong and W. N. Chen and Zhiwen Yu and Z. H. Zhan and Ying Lin}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Link mapping-oriented ant colony system for virtual network embedding}, year={2017}, editor = {Jose A. Lozano}, pages = {1223--1230}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Virtual network embedding (VNE), which is a significant problem in cloud computing, has gained much attention among many researchers recently. Due to the NP-hardness of VNE, the existing solvers are either inefficient or inaccurate. This paper develops a novel algorithm based on the ant colony system (ACS). To solve the VNE problem, the algorithm structure concentrates on the link mapping from virtual network to substrate network. Particularly, for a specific virtual network request, we first sort the embedding sequence of virtual nodes according to their link resources. Then, ACS is used to embed the virtual nodes onto substrate nodes according the sorted sequence, while the virtual links are mapped via a shortest path strategy for the embedded nodes. For the first time, we propose a link resource heuristic information and incorporate it into the search process of ACS. The link resource heuristic information has two significant effects, one is to make virtual nodes tend to be embedded on the substrate nodes that cost less bandwidth, and the other is to confirm the connectivity of the substrate nodes that embed the virtual nodes. The proposed algorithm improves the optimization performance of VNE when compared with a few existing algorithms, while it substantially reduces the cost of time.}, keywords = {ant colony optimisation, cloud computing, computer networks, ACS, NP-hardness, VNE problem, algorithm structure, ant colony system, embedded nodes, embedding sequence, link mapping, link mapping oriented ant colony system, link resource heuristic information, link resources, shortest path strategy, sorted sequence, substrate nodes, virtual links, virtual network, virtual network embedding, virtual nodes, Bandwidth, Computer science, Indium phosphide, Optimization, Resource management, Substrates, Topology, Link Mapping Oriented}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969445}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969445}}, } @INPROCEEDINGS{trivedi:2017:CEC, author={A. Trivedi and K. Sanyal and P. Verma and D. Srinivasan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A unified differential evolution algorithm for constrained optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1231--1238}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, a unified differential evolution algorithm, named UDE, is presented for real parameter constrained optimization problems. The proposed UDE algorithm is inspired from some popular DE variants existing in the literature such as CoDE, JADE, SaDE, and ranking-based mutation operator. The primary feature of UDE lies in unifying the main idea of CoDE, JADE, SaDE, and ranking-based mutation. UDE uses three trial vector generation strategies and two parameter settings. At each generation, UDE divides the current population into two sub-populations. In the top sub-population, UDE employs all the three trial vector generation strategies on each target vector, just like in CoDE. For the bottom sub-population, UDE employs strategy adaptation, in which the trial vector generation strategies are periodically self-adapted by learning from their experiences in generating promising solutions in the top sub-population. Further, UDE utilizes a DE mutation strategy based local search operation. The constraints are handled in UDE using static penalty method. In contrast to most of the DE variants presented in the literature, UDE employs a generational replacement strategy. The proposed UDE algorithm is tested on the 28 benchmark problems provided for the CEC 2017 competition on constrained real parameter optimization. The experimental results demonstrate the efficacy of the presented algorithm in solving constrained real parameter optimization problems.}, keywords = {evolutionary computation, optimisation, search problems, vectors, DE mutation strategy, UDE algorithm, constrained optimization problems, local search operation, static penalty method, subpopulation, target vector, trial vector generation strategies, unified differential evolution algorithm, Benchmark testing, Convergence, Indexes, Optimization, Sociology, Statistics, Constrained optimization, Differential evolution, Local Search, Ranking, Self-adaptive, Strategy Adaptation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969446}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969446}}, } @INPROCEEDINGS{hirsch:2017:CEC, author={L. Hirsch and A. Di Nuovo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Document clustering with evolved search queries}, year={2017}, editor = {Jose A. Lozano}, pages = {1239--1246}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Search queries define a set of documents located in a collection and can be used to rank the documents by assigning each document a score according to their closeness to the query in the multidimensional space of weighted terms. In this paper, we describe a system whereby an island model genetic algorithm (GA) creates individuals which can generate a set of Apache Lucene search queries for the purpose of text document clustering. A cluster is specified by the documents returned by a single query in the set. Each document that is included in only one of the clusters adds to the fitness of the individual and each document that is included in more than one cluster will reduce the fitness. The method can be refined by using the ranking score of each document in the fitness test. The system has a number of advantages; in particular, the final search queries are easily understood and offer a simple explanation of the clusters, meaning that an extra cluster labelling stage is not required. We describe how the GA can be used to build queries and show results for clustering on various data sets and with different query sizes. Results are also compared with clusters built using the widely used k-means algorithm.}, keywords = {data mining, genetic algorithms, pattern clustering, query formulation, text analysis, Apache Lucene search queries, document ranking, island model genetic algorithm, text document clustering, text mining, Biological cells, Clustering algorithms, Indexes, Information retrieval, Labeling, Libraries, genetic algorithm, text clustering}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969447}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969447}}, } @INPROCEEDINGS{parker:2017:CEC, author={A. Parker and G. Nitschke}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={How to best Automate Intersection Management}, year={2017}, editor = {Jose A. Lozano}, pages = {1247--1254}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Recently there has been increased research interest in developing adaptive control systems for autonomous vehicles. This study presents a comparative evaluation of two distinct approaches to automated intersection management for a multi-agent system of autonomous vehicles. The first is a centralized heuristic control approach using an extension of the Autonomous Intersection Management (AIM) system. The second is a decentralized neuro-evolution approach that adapts vehicle controllers so as they collectively navigate intersections. This study tests both approaches for controlling groups of autonomous vehicles on a network of interconnected intersections, without the constraints of traffic lights or stop signals. These task environments thus simulate potential future scenarios where vehicles must drive autonomously without specific road infrastructure constraints. The capability of each approach to appropriately handle various types of interconnected intersections, while maintaining an efficient throughput of vehicles and minimizing delay is tested. Results indicate that neuro-evolution is an effective method for automating collective driving behaviors that are robust across a broad range of road networks, where evolved controllers yield comparable task performance or out-perform an AIM controller.}, keywords = {adaptive control, decentralised control, road traffic control, AIM system, adaptive control system, autonomous intersection management system, centralized heuristic control approach, collective driving behavior, decentralized neuro-evolution approach, multi-agent system, road infrastructure constraints, Autonomous vehicles, Delays, Navigation, Protocols, Roads, Sociology, Throughput}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969448}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969448}}, } @INPROCEEDINGS{mehta:2017:CEC, author={R. Mehta and D. Srinivasan and P. Verma}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Intelligent appliance control algorithm for optimizing user energy demand in smart homes}, year={2017}, editor = {Jose A. Lozano}, pages = {1255--1262}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Advanced metering infrastructure which is an integral component of smart homes has aided in tapping the potential of the residential sector for demand side management (DSM). DSM in smart homes focus mainly on some power-intensive appliances which affect the household load profile significantly. This paper proposes an intelligent appliance control (IAC) algorithm to monitor and control the daily operation of these power-intensive appliances using their simulated load models. The proposed algorithm employs differential evolution (DE) algorithm along with a DSM strategy to limit the smart household power consumption at every half an hour to an optimum limit. The paper demonstrates the ability of the proposed algorithm in minimizing the households' monthly electricity bill, maximizing the peak load reduction and minimizing the problem of distribution transformer overloading. The paper also focuses on studying the impacts of time of use (TOU) electricity pricing on residential customers' behavior. The simulation results indicate that TOU pricing augments the benefits of the proposed algorithm both at the residential level and the distribution transformer level.}, keywords = {demand side management, evolutionary computation, home computing, intelligent control, DSM, IAC algorithm, TOU pricing, advanced metering infrastructure, differential evolution algorithm, intelligent appliance control algorithm, smart homes, smart household power consumption, time of use electricity pricing, Home appliances, Load flow control, Load modeling, Power demand, Pricing, Storage tanks, Water heating, Appliance control, demand side management (DSM), load shedding, load shifting, time-of-use (TOU) pricing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969449}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969449}}, } @INPROCEEDINGS{bartashevich:2017:CEC, author={P. Bartashevich and L. Grimaldi and S. Mostaghim}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={PSO-based Search mechanism in dynamic environments: Swarms in Vector Fields}, year={2017}, editor = {Jose A. Lozano}, pages = {1263--1270}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents the Vector Field Map PSO (VFM-PSO) as a collective search algorithm for aerial micro-robots in environments with unknown external dynamics (such as wind). The proposed method is based on a multi-swarm approach and allows to cope with unknown disturbances arising by the vector fields in which the positions and the movements of the particles are highly affected. VFM-PSO requires gathering the information regarding the vector fields and one of our goals is to investigate the amount of the required information for a successful search mechanism. The experiments show that VFM-PSO can reduce the drift and improves the performance of the PSO algorithm despite incomplete information (awareness) about the structure of considered vector fields.}, keywords = {aerospace control, aerospace robotics, microrobots, mobile robots, particle swarm optimisation, search problems, vectors, PSO algorithm, VFM-PSO, aerial microrobots, collective search algorithm, dynamic environments, multiswarm approach, search mechanism, unknown external dynamics, vector field map PSO, Heuristic algorithms, Interpolation, Mathematical model, Optimization, Robots, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969450}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969450}}, } @INPROCEEDINGS{veg:2017:CEC, author={F. F. de Vega}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Revisiting the 4-part harmonization problem with GAs: A critical review and proposals for improving}, year={2017}, editor = {Jose A. Lozano}, pages = {1271--1278}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The four-part harmonization problem is a well known problem that has been studied in the last three centuries by music scholars. The goal is to build up three different voices, melodies, based on a previously provided one, being it a soprano melody or a bass instead, so that a complete soprano, alto, tenor and bass (SATB) score is completed. The nature of the problem, combinatorial, has attracted interest for decades, and different artificial intelligence techniques have subsequently been applied, such as constraint programing or genetic algorithms. Although researchers employing the first have already stated that the problem is basically solved, and comparisons with GAs typically benefit the first, we think that a critical review of literature may provide useful information demonstrating that the problem is open for improvement, and that GAs still have an opportunity: tests presented in literature frequently employ examples of low difficulty, which provide misleading conclusions. In this paper we present a review the literature and show that the samples employed to test every available technique are frequently oversimplified; moreover, we have analyzed many of the solutions provided, and have seen how they are not solutions at all, given the number of errors they embody. Yet, we not only try to show drawbacks of previous approaches. We also try to understand difficulties for GAs when addressing the problem. We analyze the nature of the problem performing a number of tests, and try to see why the standard GA cannot cope with the problem. We propose new approaches that show how GAs could in the future be perfectly capable of addressing large and complex samples, providing solutions of much higher quality.}, keywords = {combinatorial mathematics, genetic algorithms, music, 4-part harmonization problem, GA, SATB score, artificial intelligence techniques, combinatorial problem, soprano melody, soprano-alto-tenor-and-bass score, Buildings, Computers, Modulation, Search problems, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969451}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969451}}, } @INPROCEEDINGS{phillips:2017:CEC, author={T. Phillips and M. Zhang and B. Xue}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic programming for solving common and domain-independent generic recursive problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1279--1286}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In human written computer programs, loops and recursion are very important structures. Many real-world applications require recursion and loops. Loops and recursion can also be achieved by using genetic programming (GP). There has been a lot of work on GP for loops but not much on recursion. Our recent initial work on GP for recursion has shown that GP can be used to solve recursion problems, based on which this work develops two new GP methods that can solve a wider range of problems without decreasing the performance. The two new methods are tested on symbolic regression problems, binary classification problems, and Artificial Ant problems. They are compared to methods using loops, traditional GP, and the methods developed in our previous work. The results show that the new methods have improved the accuracy and increased the range of symbolic regression problems that the methods can perfectly solve, and improved the performance on two of the Artificial Ant problems. The new methods can also solve classification problems, and have better performance than loops on many of these tasks. This is the first work using recursion for classification problems, and is the first design of a generic recursive method for GP.}, keywords = {genetic algorithms, genetic programming, pattern classification, program control structures, regression analysis, artificial ant problems, binary classification problems, domain-independent generic recursive problems, human written computer programs, loops, recursion problems, symbolic regression problems, Computer science, Computers, Design methodology, Image recognition, Libraries, Navigation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969452}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969452}}, } @INPROCEEDINGS{o'neill:2017:CEC, author={D. O'Neill and H. Al-Sahaf and B. Xue and M. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Common subtrees in related problems: A novel transfer learning approach for genetic programming}, year={2017}, editor = {Jose A. Lozano}, pages = {1287--1294}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Transfer learning is a machine learning technique which has demonstrated great success in improving outcomes on a broad range of problems. However prior methods of transfer learning in Genetic Programming (GP) have tended to rely on random processes or meta-knowledge of the problem structure to facilitate selection of information for use in transfer. To address these issues, a non-random method for automatically finding relevant information for transfer between two source domain problems from the same problem domain based on common subtrees is proposed. This information is then utilised within a modular transfer learning framework, being added to the function set for a target problem prior to population initialisation. The performance of the proposed method is assessed using multiple benchmark problems from two distinct problem domains, namely symbolic regression and Boolean domain problems, and compared to standard GP and the-state-of-the-art transfer learning method for the given problems. The results show that the newly introduced method has either significantly outperformed, or achieved comparable performance to, the competitor methods on the problems of the two domains. We conclude that the proposed method demonstrates ability as a general transfer learning technique for GP and note some possible avenues for future research based off these results.}, keywords = {genetic algorithms, genetic programming, Boolean algebra, learning (artificial intelligence), regression analysis, trees (mathematics), Boolean domain problems, common subtrees, machine learning, meta-knowledge, symbolic regression, transfer learning, Algorithm design and analysis, Learning systems, Sociology, Standards, Statistics, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969453}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969453}}, } @INPROCEEDINGS{bali:2017:CEC, author={K. K. Bali and A. Gupta and L. Feng and Y. S. Ong and Tan Puay Siew}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Linearized domain adaptation in evolutionary multitasking}, year={2017}, editor = {Jose A. Lozano}, pages = {1295--1302}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Recent analytical studies have revealed that in spite of promising success in problem solving, the performance of evolutionary multitasking deteriorates with decreasing similarity between constitutive tasks. The present day multifactorial evolutionary algorithm (MFEA) is susceptible to negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we propose a linearized domain adaptation (LDA) strategy that transforms the search space of a simple task to the search space similar to its constitutive complex task. This high order representative space resembles high correlation with its constitutive task and provides a platform for efficient knowledge transfer via crossover. The proposed framework, LDA-MFEA is tested on several benchmark problems constituting of tasks with different degrees of similarities and intersecting global optima. Experimental results demonstrate competitive performances against MFEA and shows that our proposition dramatically improves the performance relative to optimizing each task independently.}, keywords = {evolutionary computation, LDA, MFEA, constitutive complex task, constitutive tasks, evolutionary multitasking, global optima, knowledge transfer, linearized domain adaptation, multifactorial evolutionary algorithm, Correlation, Genetics, Multitasking, Optimization, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969454}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969454}}, } @INPROCEEDINGS{simões:2017:CEC, author={C. Simões and R. Neves and N. Horta}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Using sentiment from Twitter optimized by Genetic Algorithms to predict the stock market}, year={2017}, editor = {Jose A. Lozano}, pages = {1303--1310}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this work we propose to use Twitter to find companies with a good growth potential that could be good investment options. In order to achieve this we built a sentiment model using the text content of tweets. We make use of hashtags to collect Twitter posts from a broad range of emotions so that our sentiment model can reliably distinguish tweets containing different sentiment expressions. To guarantee that no human sentiment is left behind we adopted emotions from the Circumplex Model of Affect and their synonyms and used them as search terms on the Twitter API. Afterwards, we use those tweets with a support vector machine (SVM) classifier to build a sentiment model. This model was used to classify company related tweets in order to predict the predominant sentiment in them. With the sentiment measures of tweets from different companies we created a trading rule that was optimized by a Genetic Algorithm (GA) so that we can maximize profit. Our simulations show that using the rules we implemented it is possible to build a profitable strategy for trading in the stock market using Twitter with the rules we implemented. During our testing period (November 7, 2016 to December 16, 2016) we achieved a 11% return, outperforming the S&P 500, NASDAQ 100 and DJIA composites.}, keywords = {application program interfaces, genetic algorithms, investment, sentiment analysis, social networking (online), stock markets, support vector machines, API, SVM classifier, Twitter, circumplex model, investment options, sentiment expressions, stock market, support vector machine, Companies, Tagging, opinion mining}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969455}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969455}}, } @INPROCEEDINGS{brest:2017:CEC, author={J. Brest and M. S. Maučec and B. Bošković}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Single objective real-parameter optimization: Algorithm jSO}, year={2017}, editor = {Jose A. Lozano}, pages = {1311--1318}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Solving single objective real-parameter optimization problems, also known as a bound-constrained optimization, is still a challenging task. We can find such problems in engineering optimization, scientific applications, and in other real-world problems. Usually, these problems are very complex and computationally expensive. A new algorithm, called jSO, is presented in this paper. The algorithm is an improved variant of the iL-SHADE algorithm, mainly with a new weighted version of mutation strategy. The experiments were performed on CEC 2017 benchmark functions, which are different from previous competition benchmark functions. A comparison of the proposed jSO algorithm and the L-SHADE algorithm is presented first. From the obtained results we can conclude that jSO performs better in comparison with the L-SHADE algorithm. Next, a comparison of jSO and iL-SHADE is also performed, and jSO obtained better or competitive results. Using the CEC 2017 evaluation method, jSO obtained the best final score among these three algorithms.}, keywords = {computational complexity, evolutionary computation, optimisation, CEC 2017 benchmark functions, bound-constrained optimization, complex problems, computationally expensive, differential evolution, engineering optimization, iL-SHADE algorithm, jSO algorithm, mutation strategy, single objective real-parameter optimization, weighted version, Benchmark testing, Indexes, Linear programming, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969456}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969456}}, } @INPROCEEDINGS{belhaiza:2017:CEC, author={S. Belhaiza and R. M'Hallah and G. B. Brahim}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A new Hybrid Genetic Variable Neighborhood search heuristic for the Vehicle Routing Problem with Multiple Time Windows}, year={2017}, editor = {Jose A. Lozano}, pages = {1319--1326}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Vehicle Routing Problem (VRP) is a known optimization problems falling under the category of NP-Hard set of problems. VRP, along with its variations, continue to be extensively explored by the research community due to their large domain of application (environment, agriculture, industry, etc.) and economic impact on improving the overall performance, Quality of Services and reducing the operational cost. In this paper, we focus on VRPMTW; a variant of VRP with Multiple Time Windows constraints. We introduce a novel Hybrid Genetic Variable Neighborhood Search (HGVNS) based heuristic for the optimization of VRPMTW. The proposed framework uses genetic cross-over operators on a list of best parents and new implementations of local search operators. Computational results on benchmark data show substantial performance improvement when using the newly introduced heuristic.}, keywords = {genetic algorithms, search problems, vehicle routing, NP-Hard problem set, VRP, VRPMTW problem, genetic crossover operators, hybrid genetic variable neighborhood search heuristic, local search operators, multiple time windows constraints, vehicle routing problem, Benchmark testing, Genetics, Time factors, Transportation, Genetic Algorithm, VRP with Multiple Time Windows, Variable Neighborhood Search}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969457}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969457}}, } @INPROCEEDINGS{gazafroudi:2017:CEC, author={A. S. Gazafroudi and T. Pinto and F. Prieto-Castrillo and J. Prieto and J. M. Corchado and A. Jozi and Z. Vale and G. K. Venayagamoorthy}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Organization-based Multi-Agent structure of the Smart Home Electricity System}, year={2017}, editor = {Jose A. Lozano}, pages = {1327--1334}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.}, keywords = {building management systems, demand side management, home computing, multi-agent systems, power grids, power markets, stochastic programming, BEMS, DRP, JADE implementation, MSPB interval optimization method, TOU rate, building energy management system, demand response program, local electricity market, modified stochastic predicted bands, organization-based MASHES, organization-based multiagent system, power grid, prediction engine, smart home electricity system, time of use rate, Electricity supply industry, Energy management, Linear programming, Multi-stage noise shaping, Smart homes, Uncertainty, decision-making under uncertainty, interval optimization, multiagent system}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969458}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969458}}, } @INPROCEEDINGS{ashlock:2017:CECa, author={D. Ashlock and G. Greenwood}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Modeling undependable subsidies with three-player generalized divide the dollar}, year={2017}, editor = {Jose A. Lozano}, pages = {1335--1342}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Divide the dollar is a two-player simultaneous game derived from a game invented by John Nash because its strategy space contains an entire subspace of Nash equilibria. This study applies a family of generalizations of divide the dollar, called set-based divide the dollar, to the problem of understanding the impact of undependable subsidies. Set based divide the dollar defines a family of games with easily controlled properties making it ideal for this modeling task. These subsidies are intended to encourage deal making but, if abruptly discontinued or funded unreliably, may have different effects from those intended. The study also demonstrates the generalization of the game to three players, something that the set-based formalism makes easy. Agents are encoded using a finite state representation that conditions its transitions on the result of deals. These results fall into three categories, the agent obtains the highest amount, the agent receives a lesser amount, or the agents fail to make a deal. This study compares a situation with no subsidies with dependable and undependable subsidies, using the rate at which deals are made as a assessment statistic. Two sorts of undependability are studied, abrupt cessation of the subsidy and unreliable funding.}, keywords = {game theory, Nash equilibrium, assessment statistic, finite state representation, set-based divide the dollar, set-based formalism, three-player generalized divide the dollar, two-player simultaneous game, undependable subsidies modeling, Authorization, Economics, Games, Reliability, Sociology, Statistics, Upper bound}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969459}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969459}}, } @INPROCEEDINGS{lin:2017:CEC, author={Xi Lin and Q. Zhang and Sam Kwong}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An efficient batch expensive multi-objective evolutionary algorithm based on Decomposition}, year={2017}, editor = {Jose A. Lozano}, pages = {1343--1349}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-objective problem is decomposed into several subproblems which will be solved simultaneously. MOBO/D builds Gaussian process model for each objective to learn the optimization surface, and defines utility function for each subproblem to guide the searching process. At each generation, MOEA/D algorithm is called to locate a set of candidate solutions which maximize all utility functions respectively, and a subset of those candidate solutions is selected for parallel batch evaluation. Experimental study on different test instances validates that MOBO/D can efficiently solve expensive multi-objective problems in parallel. The performance of MOBO/D is also better than several classical expensive optimization methods.}, keywords = {Bayes methods, Gaussian processes, evolutionary computation, functions, Gaussian process model, MOBO/D, MOEA/D algorithm, batch expensive multiobjective evolutionary algorithm, multiobjective Bayesian optimization algorithm based on decomposition, surrogate-model, utility function, Algorithm design and analysis, Computational modeling, Kernel, Lead, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969460}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969460}}, } @INPROCEEDINGS{sallam:2017:CEC, author={K. M. Sallam and S. M. Elsayed and R. A. Sarker and D. L. Essam}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-method based orthogonal experimental design algorithm for solving CEC2017 competition problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1350--1357}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Over the last two decades, many different evolutionary algorithms (EAs) have been proposed for solving optimization problems. However, no single EA has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple EAs in a single framework. In this paper, a new multi-method based EA that utilizes the search ability of multi-operator differential evolution algorithm (MODE) and covariance matrix adaptation evolution strategy CMA-ES algorithm in a single framework, has been presented, with the orthogonal experimental design (OED) and factor analysis (FA) used to select the proper combination of mutation strategies, control parameters adaptation strategies, and crossover operators. To judge the performance of this algorithm, 30 problems are solved from the CEC2017 competition and their results are analyzed.}, keywords = {covariance matrices, evolutionary computation, search problems, EA, FA, MODE, OED, covariance matrix adaptation evolution strategy CMA-ES algorithm, evolutionary algorithms, factor analysis, multioperator differential evolution algorithm, optimization problems, orthogonal experimental design, orthogonal experimental design algorithm, search ability, single framework, solving CEC2017 competition problems, Algorithm design and analysis, Heuristic algorithms, Optimization, Sociology, differential evolution, multi-method, multi-operator}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969461}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969461}}, } @INPROCEEDINGS{bujok:2017:CEC, author={P. Bujok and J. Tvrdík}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Enhanced individual-dependent differential evolution with population size adaptation}, year={2017}, editor = {Jose A. Lozano}, pages = {1358--1365}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A new variant of individual-dependent differential evolution (IDE) algorithm is proposed. The original IDE is enhanced by a new mutation strategy accelerating convergence in the last phase of the search. Moreover, the population size is adapted with respect to the diversity of the current population. The newly proposed IDEbd algorithm is applied to the benchmark suite for CEC 2017 competition on Single Objective Real-Parameter Numerical Optimization. Preliminary experiments showed better performance of IDEbd compared to the original IDE. The results achieved on the CEC 2017 test suite are also promising, especially in problems of lower dimension.}, keywords = {evolutionary computation, CEC 2017 competition, IDEbd algorithm, convergence acceleration, individual-dependent differential evolution, mutation strategy, population size adaptation, single objective real-parameter numerical optimization, Convergence, Cost function, Radio frequency, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969462}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969462}}, } @INPROCEEDINGS{li:2017:CECh, author={Longmei Li and Feng Yao and N. Jing and M. Emmerich}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Preference incorporation to solve multi-objective mission planning of agile earth observation satellites}, year={2017}, editor = {Jose A. Lozano}, pages = {1366--1373}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper investigates Earth observation scheduling of agile satellite constellation based on evolutionary multiobjective optimization (EMO). The mission planning of agile Earth observation satellite (AEOS) is to select and specify the observation activities to acquire images on the earth surface. This should be done in accordance with operational constraints and in order to maximize certain objectives. In this paper three objectives are considered, i. e. profit, quality and timeliness. Preference-based EMO methods are introduced to generate solutions preferable for the decision maker, whose preference is expressed by a reference point. An improved algorithm based on R-NSGA-II, which is named CD-NSGA-II, is proposed and compared with other algorithms in various scheduling scenarios. Results show that the chosen preference modeling paradigm allows to focus search on the interesting part of the Pareto front. Moreover, the tested algorithms behave differently with the change of reference point and degree of conflicts, among them CD-NSGA-II has the best overall performance. Suggestions on applying these algorithms in practice are also given in this paper.}, keywords = {Pareto optimisation, artificial satellites, evolutionary computation, genetic algorithms, AEOS, CD-NSGA-II, Earth observation scheduling, Pareto front, R-NSGA-II, agile earth observation satellite, agile satellite constellation, conflict degree, evolutionary multiobjective optimization, mission planning, multiobjective mission planning, preference modeling paradigm, preference-based EMO method, profit objective, quality objective, reference point, scheduling scenario, timeliness objective, Earth, Earth Observing System, Planning, Sociology, Statistics, Preference Integration, Satellite Mission Planning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969463}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969463}}, } @INPROCEEDINGS{aardt:2017:CEC, author={W. A. van Aardt and A. S. Bosman and K. M. Malan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Characterising neutrality in neural network error landscapes}, year={2017}, editor = {Jose A. Lozano}, pages = {1374--1381}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The characterisation of topographical features of fitness landscapes can provide significant insight into the nature of underlying optimisation problems and the behaviour of metaheuristic search algorithms. Neutrality as a landscape feature is often overlooked in continuous problems, but researchers have theorised that the presence of neutral regions on neural network error surfaces may be an impediment to current population-based search algorithms for training neural networks. An empirical approach to measuring the amount of neutrality would provide a stepping stone for future studies on the effects of neutrality. To date, there is no offline technique to achieve this in continuous domains. This paper proposes two normalised measures of neutrality based on a progressive random walk algorithm. Measurements are shown to agree with visual analysis of two-dimensional benchmark problems, and are shown to scale well to higher dimensions. The measures are ultimately applied to neural network classification problems where saturation-induced neutrality is confirmed.}, keywords = {neural nets, optimisation, search problems, topology, fitness landscapes, metaheuristic search algorithms, neural network classification, neural network error landscapes, neural network error surfaces, neutral regions, optimisation problems, population-based search algorithms, progressive random walk algorithm, saturation-induced neutrality, topographical features, Algorithm design and analysis, Artificial neural networks, Current measurement, Hypercubes, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969464}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969464}}, } @INPROCEEDINGS{walton-rivers:2017:CEC, author={J. Walton-Rivers and P. R. Williams and R. Bartle and D. Perez-Liebana and S. M. Lucas}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evaluating and modelling Hanabi-playing agents}, year={2017}, editor = {Jose A. Lozano}, pages = {1382--1389}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set-Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.}, keywords = {Monte Carlo methods, computer games, knowledge based systems, multi-agent systems, search problems, trees (mathematics), Hanabi-playing agent evaluation, Hanabi-playing agent modelling, IS-MCTS, collaborative card game, game-playing strength, hidden- information, information set-Monte Carlo tree search agent, predictor capabilities, rule-based agents, Artificial intelligence, Cognition, Computational modeling, Games, Planning, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969465}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969465}}, } @INPROCEEDINGS{schmitt:2017:CEC, author={R. Schmitt and P. Ramos and R. Santiago and L. Lamb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Novel Clique enumeration heuristic for detecting overlapping clusters}, year={2017}, editor = {Jose A. Lozano}, pages = {1390--1397}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={There are several known methods for detecting overlapping communities in graphs, each one having their advantages and limitations. The Clique Percolation Method (CPM) is one such method. CPM works by joining highly connected subgraphs (cliques) and using it to find the graph communities. However, the clique enumeration problem is NP-Hard, taking exponential time to be solved. This makes its use impractical in large real-world networks and applications. The aim of this paper is to present an efficient heuristic to enumerate cliques. This enables the Clique Percolation Method to detect overlapping communities in networks containing thousands of nodes. The analyses showed that our novel heuristic is competitive with other known methods regarding solution quality and we also make the CPM more scalable.}, keywords = {computational complexity, network theory (graphs), CPM, NP-Hard, clique enumeration heuristic, clique percolation method, graphs, overlapping clusters detection, overlapping communities, real-world networks, solution quality, Algorithm design and analysis, Binary trees, Clustering algorithms, Context, Electronic mail, Heuristic algorithms, Random access memory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969466}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969466}}, } @INPROCEEDINGS{alixandre:2017:CEC, author={B. F. d. F. Alixandre and M. Dorn}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={D-BRKGA: A Distributed Biased Random-Key Genetic Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1398--1405}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Despite the use of genetic algorithms in many optimization problems, many new versions were proposed since them, from distributed versions of the canonical genetic algorithm (GA) to more restructured evolutions like the Biased Random-Key Genetic Algorithm (BRKGA). Aiming to explore the best of both techniques, in this paper, a novel approach was proposed, resulting in a Distributed BRKGA (D-BRKGA) with a stratified migration policy. To compare the performance of the Distributed Genetic Algorithm (DGA) and the D-BRKGA, some functions of the CEC 2013 Benchmark set they were chosen because of their high complexity and greater dimensionality. The analysis of the results aimed to explore three aspects: quality of the final solutions, population diversity and convergence curve of both approaches. The results point out to a superior performance of D-BRKGA, proving to be efficient and scalable in relation to the number of distributions, in addition to maintaining a high population diversity.}, keywords = {computational complexity, distributed algorithms, genetic algorithms, CEC 2013 Benchmark, D-BRKGA, DGA, convergence curve, distributed BRKGA, distributed biased random-key genetic algorithm, optimization problems, population diversity, stratified migration policy, Computer architecture, Convergence, Genetics, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969467}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969467}}, } @INPROCEEDINGS{li:2017:CECi, author={Xiang Li and Dongni Li and Xuhui Wu and Hong Zheng and Y. Yin}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A cooperative co-evolution approach for a line-seru conversion problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1406--1411}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The seru system is a new way of assembling products which is widely used in the electronics industry. Unlike the traditional assembly line, several small assembly units are constructed of which each is responsible for assembling an entire product. In this paper, the conversion process of assembly lines to seru systems (line-seru conversion) is studied. A cooperative co-evolution (CC) approach is proposed to solve the two sub-problems simultaneously, i.e. seru formation and seru loading. Specifically, redundant encoding is added to the encoding scheme so that serus can be adjusted during the evolutionary process as the market demands change. The computational result shows that by the adoption of coordination mechanism, CC outperforms other approaches in minimizing both total throughput time (TTPT) and total labor hours (TLH).}, keywords = {assembling, electronics industry, evolutionary computation, assembly lines conversion process, assembly units, cooperative coevolution approach, coordination mechanism, evolutionary process, line-seru conversion problem, market demands, product assembling, redundant encoding, seru formation, seru loading, seru system, Business, Electronic mail, Encoding, Loading, Production, Throughput, assembly line, cooperative co-evolution, line-seru conversion}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969468}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969468}}, } @INPROCEEDINGS{utkarsh:2017:CEC, author={K. Utkarsh and D. Srinivasan and T. Reindl and A. Trivedi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Grid ancillary service using distributed computational intelligence based control of renewables and storage systems in a distribution network}, year={2017}, editor = {Jose A. Lozano}, pages = {1412--1419}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The large-scale integration of renewable energy sources (RESs) at the distribution network level provides several benefits and opportunities for the distribution network as well as the transmission network, despite having some technical challenges such as intermittency handling, network voltage control, etc. Such RESs are typically accompanied with battery storage systems (BSSs) to mitigate effects of their intermittency. The RES-BSS systems can therefore be used to provide ancillary services to the transmission network while providing technical benefits to the distribution network operator (DNO) and economic benefits to the RES owners. Further, computational intelligence based techniques can provide a near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in fulfilling ancillary service requests. Therefore, this paper presents a fully distributed computational intelligence based technique so that the distribution network could handle ancillary service requests by the transmission system operator (TSO) in real-time. Specifically, an agent is associated with each node of the distribution network and it utilizes computational intelligence and communication with nearby agents to achieve the solution. Simulation studies on a modified IEEE 30-node test system are shown to validate our aforementioned thesis.}, keywords = {distribution networks, energy storage, power generation control, renewable energy sources, smart power grids, Grid ancillary service, RES, TSO, ancillary services, battery storage systems, distributed computational intelligence, distribution network, distribution network level, large-scale integration, renewables systems, storage systems, transmission network, transmission system operator, Computational intelligence, Economics, Generators, Linear programming, Optimization, Reactive power, Real-time systems, distributed optimization, reactive power control, smart grids}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969469}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969469}}, } @INPROCEEDINGS{hernandez-castro:2017:CEC, author={J. Hernandez-Castro and D. F. Barrero}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary generation and degeneration of randomness to assess the indepedence of the Ent test battery}, year={2017}, editor = {Jose A. Lozano}, pages = {1420--1427}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Randomness tests are a key tool to assess the quality of pseudo-random and true random (physical) number generators. They exploit some properties of random numbers to quantify to what extent the observed behavior of the tested sequence approximates the expected one. Given the many sides of randomness, there is not an unique test providing the whole picture, instead a suite of tests assessing different aspects randomness. A robust test suite must include independent tests, otherwise tests would assess the same property, providing redundant information. This paper addresses the independence assessment of a popular test suite named Ent. To this end we generate a large number of pseudo-random numbers with different degrees of randomness by evolving them with a Genetic Algorithm. The numbers are generated to maximize their diversity attending different criteria based on Ent output, used as fitness. We encourage diversity by maximizing and minimizing randomness measures. Once a diverse set of pseudo-random numbers is generated, the Ent test suite is run on them, and their statistics studied by means of a classical correlation analysis. The results show high correlation among some statistics used in the literature, which could be overestimating the quality of their randomness source.}, keywords = {genetic algorithms, random number generation, statistical testing, Ent independence assessment, Ent test battery, Ent test suite, correlation analysis, evolutionary degeneration, evolutionary generation, genetic algorithm, pseudorandom number generators, randomness tests, statistics, true random number generators, Batteries, Correlation, Entropy, NIST, Random sequences, Tools, Ent, Randomness, pseudo-random number generators, randomness sources, randomness test}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969470}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969470}}, } @INPROCEEDINGS{harel:2017:CEC, author={M. Harel and E. Matalon-Eisenstadt and A. Moshaiov}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving Multi-Objective Games using a-priori auxiliary criteria}, year={2017}, editor = {Jose A. Lozano}, pages = {1428--1435}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper describes a method to support strategy selection in zero-sum Multi-Objective Games (MOGs). It follows a recent development concerning the solution of MOGs based on a novel non-utility approach. Such an approach commonly results with a large set of rationalizable strategies to choose from. Here, this approach is further developed to narrow down the set of rationalizable strategies into a set of preferable strategies using a-priori incorporation of decision-makers' preferences (auxiliary criteria). To search for the latter set a co-evolutionary algorithm is devised. The effectiveness of the algorithm is studied using an academic example of a zero-sum MOG involving two manipulators. To test the algorithm, a validation method is suggested using a discrete version of the example. The results substantiate the claim that the proposed algorithm finds a good approximation of the set of preferable strategies.}, keywords = {evolutionary computation, game theory, a-priori auxiliary criteria, coevolutionary algorithm, novel nonutility approach, preferable strategies, rationalizable strategies, zero-sum MOG, zero-sum multiobjective games, Approximation algorithms, Games, Manipulators, Optimization, Sociology, Statistics, Pareto optimization, multi-crieteria decision-making, multi-objective game, multi-payoff game, non-cooperative game}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969471}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969471}}, } @INPROCEEDINGS{tvrdík:2017:CEC, author={J. Tvrdík and R. Poláková}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A simple framework for constrained problems with application of L-SHADE44 and IDE}, year={2017}, editor = {Jose A. Lozano}, pages = {1436--1443}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A simple framework for cooperation of evolutionary algorithms in the solution of constrained optimization problems is proposed and implemented using two advanced adaptive variants of differential evolution. The new algorithm is applied to the test problems defined for CEC 2017 competition on constrained single objective real-parameter optimization. The new algorithm finds acceptable solution in 75 % of test problems in the all tested dimensions with high efficiency. However, the algorithm is not able to find any feasible solution in the rest 25 % of the test problems. This issue is a field for next research.}, keywords = {evolutionary computation, optimisation, IDE, L-SHADE44, adaptive differential evolution, constrained single-objective real-parameter optimization, evolutionary algorithm cooperation, Benchmark testing, Electronic mail, Minimization, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969472}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969472}}, } @INPROCEEDINGS{lazreg:2017:CEC, author={M. B. Lazreg and M. Goodwin and O. C. Granmo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder}, year={2017}, editor = {Jose A. Lozano}, pages = {1444--1450}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to preprocess the non-standard spellings and autoencoder to derive the vector representation. The derived vectors encode word patterns and the Euclidean distance between the vectors represents a distance in the word space that challenges the prevailing edit distance. After training the autoencoder on 1051 different words and their non-standard versions, the results show that the new distance can be used to obtain the correct standard word among the closest five words in 89.53% of the cases compared to only 68.22% using the edit distance.}, keywords = {natural language processing, social networking (online), stochastic processes, text analysis, unsupervised learning, Euclidean distance, denoising autoencoder, dynamic time warping, nonstandard real vectors, nonstandard spellings, stochastic relationship, vector representation, word patterns, word space, Context, Hidden Markov models, Neural networks, Noise reduction, Speech recognition, Standards, Twitter}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969473}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969473}}, } @INPROCEEDINGS{castro:2017:CEC, author={O. R. Castro and R. Santana and J. A. Lozano and A. Pozo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Combining CMA-ES and MOEA/DD for many-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1451--1458}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-objective Estimation of Distribution Algorithms (MOEDAS) have been successfully applied to solve Multi-objective Optimization Problems (MOPs) since they are able to model dependencies between variables of the problem and then sample new solutions to guide the search to promising areas. A state-of-the-art optimizer for single-objective continuous functions that also uses probabilistic modeling is the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Different variants of CMA-ES have been proposed for MOPs however most of them are based on Pareto dominance as the main selection criterion. Recently, a new multi-objective CMA-ES called MOEA/D-CMA was proposed combining the strengths of CMA-ES with those of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Nowadays, however, researchers on MOEAs agree that combining Pareto and decomposition can be beneficial for the search on MOPs. As a result, a new MOEA has been proposed, called MOEA/DD. This algorithm modifies the MOEA/D by including a new Pareto dominance update mechanism that brings more diversity into the search. In this study, we extend the MOEA/D-CMA by replacing its update mechanism by the one of MOEA/DD. The hypothesis is that this update mechanism will improve the performance of MOEA/D-CMA as it improved MOEA/D. MOEA/D-CMA and MOEA/DD-CMA are implemented and evaluated through an experimental study. The experimental study involves two well-known families of benchmark problems whose objective numbers scale from two to fifteen. Then, an extensive statistical analysis of the results is made to extract sound, statistically supported conclusions about the performance of the algorithms as the number of objectives scales.}, keywords = {Pareto optimisation, covariance matrices, evolutionary computation, probability, statistical analysis, MOEA/D-CMA, MOEA/DD-CMA, MOEDAS, Pareto dominance update mechanism, covariance matrix adaptation evolution strategy, many-objective optimization, multiobjective CMA-ES, multiobjective estimation of distribution algorithms, multiobjective evolutionary algorithm based on decomposition, multiobjective optimization problems, probabilistic modeling, single-objective continuous functions, Adaptation models, Algorithm design and analysis, Optimization, Sociology, CMA-ES, Gaussian model, MOEA/D, dominance and decomposition, multi-objective}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969474}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969474}}, } @INPROCEEDINGS{mishra:2017:CEC, author={S. Mishra and S. Saha and S. Mondal}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Unsupervised method to ensemble results of multiple clustering solutions for bibliographic data}, year={2017}, editor = {Jose A. Lozano}, pages = {1459--1466}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the solutions are non-dominated with respect to each other. A single solution is selected by the decision maker according to his/her preference. But when the number of non-dominated solutions is large in number, then it is difficult for the decision maker to choose the one solution. The selection of a solution from the given Pareto front is known as Post-Pareto optimality analysis. In the past many approaches were proposed for solving the aforementioned problem, but most of these involve the decision maker. In this paper, we have proposed an approach to obtain a single solution from a set of non-dominated solutions by combining these solutions without the intervention of the decision maker. We have evaluated our approach on the set of solutions obtained after application of a newly developed multiobjective based clustering technique on bibliographic databases like DBLP.}, keywords = {Pareto optimisation, bibliographic systems, decision making, pattern clustering, Pareto based approach, bibliographic data, multiobjective optimization, multiple cluster quality, multiple clustering solutions, Clustering algorithms, Computers, Electronic mail, Genetic algorithms, Information services, Linear programming, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969475}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969475}}, } @INPROCEEDINGS{moritz:2017:CEC, author={R. L. Moritz and H. Zille and S. Mostaghim}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Elitism and aggregation methods in partial redundant evolutionary swarms solving a multi-objective tasks}, year={2017}, editor = {Jose A. Lozano}, pages = {1467--1473}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In evolutionary swarms adaptability and diversity are closely related concepts. In order to get a better understanding of their codependency we study a heterogeneous evolutionary multi-agent system with different rates of redundancy within the genetic material. The agents process a dynamic multi-objective task, where their genetic material defines their efficiency concerning the different objective functions of that task. One focus of this study is the influence of an elitist behavior performed by the agents during the evolutionary process, where an agent can decline the genetic material of another agent if it does not meet specific requirements. Further we analyze the impact of three different methods to aggregate the objective values into a single fitness value that is applicable for the evolutionary mechanism of the system. The results show that heterogeneity in the optimization behavior of the agents is very beneficial as it maintains a higher diversity in the system. The elitist behavior of the agents slows the evolutionary process but gives it a stronger pull towards qualitatively higher positions in the objective space. Indeed, the pace of the evolutionary process ultimately has a higher impact on the adaptability of the system than the amount of redundancy in the genetic information.}, keywords = {genetic algorithms, multi-agent systems, aggregation methods, elitist behavior, genetic material, heterogeneous evolutionary multiagent system, multiobjective tasks, partial redundant evolutionary swarms, Aggregates, Genetics, Optimization, Redundancy, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969476}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969476}}, } @INPROCEEDINGS{tyasnurita:2017:CEC, author={R. Tyasnurita and E. Özcan and R. John}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning heuristic selection using a Time Delay Neural Network for Open Vehicle Routing}, year={2017}, editor = {Jose A. Lozano}, pages = {1474--1481}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen `expert' hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier, i.e an `apprentice' hyper-heuristic, which is then used to solve the `unseen' problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances.}, keywords = {Taguchi methods, design of experiments, goods dispatch data processing, learning (artificial intelligence), neural nets, search problems, vehicle routing, TDNN, Taguchi orthogonal array, apprentice hyper-heuristic, classifier, expert hyper-heuristic, hidden pattern extraction, hyper-heuristic selection learning, low-level heuristic, low-level heuristics, open vehicle routing, search method, search process, time delay neural network, Delay effects, Design methodology, Machine learning algorithms, Neural networks, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969477}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969477}}, } @INPROCEEDINGS{camero:2017:CEC, author={A. Camero and J. Arellano-Verdejo and C. Cintrano and E. Alba}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Tile map size optimization for real world routing by using differential evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {1482--1488}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Finding the shortest path between two places is a well known problem in road traveling. While most of the work done up to this moment is focused on algorithmics, efficiently managing the information has received significantly less attention. Nevertheless, real world problems like road map routing present a challenge due to the impact that the immense size of the map has over the temporal complexity of the routing algorithms. In this work we propose a strategy for efficiently computing the shortest path in real road maps based on data managing: the tile map partitioning. To recreate a real scenario, we implemented a routing system and we tested our strategy using the road map of the Province of Málaga, Spain. Using a Differential Evolution we found the optimal tile size and prove that significant time reductions can be achieved by using the tile map partitioning.}, keywords = {evolutionary computation, optimisation, vehicle routing, Malaga province, Spain, data management, differential evolution, optimal tile size, road map routing, road traveling, shortest path problem, temporal complexity, tile map partitioning, tile map size optimization, time reductions, Algorithm design and analysis, Partitioning algorithms, Proposals, Roads, Routing, Sociology, Statistics, Heuristic algorithms, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969478}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969478}}, } @INPROCEEDINGS{biedrzyck:2017:CEC, author={R. Biedrzycki}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A version of IPOP-CMA-ES algorithm with midpoint for CEC 2017 single objective bound constrained problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1489--1494}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents the RB-IPOP-CMA-ES algorithm which is an enhanced version of IPOP-CMA-ES. The algorithm uses midpoint of the population as an approximation of the optimum. The midpoint fitness is also used to introduce a new restart trigger for IPOP. Other IPOP restart triggers and parameters are also corrected. The performance of the proposed approach is evaluated on 30 problems from the CEC 2017 benchmark for 10, 30, 50 and 100 dimensions. The results confirm that RB-IPOP-CMA-ES achieves better results than its version that does not utilize midpoint and is a considerable improvement over a plain IPOP-CMA-ES.}, keywords = {constraint theory, covariance matrices, evolutionary computation, CEC 2017 single objective bound constrained problems, IPOP restart triggers, RB-IPOP-CMA-ES algorithm, midpoint fitness, population midpoint, Approximation algorithms, Benchmark testing, Optimization, Sociology, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969479}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969479}}, } @INPROCEEDINGS{huang:2017:CEC, author={Szu-Yi Huang and Y. p. Chen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Proving theorems by using evolutionary search with human involvement}, year={2017}, editor = {Jose A. Lozano}, pages = {1495--1502}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The link between logic and computation has been established by the BHK interpretation and the Curry-Howard isomorphism, based on which proof assistants capable of verifying formal proofs by transforming proofs into programs and by computationally evaluating the programs have been developed for the past few decades. Because evolutionary algorithms are search methods with remarkable feasibility and can be used to automatically generate programs, in our previous proposal, evolutionary algorithms and proof assistants were integrated to create a framework able to automatically prove simple theorems. In the present work, we aim to enhance the search ability of the proof generator such that proofs of slightly advanced, complicated theorems can be generated via evolutionary search with human involvement. This article describes in detail the algorithmic design of the proposed proof generator, how and why humans are involved in the process of proof development, and the test runs, in which proofs as Coq formalization of three theorems, the divisibility rule for 3, the sum of an arithmetic series, and the inequality of arithmetic and geometric means, were successfully generated. The developed source code with the obtained experimental results, including the human created rules and the software generated proofs, are released as open source.}, keywords = {evolutionary computation, search problems, theorem proving, BHK interpretation, Coq formalization, Curry-Howard isomorphism, arithmetic series sum, arithmetic-and-geometric means inequality, automatic program generation, divisibility rule, evolutionary algorithms, evolutionary search, human created rules, human involvement, proof assistants, software generated proofs, source code, Algorithm design and analysis, Computers, Generators, Sociology, Software, Statistics, Coq, Proof generator, automatic theorem proving, evolutionary algorithm, proof assistant}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969480}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969480}}, } @INPROCEEDINGS{ravber:2017:CEC, author={M. Ravber and M. Mernik and M. Črepinšek}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Ranking Multi-Objective Evolutionary Algorithms using a chess rating system with Quality Indicator ensemble}, year={2017}, editor = {Jose A. Lozano}, pages = {1503--1510}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary Algorithms have been applied successfully for solving real-world multi-objective problems which explains the influx of newly proposed Multi-Objective Evolutionary Algorithms (MOEAs). In order to determine their performance, comparison with existing algorithms must be conducted. However, conducting a comparison is not a trivial task. Benchmark functions must be selected and the results have to be analyzed using a statistical method. In addition, the results of MOEAs can be evaluated with different Quality Indicators (QIs), which aggravates the comparison additionally. In this paper, we present a chess rating system which was adapted for ranking MOEAs with a Quality Indicator ensemble. The ensemble ensures that different aspects of quality are evaluated of the resulting approximation sets. The chess rating system is compared with an existing method which uses a double-elimination tournament and a quality indicator ensemble. Experimental results show that the chess rating system achieved similar rankings with fewer runs of MOEAs.}, keywords = {algorithm theory, evolutionary computation, MOEA, QI, benchmark functions, chess rating system, double-elimination tournament, multiobjective evolutionary algorithms, quality indicator ensemble, real-world multiobjective problems, statistical method, Approximation algorithms, Computer science, Electrical engineering, Electronic mail, Pareto optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969481}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969481}}, } @INPROCEEDINGS{dhief:2017:CEC, author={I. Dhief and N. H. Dougui and D. Delahaye and N. Hamdi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A new trans-Atlantic route structure for strategic flight planning over the NAT airspace}, year={2017}, editor = {Jose A. Lozano}, pages = {1511--1518}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Air traffic across the North Atlantic airspace has witnessed an incessant increase over the last decades. However, the efficiency of trans-Atlantic air traffic management is still low nowadays due to the limited radar coverage. Automated Dependent Surveillance-Broadcast systems represents an opportunity to enhance the strategic flight planning over the oceans by reducing separation standards between aircraft. Besides, the strong winds present a challenge for oceanic flights. Therefore, flying on the wind-optimal routes will save significantly both fuel and time. In this paper, we propose a new trans-Atlantic route structure that benefits from the jetstreams in order to construct wind-optimal flight trajectories. Then, we introduce an optimization model for detecting and resolving conflicts. The analysis is carried out on real traffic data to prove the efficiency of the proposed method. Experimental findings show an improvement in terms of conflict resolution and induced delays.}, keywords = {air traffic, aircraft, optimisation, vehicle routing, NAT airspace, North Atlantic airspace, automated dependent surveillance-broadcast systems, conflict resolution, induced delays, jetstreams, oceanic flights, optimization model, radar coverage, separation standards, strategic flight planning, trans-Atlantic air traffic management, trans-Atlantic route structure, wind-optimal flight trajectories, wind-optimal routes, Air traffic control, Aircraft manufacture, Atmospheric modeling, Radar tracking, Standards, Trajectory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969482}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969482}}, } @INPROCEEDINGS{ortega:2017:CEC, author={C. Ortega and M. Vasile}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={New heuristics for multi-objective worst-case optimization in evidence-based robust design}, year={2017}, editor = {Jose A. Lozano}, pages = {1519--1526}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a non-nested algorithm for the solution of multi-objective min-max problems (MOMMP) in worst-case optimization. The algorithm has been devised for evidence-based robust optimization, where the lack of a defined probabilistic behaviour of the uncertain parameters makes it impossible to apply sample-based techniques and forces the designer to identify the worst case over the subdomains of the uncertainty space. In evidence theory, the robustness of the solutions is measured in terms of the Belief in the realization of the value of the design budgets, which acts as a lower bound to the unknown cumulative distribution function of the budget. Thus a means of finding robust solutions in preliminary design consists on applying the minimax model, where the worst-case budget over the uncertainty space is optimized over the control space. The paper proposes a novel heuristic to solve MOMMP and demonstrates its capability to approximate the worst-case Pareto front at a very reduced cost with respect to approaches based on nested optimization.}, keywords = {Pareto optimisation, approximation theory, cost reduction, minimax techniques, sampling methods, statistical distributions, uncertainty handling, MOMMP, case budget, control space, cumulative distribution function, design budgets, evidence theory, evidence-based robust design, evidence-based robust optimization, minimax model, multiobjective min-max problems, multiobjective worst-case optimization, nested optimization, new heuristics, nonnested algorithm, probabilistic behaviour, sample-based techniques, uncertain parameters, uncertainty space, worst-case Pareto front approximation, Algorithm design and analysis, Gold, Minimization, Modeling, Optimization, Robustness, Uncertainty}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969483}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969483}}, } @INPROCEEDINGS{peng:2017:CEC, author={X. Peng and Yapei Wu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Large-scale cooperative co-evolution with bi-objective selection based imbalanced multi-modal optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1527--1532}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Cooperative co-evolutionary algorithm (CC) which runs in a divide-and-conquer manner is effective to solve large-scale global optimization (LSGO) problems. Multi-modal optimization (MMO) intends to locate multiple optimal solutions. Using MMO methods in CC algorithm would be beneficial, because MMO optimizer can provide more information about the landscapes. In this paper, a bi-objective selection is proposed to introduce imbalance among the subpopulations of a MMO optimizer. Only the highly representative subpopulations will be active and evolved in the MMO procedure. With this imbalanced MMO technique, the CC's subcomponents could obtain sufficient coevolutionary information (multiple optima) from each other. In addition, more computational resources could be saved and used in CC procedure. Experiments and statistical comparisons are conducted on LSGO benchmark functions to verify the effectiveness of the proposed method. The results indicate that the proposed algorithm significantly outperforms seven state-of-the-art large-scale CC algorithms.}, keywords = {divide and conquer methods, evolutionary computation, optimisation, statistical analysis, CC algorithm, CC subcomponents, LSGO benchmark functions, LSGO problems, MMO optimizer, biobjective selection, coevolutionary information, cooperative coevolutionary algorithm, divide-and-conquer manner, imbalanced multimodal optimization, large-scale cooperative coevolution, large-scale global optimization problems, statistical comparisons, Benchmark testing, Context, Heuristic algorithms, Optimization, Sociology, Statistics, Cooperative Co-evolutionary, Multi-Modal Optimization, bi-objective selection, large-scale optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969484}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969484}}, } @INPROCEEDINGS{grochol:2017:CEC, author={D. Grochol and L. Sekanina}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective evolution of hash functions for high speed networks}, year={2017}, editor = {Jose A. Lozano}, pages = {1533--1540}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Hashing is a critical function in capturing and analysis of network flows as its quality and execution time influences the maximum throughput of network monitoring devices. In this paper, we propose a multi-objective linear genetic programming approach to evolve fast and high-quality hash functions for common processors. The search algorithm simultaneously optimizes the quality of hashing and the execution time. As it is very time consuming to obtain the real execution time for a candidate solution on a particular processor, the execution time is estimated in the fitness function. In order to demonstrate the superiority of the proposed approach, evolved hash functions are compared with hash functions available in the literature using real-world network data.}, keywords = {genetic algorithms, genetic programming, cryptography, critical function, fitness function, hash functions, hashing, high speed networks, multiobjective evolution, multiobjective linear genetic programming, network flows, network monitoring devices, real-world network data, search algorithm, Hardware, Monitoring, Program processors, Registers}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969485}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969485}}, } @INPROCEEDINGS{chugh:2017:CEC, author={T. Chugh and K. Sindhya and K. Miettinen and Yaochu Jin and T. Kratky and P. Makkonen}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system}, year={2017}, editor = {Jose A. Lozano}, pages = {1541--1548}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. From the set of nondominated solutions generated by K-RVEA, a decision maker having substance knowledge selected the final one based on his preferences. The final selected solution has better objective function values compared to the baseline solution of the initial design. A comparison of solutions with K-RVEA and RVEA (which does not use surrogates) is also performed to show the potential of using surrogates.}, keywords = {digital simulation, evolutionary computation, intake systems (machines), mechanical engineering computing, optimisation, ventilation, K-RVEA, air intake ventilation system, computationally expensive objective functions, objective function values, real-world industrial problem, simulation tools, surrogate-assisted evolutionary multiobjective shape optimization, Hydraulic systems, Linear programming, Numerical models, Optimization, Resistance, Software}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969486}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969486}}, } @INPROCEEDINGS{li:2017:CECj, author={X. Li and Huiyan Yang and Meihua Yang and Xian Yang and G. Yang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Accelerating artificial bee colony algorithm with neighborhood search}, year={2017}, editor = {Jose A. Lozano}, pages = {1549--1556}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we integrate variable neighborhood search (VNS) into artificial bee colony (ABC) algorithm so that the search ability under variable neighborhood structures and local search is accelerated. Two VNS methods, including the reduced VNS and basic VNS, are integrated to develop different versions. The proposed algorithm, named variable neighborhood ABC (VNABC), is verified in a comprehensive set of benchmark functions. The experimental results confirm that VNABC outperforms the state-of-the-art ABC and differential evolution (DE) algorithms in terms of the convergence speed.}, keywords = {convergence, evolutionary computation, search problems, DE, VNABC, VNS, artificial bee colony algorithm, benchmark functions, convergence speed, differential evolution algorithms, local search, variable neighborhood ABC, variable neighborhood search, Acceleration, Genetic algorithms, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969487}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969487}}, } @INPROCEEDINGS{assunção:2017:CEC, author={F. Assunção and N. Lourenço and P. Machado and B. Ribeiro}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Automatic generation of neural networks with structured Grammatical Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {1557--1564}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.}, keywords = {grammars, neural nets, pattern classification, ANN, SGE, artificial neural network automatic generation, classification datasets, grammar-based methods, structured grammatical evolution, Artificial neural networks, Biological neural networks, Decoding, Encoding, Grammar, Network topology, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969488}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969488}}, } @INPROCEEDINGS{li:2017:CECk, author={Peng Li and Y. Yang and Xiying Du and Xinghua Qu and K. Wang and B. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Iterated local search for distributed multiple assembly no-wait flowshop scheduling}, year={2017}, editor = {Jose A. Lozano}, pages = {1565--1571}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Driven by the pressing needs in coordinating and synchronizing multi-plant facilities for efficient production and manufacturing, distributed assembly permutation flowshop scheduling problem (DAPFSP) has been becoming the focus of concern of evolutionary computing and operations research, which is a typical NP-hard combinatorial optimization problem. In this paper, we propose a novel generalized version of DAPFSP, where multiple assembly factories exist rather than only one assembly factory in the conventional DAPFSP, meanwhile no-wait constraint exists in the processing stage. We name this new model as the distributed multiple assembly permutation flowshop scheduling problem with no-wait, abbreviated as DMAPFSP-NW. We propose hybrid iterated local search with simulated annealing (ILS-SA) for the proposed scheduling model. Simulation results based on 27 large-scale benchmark problems show that our proposed ILS-SA can effectively solve the DMAPFSP-NW.}, keywords = {combinatorial mathematics, computational complexity, flow shop scheduling, iterative methods, production facilities, search problems, simulated annealing, DAPFSP, DMAPFSP-NW, ILS-SA, NP-hard combinatorial optimization problem, distributed assembly permutation flowshop scheduling problem, distributed multiple assembly no-wait flowshop scheduling, distributed multiple assembly permutation flowshop scheduling problem-with-no-wait, evolutionary computing, hybrid iterated local search-with-simulated annealing, large-scale benchmark problems, multiplant facilities coordination, multiplant facilities synchronization, multiple assembly factories, no-wait constraint, operations research, processing stage, Job shop scheduling, Processor scheduling, assembly scheduling, combinatorial optimization, distributed assembly permutation flowshop problem, iterated local search}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969489}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969489}}, } @INPROCEEDINGS{le:2017:CEC, author={N. Le and M. O'Neill and D. Fagan and A. Brabazon}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Social Grammatical Evolution with imitation learning for real-valued function estimation}, year={2017}, editor = {Jose A. Lozano}, pages = {1572--1578}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Drawing on a rich literature concerning social learning in animals, this paper presents a variation of Grammatical Evolution (GE) which incorporates one of the most powerful forms of social learning, namely imitation learning. This replaces the traditional method of `communication' between individuals in GE - crossover - which is drawn from an evolutionary metaphor. The paper provides an introduction to social learning, describes the proposed variant of GE, and tests on a series of benchmark symbolic regression problems. The results obtained are encouraging, being very competitive when compared with canonical GE. It is noted that the literature on social learning provides a number of useful meta-frameworks which can be used in the design of new search algorithms and to allow us to better understand the strengths and weaknesses of existing algorithms. Future work is indicated in this area.}, keywords = {grammars, regression analysis, social sciences, imitation learning, real-valued function estimation, social grammatical evolution, social learning, symbolic regression, Algorithm design and analysis, Animals, Benchmark testing, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969490}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969490}}, } @INPROCEEDINGS{ayodele:2017:CEC, author={M. Ayodele and J. McCall and O. Regnier-Coudert}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Estimation of distribution algorithms for the Multi-Mode Resource Constrained Project scheduling problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1579--1586}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary computation community as they are more complex to solve. Estimation of Distribution Algorithms (EDAs) generate solutions by sampling a probabilistic model that captures key features of good solutions. Often they can significantly improve search efficiency and solution quality. Previous research has shown that the mode assignment subproblem can be more effectively solved with an EDA. Also, a competitive Random Key based EDA (RK-EDA) for permutation problems has recently been proposed. In this paper, activity and mode solutions are respectively generated using the RK-EDA and an integer based EDA. This approach is competitive with leading approaches of solving the MRCPSP.}, keywords = {evolutionary computation, probability, project management, resource allocation, sampling methods, scheduling, stochastic programming, MRCPSP, RK-EDA, activity scheduling, activity solutions, competitive random key based EDA, estimation of distribution algorithms, evolutionary computation community, integer based EDA, mode assignment, mode solutions, multicomponent problem, multimode resource constrained project scheduling problem, permutation problems, probabilistic model sampling, search efficiency, solution quality, Estimation, Genetic algorithms, Probabilistic logic, Processor scheduling, Schedules, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969491}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969491}}, } @INPROCEEDINGS{baioletti:2017:CEC, author={M. Baioletti and A. Milani and V. Santucci}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Algebraic Particle Swarm Optimization for the permutations search space}, year={2017}, editor = {Jose A. Lozano}, pages = {1587--1594}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Particle Swarm Optimization (PSO), though being originally introduced for continuous search spaces, has been increasingly applied to combinatorial optimization problems. In particular, we focus on the PSO applications to permutation problems. As far as we know, the most popular PSO variants that produce permutation solutions are those based on random key techniques. In this paper, after highlighting the main criticalities of the random key approach, we introduce a totally discrete PSO variant for permutation-based optimization problems. The proposed algorithm, namely Algebraic PSO (APSO), simulates the original PSO design in permutations search space. APSO directly represents the particle positions and velocities as permutations. The APSO search scheme is based on a general algebraic framework for combinatorial optimization previously, and successfully, introduced in the context of discrete differential evolution schemes. The particularities of the PSO design scheme arouse new challenges for the algebraic framework: the non-commutativity of the velocity terms, and the rationale behind the PSO inertial move. Design solutions have been proposed for both the issues, and two APSO variants are provided. Experiments have been held to compare the performances of the APSO schemes with respect to the random key based PSO schemes in literature. Widely adopted benchmark instances of four popular permutation problems have been considered. The experimental results clearly show, with high statistical evidence, that APSO outperforms its competitors.}, keywords = {algebra, combinatorial mathematics, evolutionary computation, particle swarm optimisation, search problems, APSO search scheme, PSO inertial move, PSO variants, algebraic PSO, algebraic particle swarm optimization, combinatorial optimization problems, continuous search spaces, discrete differential evolution schemes, general algebraic framework, permutation-based optimization problems, permutations search space, random key based PSO schemes, random key techniques, statistical evidence, velocity terms, Algorithm design and analysis, Decoding, Optimization, Particle swarm optimization, Sociology, Statistics, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969492}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969492}}, } @INPROCEEDINGS{hartwig:2017:CEC, author={L. Hartwig and D. Bestle}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Compressor blade design for stationary gas turbines using dimension reduced surrogate modeling}, year={2017}, editor = {Jose A. Lozano}, pages = {1595--1602}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Automated compressor blade design requires solution of a high-dimensional optimization problem involving several evaluation tools to account for both aerodynamic and structural aspects. However, the computational complexity of CFD and FEM simulations is rather high, and time scales for the evaluations may be rather different. Both problems can be overcome by using surrogate modeling where the expensive simulations are replaced by computationally cheap approximation model and design evaluation is decoupled from the optimization loop. A generic design process structure for multi-disciplinary optimization is introduced, which makes use of surrogate modeling to mitigate time restrictions and reduce unnecessary waiting times for the independent sub-processes. Partial least squares (PLS) in combination with Kriging is investigated as a measure to reduce dimensionality and multi-collinearity, which thereby reduces the time for building up the surrogate model to speed up the design process. This is demonstrated by two well-proven optimization test functions and also an application to a real compressor blade optimization for stationary gas turbines.}, keywords = {aerodynamics, blades, compressors, computational fluid dynamics, gas turbines, structural engineering computing, Automated compressor blade design, CFD, FEM simulations, Kriging, PLS, aerodynamic aspects, approximation model, compressor blade optimization, computational complexity, design evaluation, dimension reduced surrogate modeling, dimensionality, evaluation tools, generic design process structure, high-dimensional optimization problem, multicollinearity, multidisciplinary optimization, optimization loop, optimization test functions, partial least squares, stationary gas turbines, time restrictions, Computational modeling, Databases, Mathematical model, Optimization, Turbines}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969493}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969493}}, } @INPROCEEDINGS{hamada:2017:CEC, author={N. Hamada and K. Chiba}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Knee point analysis of many-objective Pareto fronts based on Reeb graph}, year={2017}, editor = {Jose A. Lozano}, pages = {1603--1612}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In many-objective optimization, the dimensionality of Pareto fronts becomes higher than three, and extracting preferable points for the decision maker is a key issue in the post-optimal analysis. The aim of this study is to develop a method to detect and visualize high-dimensional knee points. We propose a new definition of knee point and a graph-based approach to detect our knee points with a visualization of the geometry of the whole Pareto front. Our method is examined via Pareto front samples of synthetic problems and a real-world airplane design.}, keywords = {Pareto optimisation, decision making, graph theory, Pareto front dimensionality, Pareto front geometry visualization, Reeb graph, airplane design, graph-based approach, high-dimensional knee point detection, high-dimensional knee point visualization, knee point analysis, many-objective Pareto fronts, many-objective optimization, post-optimal analysis, Airplanes, Electronic mail, Geometry, Informatics, Measurement, Optimization, Visualization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969494}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969494}}, } @INPROCEEDINGS{grudniewski:2017:CEC, author={P. A. Grudniewski and A. J. Sobey}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Level Selection Genetic Algorithm applied to CEC '09 test instances}, year={2017}, editor = {Jose A. Lozano}, pages = {1613--1620}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural selection. They are applied in many areas of engineering and industry, on increasingly complex problems. To improve the performance, the new algorithms have a tendency to be derived from sophisticated mathematical and computational mechanisms, where many biological and evolutionary advances have been neglected. One such mechanism is multi-level selection theory which has been proposed as being necessary for evolution. Previously, an algorithm developed using this theory as its inspiration has shown promising performance on simple test problems. It proposes the addition of a collective reproduction mechanism alongside the standard individual one. Here the algorithm, Multi-Level Selection Genetic Algorithm (MLSGA), is benchmarked on more sophisticated test instances from CEC '09 and compared to the final rankings. In this instance a simple genetic algorithm is used at the individual level. The developed algorithm cannot compete with top algorithms on complex unconstrained problems, however it shows interesting results and behaviour, and better performance on constrained test functions. The approach provides promise for further investigation, especially in integrating state-of-the-art individual reproduction methods to improve the performance and improving the novel collective mechanism.}, keywords = {genetic algorithms, CEC '09 test instances, MLSGA, collective reproduction mechanism, individual reproduction methods, multilevel selection genetic algorithm, multilevel selection theory, natural selection, population-based optimisation tools, simple genetic algorithm, sophisticated mathematical computational mechanisms, Benchmark testing, Heuristic algorithms, Linear programming, Optimization, Sociology, Statistics, Evolutionary theory, evolutionary computation, multi-level selection, multi-objective optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969495}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969495}}, } @INPROCEEDINGS{landa-torres:2017:CEC, author={I. Landa-Torres and J. L. Lobo and I. Murua and D. Manjarres and J. Del Ser}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective heuristics applied to robot task planning for inspection plants}, year={2017}, editor = {Jose A. Lozano}, pages = {1621--1628}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators.}, keywords = {inspection, optimal control, path planning, search problems, service robots, travelling salesman problems, CR performance indicators, HV, MOHS, TSP, coverage rate, hypervolume, inspection plants, mission planning, multiobjective harmony search algorithm, multiobjective heuristics, random keys encoding strategy, resource allocation, robot task planning, robotic resources, stringent operational conditions, traveling salesman problem, Batteries, Job shop scheduling, Processor scheduling, Robot kinematics, Schedules}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969496}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969496}}, } @INPROCEEDINGS{ceberio:2017:CEC, author={J. Ceberio and A. Mendiburu and J. A. Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A square lattice probability model for optimising the Graph Partitioning Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1629--1636}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Estimation of Distribution Algorithms have proved to be very competitive for solving combinatorial and continuous optimisation problems. However, there are problems for which they have not been extensively developed: we refer to constrained optimisation problems. Existing proposals approach these problems by (i) modifying the sampling strategy of the probabilistic model to allow feasible solutions or (ii) adopting general approaches used in the context of heuristic optimisation such as penalisation. Nonetheless, from a theoretical point of view, little progress have been given in the context of EDAs when developing algorithms designed specifically to solve constrained problems. In this paper, we propose developing EDAs by introducing probability models defined exclusively on the space of feasible solutions. In this sense, we give a first approach by taking the Graph Partitioning Problem (GPP) as a case of study, and present a probabilistic model defined exclusively on the feasible region of solutions: a square lattice probability model. The experiments conducted on a benchmark of 22 artificial instances confirm the effectiveness of the proposal in terms of quality of solutions and execution time.}, keywords = {graph theory, lattice theory, probability, sampling methods, stochastic programming, GPP, combinatorial problems, constrained optimisation problems, continuous optimisation problems, estimation of distribution algorithms, general approaches, graph partitioning problem, heuristic optimisation, sampling strategy, square lattice probability model, Algorithm design and analysis, Context, Context modeling, Estimation, Lattices, Optimization, Probabilistic logic}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969497}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969497}}, } @INPROCEEDINGS{cheng:2017:CECa, author={Shi Cheng and Yifei Sun and Junfeng Chen and Quande Qin and Xianghua Chu and Xiujuan Lei and Yuhui Shi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A comprehensive survey of brain storm optimization algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {1637--1644}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The development, implementation, variant, and future directions of a new swarm intelligence algorithm, brain storm optimization (BSO) algorithm, are comprehensively surveyed. Brain storm optimization algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. To the best of our knowledge, there are 75 papers, 8 theses, and 5 patents in total on the development and application of the BSO algorithm. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Based on the developments of brain storm optimization algorithms, different kinds of optimization problems and real-world applications could be solved.}, keywords = {optimisation, swarm intelligence, BSO algorithm, brain storm optimization algorithms, convergent operation, divergent operation, human brainstorming process, objective space, search space, swarm intelligence algorithm, Algorithm design and analysis, Clustering algorithms, Conferences, Optimization, Particle swarm optimization, Signal processing algorithms, Storms, Brain storm optimization, Developmental swarm intelligence}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969498}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969498}}, } @INPROCEEDINGS{ceberio:2017:CECa, author={J. Ceberio and A. Mendiburu and J. A. Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Are we generating instances uniformly at random?}, year={2017}, editor = {Jose A. Lozano}, pages = {1645--1651}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In evolutionary computation, it is common practice to use sets of instances as test-beds for evaluating and comparing the performance of new optimisation algorithms. In some cases, real-world instances are available, and, thus, they are used to constitute the experimental benchmark. Unfortunately, this is not the general case. Due to the difficulties for obtaining real-world instances, or because the optimisation problems defined in the literature are not exactly as those defined in the industry, practitioners are forced to create artificial instances. In this paper, we study some aspects related to the random generation of artificial instances. Particularly, we elaborate on the assumption that states that sampling uniformly at random in the space of parameters is equivalent to sampling uniformly at random in the space of functions. Illustrated with some experiments, we prove that for some type of algorithms this assumption does not hold.}, keywords = {evolutionary computation, optimisation algorithms, Benchmark testing, Heuristic algorithms, Industries, Linear programming, Optimization, Search problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969499}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969499}}, } @INPROCEEDINGS{mistry:2017:CEC, author={K. Mistry and L. Zhang and G. Sexton and Yifeng Zeng and Mengda He}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Facial expression recongition using firefly-based feature optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1652--1658}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Automatic facial expression recognition plays an important role in various application domains such as medical imaging, surveillance and human-robot interaction. This research proposes a novel facial expression recognition system with modified Local Gabor Binary Patterns (LGBP) for feature extraction and a firefly algorithm (FA) variant for feature optimization. First of all, in order to deal with illumination changes, scaling differences and rotation variations, we propose an extended overlap LGBP to extract initial discriminative facial features. Then a modified FA is proposed to reduce the dimensionality of the extracted facial features. This FA variant employs Gaussian, Cauchy and Levy distributions to further mutate the best solution identified by the FA to increase exploration in the search space to avoid premature convergence. The overall system is evaluated using three facial expression databases (i.e. CK+, MMI, and JAFFE). The proposed system outperforms other heuristic search algorithms such as Genetic Algorithm and Particle Swarm Optimization and other existing state-of-the-art facial expression recognition research, significantly.}, keywords = {Gabor filters, Gaussian distribution, face recognition, feature extraction, optimisation, Cauchy distributions, Gaussian distributions, LGBP, Levy distributions, automatic facial expression recognition, discriminative facial features, facial features dimensionality, firefly algorithm, firefly-based feature optimization, human-robot interaction, illumination changes, local Gabor binary patterns, medical imaging, rotation variations, scaling differences, surveillance, Algorithm design and analysis, Brightness, Facial features, Genetic algorithms, Optimization, facial expression recognition, feature selection, firefly optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969500}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969500}}, } @INPROCEEDINGS{martín:2017:CEC, author={A. Martín and F. Fuentes-Hurtado and V. Naranjo and D. Camacho}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Deep Neural Networks architectures for Android malware classification}, year={2017}, editor = {Jose A. Lozano}, pages = {1659--1666}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Deep Neural Networks (DNN) have become a powerful, widely used, and successful mechanism to solve problems of different nature and varied complexity. Their ability to build models adapted to complex non-linear problems, have made them a technique widely applied and studied. One of the fields where this technique is currently being applied is in the malware classification problem. The malware classification problem has an increasing complexity, due to the growing number of features needed to represent the behaviour of the application as exhaustively as possible. Although other classification methods, as those based on SVM, have been traditionally used, the DNN pose a promising tool in this field. However, the parameters and architecture setting of these DNNs present a serious restriction, due to the necessary time to find the most appropriate configuration. This paper proposes a new genetic algorithm designed to evolve the parameters, and the architecture, of a DNN with the goal of maximising the malware classification accuracy, and minimizing the complexity of the model. This model is tested against a dataset of malware samples, which are represented using a set of static features, so the DNN has been trained to perform a static malware classification task. The experiments carried out using this dataset show that the genetic algorithm is able to select the parameters and the DNN architecture settings, achieving a 91% accuracy.}, keywords = {genetic algorithms, invasive software, neural nets, pattern classification, smart phones, Android malware classification, DNN architecture settings, complexity minimization, deep neural networks architectures, genetic algorithm, malware samples, static malware classification task, Biological neural networks, Complexity theory, Computer architecture, Malware, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969501}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969501}}, } @INPROCEEDINGS{vijay:2017:CEC, author={R. Vijay and S. J. Nanda}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Declustering of an earthquake catalog based on ergodicity using parallel Grey Wolf Optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1667--1674}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The declustering of an earthquake catalog identify the background events (seismic events generated by regular earth movements), which leads to an unbiased estimation of seismic activities in a region. The ergodicity of a seismic region represents the ensemble average of events in time and space. The ergodicity of a seismic catalog is represented by a Thirumalai-Mountain (TM) metric. If the inverse TM metric becomes linear with time then the catalog is assumed to be declustered (it contains only the background events). But the original catalog normally contains backgrounds as well as seismically triggered events (Foreshocks and Aftershocks). The objective here is to optimally remove the triggered events from the catalog with an optimization algorithm so that the remaining catalog contains only the backgrounds. Here a parallel Grey Wolf Optimization (P-GWO) is introduced to perform the optimization task. Compared to the original GWO here the new updated positions of wolves are computed in parallel which reduces the computational complexity of the algorithm keeping the same accuracy level. The analysis is carried out on Southern California catalog and the results obtained are superior to that achieved by Cho et al. using PSO in 2010. Comparative results also demonstrate better performance over three benchmark statistical de-clustering methods by Gardner-Knopoff, Uhrhammer and Reseanberg.}, keywords = {computational complexity, earthquakes, geophysics computing, optimisation, pattern clustering, seismology, Southern California catalog, Thirumalai-Mountain metric, earthquake catalog declustering, inverse TM metric, parallel Grey Wolf Optimization, seismic activities, seismic catalog ergodicity, seismically triggered events, Algorithm design and analysis, Mathematical model, Measurement, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969502}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969502}}, } @INPROCEEDINGS{cózar:2017:CEC, author={J. Cózar and L. delaOssa and J. A. Gámez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Generation of first-order TSK rules based on the apriori + search approach}, year={2017}, editor = {Jose A. Lozano}, pages = {1675--1682}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this work, we propose several algorithms for learning first-order TSK fuzzy rules. These methods, consist of two stages: first, they generate a set of candidate rules with an adaptation of the apriori algorithm for frequent itemset detection. Then, they select a subset of such rules, generally by means of a search algorithm. In this work we have tested a genetic and two different local search algorithms. The results obtained show that, genetic algorithms tends to converge to systems with a higher number of rules, which minimize the training error, but also overfit. On the other hand, local search gets stuck in configurations with fewer rules which, despite producing a higher training error, avoid overfitting and lead to best results in terms of error.}, keywords = {fuzzy reasoning, fuzzy set theory, genetic algorithms, learning (artificial intelligence), minimisation, search problems, Takagi-Sugeno-Kang rules, apriori algorithm, first-order TSK fuzzy rule learning, first-order TSK rule generation, frequent itemset detection, local search algorithms, search algorithm, training error minimization, Fuzzy sets, Input variables, Partitioning algorithms, Pragmatics, Prediction algorithms, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969503}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969503}}, } @INPROCEEDINGS{polákov:2017:CEC, author={R. Poláková}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={L-SHADE with competing strategies applied to constrained optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1683--1689}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={An enhanced version of L-SHADE algorithm has been proposed recently. The LSHADE44 algorithm uses three different additional strategies for computing a trial point and employed strategies compete in the algorithm. The algorithm was originally developed for bound-constrained optimization. In this paper, the LSHADE44 algorithm was slightly simplified and modified to be able to solve constrained problems. The benchmark set arranged for CEC2017 competition on constrained real-parameter optimization is employed.}, keywords = {optimisation, CEC2017 competition, L-SHADE algorithm, LSHADE44 algorithm, bound-constrained optimization, constrained real-parameter optimization, Benchmark testing, Electronic mail, Optimization, Robustness, Search problems, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969504}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969504}}, } @INPROCEEDINGS{feoktistov:2017:CEC, author={V. Feoktistov and S. Pietravalle and N. Heslot}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal experimental design of field trials using Differential Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {1690--1696}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={When setting up field experiments, to test and compare a range of genotypes (e.g. maize hybrids), it is important to account for any possible field effect that may otherwise bias performance estimates of genotypes. To do so, we propose a model-based method aimed at optimizing the allocation of the tested genotypes and checks between fields and placement within field, according to their kinship. This task can be formulated as a combinatorial permutation-based problem. We used Differential Evolution concept to solve this problem. We then present results of optimal strategies for between-field and within-field placements of genotypes and compare them to existing optimization strategies, both in terms of convergence time and result quality. The new algorithm gives promising results in terms of convergence and search space exploration.}, keywords = {biology, combinatorial mathematics, evolutionary computation, combinatorial permutation, differential evolution, differential evolution concept, field trials, optimal experimental design, optimal strategies, optimization strategies, search space exploration, tested genotypes, Algorithm design and analysis, Convergence, Covariance matrices, Linear programming, Mathematical model, Optimization, Space exploration, breeding trials, combinatorial, experimental design, permutation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969505}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969505}}, } @INPROCEEDINGS{witten:2017:CEC, author={M. Witten and O. Clancey}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Residual dose deviation differential histogram analysis using evolutionary-optimized transform parameters for dose distribution warping in patient-specific quality assurance in external beam radiation therapy}, year={2017}, editor = {Jose A. Lozano}, pages = {1697--1703}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Patient-specific quality assurance in external beam radiation therapy is performed to ensure the safe and effective administration of ionizing radiation to patients. This individualized quality assurance process often involves the measurement of a planar radiation dose distribution obtained during irradiation of a surrogate phantom, in order to sample the actual delivered doses to the patient; this measured dose distribution is then compared with that calculated by the radiation therapy treatment planning software. The current work, a continuation of the authors' previous efforts, uses a covariance matrix adaption-evolutionary strategy approach to optimize transform parameters of an affine transformation which includes normalization, translation, dilation (or erosion), roll, pitch, and yaw, such that the application of the transformation warps the measured dose distribution to the planned dose distribution. The deviations, from unity, of the transformation parameters associated with translations and with scaling, and the deviations, from zero, of those associated with rotations, are a means of assessing possible errors in experimental setup, as well as possible errors in the dose calculation algorithm used in the treatment planning system. The optimization selects a Pareto-optimal set of eight transformation parameters that minimizes the difference, in the sense of the sum of least squares, between the doses at distinct points in the calculated and measured dose distributions. The method was tested using four distinct treatment plans, and the results indicate that it may prove useful in the clinic.}, keywords = {Pareto optimisation, affine transforms, covariance matrices, evolutionary computation, least squares approximations, medical computing, quality assurance, radiation therapy, Pareto-optimal set optimization, affine transformation, covariance matrix adaption-evolutionary strategy, dose calculation algorithm, error assessment, evolutionary-optimized transform parameters, external beam radiation therapy, patient ionizing radiation administration, patient-specific quality assurance, planar radiation dose distribution measurement, radiation therapy treatment planning software, residual dose deviation differential histogram analysis, sum of least squares, surrogate phantom irradiation, transform parameter optimization, transformation warps, treatment planning system, treatment plans, Biomedical applications of radiation, Ions, Phantoms, Planning, Radiation effects, Software, covariance matrix adaptation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969506}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969506}}, } @INPROCEEDINGS{wagner:2017:CEC, author={M. Wagner and T. Friedrich and M. Lindauer}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving local search in a minimum vertex cover solver for classes of networks}, year={2017}, editor = {Jose A. Lozano}, pages = {1704--1711}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={For the minimum vertex cover problem, a wide range of solvers has been proposed over the years. Most classical exact approaches are encountering run time issues on massive graphs that are considered nowadays. A straightforward alternative approach is then to use heuristics, which make assumptions about the structure of the studied graphs. These assumptions are typically hard-coded and are hoped to work well for a wide range of networks-which is in conflict with the nature of broad benchmark sets. With this article, we contribute in two ways. First, we identify a component in an existing solver that influences its performance depending on the class of graphs, and we then customize instances of this solver for different classes of graphs. Second, we create the first algorithm portfolio for the minimum vertex cover to further improve the performance of a single integrated approach to the minimum vertex cover problem.}, keywords = {graph theory, search problems, algorithm portfolio, benchmark sets, graphs, local search, minimum vertex cover solver, network classes, single integrated approach, straightforward alternative approach, Algorithm design and analysis, Benchmark testing, Machine learning algorithms, Optimization, Portfolios, Prediction algorithms, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969507}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969507}}, } @INPROCEEDINGS{pric:2017:CEC, author={K. V. Price}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={How symmetry constrains evolutionary optimizers}, year={2017}, editor = {Jose A. Lozano}, pages = {1712--1719}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Black box optimization begins with the assumption that if nothing is known about the objective function, then there is no justifiable reason for an optimization algorithm to, e.g. preferentially search in one direction, or to favor one coordinate system orientation over another. This paper investigates whether or not classic differential evolution (DE/rand/1/bin) satisfies these black-box constraints and if not, what it takes to bring the algorithm into conformity with them. The result is an exceptionally simple algorithm, black box differential evolution (BBDE), whose performance is invariant under a coordinate system translation, an orthogonal rotation, a reflection and a permutation of parameters. Performance is also invariant under both the addition of a function bias and an order-preserving transform of the objective function. On the family of ellipsoids, its performance is invariant to both scaling and high-conditioning (eccentricity). Additionally, BBDE is free of both selection and generating drift biases. Furthermore, selection, mutation and recombination are decoupled to become independent operations, as they should be, since each performs a distinctly different function that ought not to be duplicated by another. BBDE also satisfies a few algorithm-specific, symmetry-based constraints. Like the CMA-ES, BBDE's only control parameter is the population size. In short, BBDE appears to be the simplest DE strategy to conform to a set of symmetry-based constraints that are necessary for unbiased, i.e. black box, optimization.}, keywords = {evolutionary computation, BBDE, DE strategy, black box differential evolution, black box optimization, black-box constraints, coordinate system orientation, coordinate system translation, drift bias generation, drift bias selection, evolutionary optimizers, function bias, objective function, orthogonal rotation, parameter permutation, Ellipsoids, Linear programming, Optimization, Reflection, Sociology, Statistics, Transforms, black box, differential evolution, invariance, numerical optimization, symmetry}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969508}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969508}}, } @INPROCEEDINGS{kidoň:2017:CEC, author={M. Kidoň and R. Dobai}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary design of hash functions for IP address hashing using genetic programming}, year={2017}, editor = {Jose A. Lozano}, pages = {1720--1727}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Hash tables are common lookup data structures. A key element of such data structure is a hash function because it greatly affects its latency. A badly designed hash function may slow down the hash table by producing hash collisions which is a negative state that has to be resolved using additional computation time. There is no deterministic method for designing a well performing hash function. The designer solely relies on his/her experience, knowledge or intuition. This paper focuses on the evolutionary design of hash functions for Cuckoo hashing which is a modern approach to collision resolution. Its main benefit is constant time complexity of lookup which is achieved by using two or more hash functions per hash table. Hash functions are automatically designed using common elementary hashing operations such as multiplication or binary shift by means of genetic programming. The evolved hash functions are about 2.7 to 7 times faster, can utilize about 1 to 1.6% more keys and use fewer elementary operations than human-created counterparts on the IP address hashing problem.}, keywords = {genetic algorithms, genetic programming, IP networks, computational complexity, computer network security, cryptography, data structures, table lookup, IP address hashing problem, binary shift, collision resolution, constant time complexity, cuckoo hashing, elementary hashing operation, evolutionary design, hash collisions, hash functions, lookup data structures, multiplication shift, Electrical resistance measurement, Hardware, Resistance, Software, Time complexity, Hash function, Hash table}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969509}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969509}}, } @INPROCEEDINGS{yang:2017:CECa, author={Y. Yang and Y. Sun and Z. Zhu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective memetic algorithm based on request prediction for dynamic pickup-and-delivery problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1728--1733}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a multi-objective memetic algorithm based on request prediction for route planning in dynamic pickup-and-delivery problems. Historical data are used to predict the occurrence of new dynamic requests, based on which predictive routes are planned and tuned subsequently as the real requests occur. Two objectives namely route length and response time are optimized using multi-objective memetic algorithm that is a synergy of multi-objective genetic algorithm and a locality-sensitive hashing based local search. The proposed algorithm is tested on three benchmark problems and the experimental results demonstrate the efficiency of the algorithm.}, keywords = {genetic algorithms, planning, search problems, vehicle routing, dynamic pickup-and-delivery problems, local search, locality-sensitive hashing, multiobjective genetic algorithm, multiobjective memetic algorithm, request prediction, route planning, Biological cells, Heuristic algorithms, Memetics, Prediction algorithms, Sociology, Statistics, Vehicle dynamics, Memetic algorithm, dynamic pickup-and-delivery problem, multi-objective optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969510}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969510}}, } @INPROCEEDINGS{oliveira:2017:CECa, author={S. Oliveira and M. S. Hussin and A. Roli and M. Dorigo and T. Stützle}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Analysis of the population-based ant colony optimization algorithm for the TSP and the QAP}, year={2017}, editor = {Jose A. Lozano}, pages = {1734--1741}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The population-based ant colony optimization algorithm (P-ACO) differs from other ACO algorithms because of its implementation of the pheromone update. P-ACO keeps track of a population of solutions, which serves as an archive of solutions generated by the ants' colony. Pheromone updates in P-ACO are only done based on solutions that enter or leave the solution archive. The population-based scheme reduces considerably the computation time needed for the pheromone update when compared to classical ACO algorithms such as Ant System. In this work, we study the behavior of P-ACO when solving the traveling salesman and the quadratic assignment problem. In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results show that P-ACO reaches competitive performance but that the parameter settings and algorithm behavior are strongly problem-dependent.}, keywords = {ant colony optimisation, search problems, travelling salesman problems, P-ACO parameters, P-ACO performance, QAP, TSP, computation time reduces, local search impact, pheromone update, population-based ant colony optimization algorithm, quadratic assignment problem, traveling salesman problem, Algorithm design and analysis, Ant colony optimization, Complexity theory, Electronic mail, Linear programming, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969511}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969511}}, } @INPROCEEDINGS{shakya:2017:CEC, author={S. Shakya and Beum Seuk Lee and C. Di Cairano-Gilfedder and G. Owusu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Spares parts optimization for legacy telecom networks using a permutation-based evolutionary algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1742--1748}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Large service organizations such as telecom or utility companies face numerous decision management problems, many of which are related to the management of various resources. The management of spare parts and inventory is one of the key resource management problems an organization faces on a daily basis. Especially their timely availability can have serious impacts on the service quality and the customer satisfaction. Lack of visibility and availability of spare parts in the right place and at the right time can lead to traveling longer distance to supply the parts or in worst case a disruption of the service. We propose to automate the management of spare parts for a legacy technology in a telecom network by leveraging an evolutionary algorithm for optimizing the distribution of spare parts. Our results show that a significant gain can be made in comparison to a assignment typically performed in a manual mechanism.}, keywords = {customer satisfaction, evolutionary computation, inventory management, maintenance engineering, quality of service, telecommunication networks, decision management problems, large service organizations, legacy technology, legacy telecom networks, permutation-based evolutionary algorithm, resource management problems, service disruption, service quality, spare parts distribution optimization, spare parts inventory management, spare parts management automation, spare parts optimization, telecom companies, utility companies, Genetic algorithms, Optimization, Organizations, Planning, Resource management, Telecommunications, Evolutionary algorithms, Telecom, permutation representation, spare parts management}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969512}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969512}}, } @INPROCEEDINGS{tam:2017:CEC, author={Hiu-Hin Tam and Sin-Chun Ng and A. K. Lui and Man-Fai Leung}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improved activation schema on Automatic Clustering using Differential Evolution algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1749--1756}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Partitional Clustering is one of the major techniques in Unsupervised Learning in which similar data are put into the same partition. Besides partitioning the unlabeled data, determining the optimal number of partitions is also another main concern in the field of data clustering. Automatic Clustering Differential Evolution (ACDE) is one of the state-of-the-art algorithms that address this concern. In ACDE, the mechanism to determine the optimal number of clusters is by encoding the activation value of each cluster centroid into the chromosome with fixed threshold value. However, it could be argued that a fixed threshold value would be seen as arbitrary, but a varying and adaptive threshold value could yield a solution that would better reflect the quality of clusters. In this paper, a new changing schema of threshold values is introduced for adaptively activating the clusters in the chromosomes, and a heuristic approach is implemented for adjusting the threshold values of each cluster according to their individual quality measurements. The results of several experiments show that the proposed algorithm performed generally better than other state-of-the-art automatic evolutionary clustering algorithms.}, keywords = {evolutionary computation, pattern clustering, unsupervised learning, ACDE, activation schema, adaptive threshold value, automatic clustering differential evolution, cluster centroid activation value, data clustering, fixed threshold value, heuristic approach, partitional clustering, unlabeled data partitioning, Biological cells, Clustering algorithms, Encoding, Genetic algorithms, Partitioning algorithms, Sociology, Statistics, Automatic clustering, differential evolution (DE), evolutionary clustering, genetic algorithm (GA)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969513}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969513}}, } @INPROCEEDINGS{wang:2017:CECe, author={Yali Wang and L. Li and K. Yang and M. T. M. Emmerich}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A new approach to target region based multiobjective evolutionary algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {1757--1764}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, a target region based multiobjective evolutionary algorithm framework is proposed to incorporate preference into the optimization process. It aims at finding a more fine-grained resolution of a target region without exploring the whole set of Pareto optimal solutions. It can guide the search towards the regions on the Pareto Front which are of real interest to the decision maker. The algorithm framework has been combined with SMS-EMOA, R2-EMOA, NSGA-II to form three target region based multiobjective evolutionary algorithms: T-SMS-EMOA, T-R2-EMOA and T-NSGA-II. In these algorithms, three ranking criteria are applied to achieve a well-converged and well-distributed set of Pareto optimal solutions in the target region. The three criteria are: 1. Non-dominated sorting; 2. indicators (hypervolume or R2 indicator) or crowding distance in the new coordinate space (i.e. target region) after coordinate transformation; 3. the Chebyshev distance to the target region. Rectangular and spherical target regions have been tested on some benchmark problems, including continuous problems and discrete problems. Experimental results show that new algorithms can handle the preference information very well and find an adequate set of Pareto-optimal solutions in the preferred regions quickly. Moreover, the proposed algorithms have been enhanced to support multiple target regions and preference information based on a target point or multiple target points. Some results of enhanced algorithms are presented.}, keywords = {Pareto optimisation, decision making, genetic algorithms, search problems, Chebyshev distance, Pareto front, Pareto optimal solutions, R2 indicator, T-NSGA-II, T-R2-EMOA, T-SMS-EMOA, continuous problems, coordinate transformation, crowding distance, decision maker, discrete problems, hypervolume, multiobjective evolutionary algorithms, non-dominated sorting, ranking criteria, rectangular target regions, spherical target regions, Chebyshev approximation, Evolutionary computation, Pareto optimization, Sociology, Sorting, Evolutionary algorithms, Multiobjective optimization, Preference, Target region}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969514}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969514}}, } @INPROCEEDINGS{tasgetiren:2017:CECa, author={M. F. Tasgetiren and Q. K. Pan and Y. Ozturkoglu and O. K. Cotur}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Variable block insertion heuristic for the quadratic assignment problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1765--1770}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The aim of this paper is to apply the variable block insertion heuristic (VBIH) algorithm recently proposed in the literature for solving the quadratic assignment problem (QAP). The VBIH algorithm is concerned with making block moves in a given solution. As a local search in this paper, the VNST is employed from the literature to be applied to a solution obtained after several block moves. Besides the single-solution based VBIH, we also propose a populated VBIH (PVBIH) in this paper. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are very effective in solving both types of instances. All PCB instances are further improved.}, keywords = {combinatorial mathematics, optimisation, search problems, PCB instances, PVBIH, QAPLIB, VBIH algorithm, VNST, block moves, local search in, populated VBIH, quadratic assignment problem, single-solution based VBIH, variable block insertion heuristic, Benchmark testing, Genetic algorithms, Heuristic algorithms, Logistics, Optimization, Sociology, Statistics, variable neighborhood search}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969515}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969515}}, } @INPROCEEDINGS{algethami:2017:CEC, author={H. Algethami and D. Landa-Silva}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {1771--1778}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems.}, keywords = {cost reduction, genetic algorithms, personnel, scheduling, WSRP, adaptive evolutionary algorithms, combinatorial optimisation, diversity-based adaptive genetic algorithm, diversity-based adaptive parameter control, geographical locations, parameter tuning, personnel assignment, routing constraints, total operational cost minimisation, workforce scheduling and routing problem, Biological cells, Genetics, Routing, Sociology, Statistics, Adaptive Evolutionary Algorithm, Workforce Scheduling and Routing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969516}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969516}}, } @INPROCEEDINGS{shirazi:2017:CEC, author={A. Shirazi and J. Ceberio and J. A. Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission}, year={2017}, editor = {Jose A. Lozano}, pages = {1779--1786}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the in-plane rotation of space orbit, denominated as orbital precession. To overcome this problem, one option is to correct the orbit direction by employing low-thrust trajectories. However, in addition to the orbital perturbation acting on the spacecraft, a number of parameters related to the spacecraft and its propulsion system must be optimized. This article lays out the trajectory optimization of orbital precession missions using Evolutionary Algorithms (EAs). In this research, the dynamics of spacecraft in the presence of orbital perturbation is modeled. The optimization approach is employed based on the parametrization of the problem according to the space mission. Numerous space mission cases have been studied in low and middle Earth orbits, where various types of orbital perturbations are acted on spacecraft. Consequently, several EAs are employed to solve the optimization problem. Results demonstrate the practicality of different EAs, along with comparing their convergence rates. With a unique trajectory model, EAs prove to be an efficient, reliable and versatile optimization solution, capable of being implemented in conceptual and preliminary design of spacecraft for orbital precession missions.}, keywords = {Earth rotation, aerospace propulsion, evolutionary computation, space vehicles, trajectory optimisation (aerospace), EA, convergence rates, evolutionary algorithms, in-plane space orbit rotation, low-Earth orbits, low-thrust trajectories, low-thrust trajectory design optimization, middle-Earth orbits, orbit direction, orbital perturbation, propulsion system, spacecraft dynamics, spacecraft orbital precession mission, Mathematical model, Optimization, Orbits, Propulsion, Space missions, Trajectory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969517}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969517}}, } @INPROCEEDINGS{greensmith:2017:CEC, author={J. Greensmith and M. B. Gale}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The Functional Dendritic Cell Algorithm: A formal specification with Haskell}, year={2017}, editor = {Jose A. Lozano}, pages = {1787--1794}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Dendritic Cell Algorithm (DCA) has been described in a number of different ways, sometimes resulting in incorrect implementations. We believe this is due to previous, imprecise attempts to describe the algorithm. The main contribution of this paper is to remove this imprecision through a new approach inspired by purely functional programming. We use new specification to implement the deterministic DCA in Haskell - the hDCA. This functional variant will also serve to introduce the DCA to a new audience within computer science. We hope that our functional specification will help improve the quality of future DCA related research and to help others understand further its algorithmic properties.}, keywords = {biology computing, formal specification, functional languages, Haskell, functional dendritic cell algorithm, functional programming, hDCA, Algorithm design and analysis, Artificial intelligence, Computer science, Immune system, Monitoring, Object oriented modeling, Signal processing algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969518}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969518}}, } @INPROCEEDINGS{barroso:2017:CEC, author={B. C. Barroso and F. G. D. C. Ferreira and G. P. Hanaoka and F. D. Paiva and R. T. N. Cardoso}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Composition of investment portfolios through a combinatorial multiobjective optimization model using CVaR}, year={2017}, editor = {Jose A. Lozano}, pages = {1795--1802}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a combinatorial multiobjective optimization methodology to address diversification of investment in portfolios consistent with the market practices, using a “downside risk” measure. To cope with this feature, parallel versions of two evolutionary algorithms are proposed, based on NSGA-II and DEMO. Simulations consider portfolios comprised of shares that participated in the theoretical portfolio of Ibovespa in 2015. In-sample analysis considers graphical analysis, performance measures for diversity solutions and objective space coverage. Out-of-sample analysis is performed comparing the behavior of lower risk and higher return portfolios in relation to measures of risk and return, for several cardinalities.}, keywords = {genetic algorithms, graph theory, investment, CVaR, DEMO algorithm, NSGA-II algorithm, combinatorial multiobjective optimization model, down-side risk measure, evolutionary algorithm, graphical analysis, higher-return portfolios, in-sample analysis, investment portfolios, lower-risk portfolios, objective space coverage, out-of-sample analysis, performance measures, Algorithm design and analysis, Evolutionary computation, Optimization, Portfolios, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969519}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969519}}, } @INPROCEEDINGS{blom:2017:CEC, author={K. van der Blom and S. Boonstra and H. Hofmeyer and T. Bäck and M. T. M. Emmerich}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Configuring advanced evolutionary algorithms for multicriteria building spatial design optimisation}, year={2017}, editor = {Jose A. Lozano}, pages = {1803--1810}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper solution approaches for solving the building spatial design optimisation problem for structural and energy performance are advanced on multiple fronts. A new initialisation operator is introduced to generate an unbiased initial population for a tailored version of SMS-EMOA with problem specific operators. Improvements to the mutation operator are proposed to eliminate bias and allow mutations consisting of multiple steps. Moreover, landscape analysis is applied in order to explore the landscape of both objectives and investigate the behaviour of the mutation operator. Parameter tuning is applied with the irace package and the Mixed Integer Evolution Strategy to find improved parameter settings and explore tuning with a relatively small number of expensive evaluations. Finally, the performances of the standard and tailored SMS-EMOA algorithms with tuned parameters are compared.}, keywords = {buildings (structures), design engineering, evolutionary computation, optimisation, advanced evolutionary algorithms, energy performance, initialisation operator, irace package, landscape analysis, mixed integer evolution strategy, multicriteria building spatial design optimisation, mutation operator, parameter tuning, problem specific operators, standard SMS-EMOA algorithm, structural performance, tailored SMS-EMOA algorithm, unbiased initial population, Algorithm design and analysis, Buildings, Optimization, Shape, Sociology, Statistics, Tuning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969520}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969520}}, } @INPROCEEDINGS{zaharie:2017:CEC, author={D. Zaharie and F. Micota}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Revisiting the analysis of population variance in Differential Evolution algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {1811--1818}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The performance of Differential Evolution (DE) algorithms is highly dependent on the trial population diversity and on the way the control parameter space is sampled. Therefore, identifying critical regions containing control parameters (e.g. scale factor, crossover rate) which can induce undesired behaviour (e.g. premature convergence) is useful. In this context, the aim of the paper is twofold. On one hand, the paper revisits some existing theoretical results on the expected variance of the trial population aiming to provide a comparative image on critical regions in the control parameter space for several DE variants: DE/rand/1/*, DE/best/1/*, DE/rand-to-best/*, DE/either-or. On the other hand, a new theoretical result on DE/rand/1/* population variance evolution is obtained under the assumption that the bound constraints are handled by random reinitialization of infeasible components. The relationship between the probability of violating the bound constraints and the value of the scale factor, F, is theoretically derived for DE/rand/1/* and empirically analyzed for other DE mutation operators.}, keywords = {algorithm theory, evolutionary computation, DE algorithms, DE mutation operators, control parameter space, differential evolution algorithms, population variance, random reinitialization, trial population diversity, Aerospace electronics, Algorithm design and analysis, Computer science, Convergence, Electronic mail, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969521}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969521}}, } @INPROCEEDINGS{chen:2017:CECd, author={J. Chen and M. N. Omidvar and M. Azad and X. Yao}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Knowledge-based particle swarm optimization for PID controller tuning}, year={2017}, editor = {Jose A. Lozano}, pages = {1819--1826}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a well-performing controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications. The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.}, keywords = {control system synthesis, dynamic response, feedback, industrial control, particle swarm optimisation, performance index, three-term control, PID controller tuning, PID dynamic response, automatic voltage regulator system, control loop feedback mechanism, electric pump drive, industrial control systems, knowledge-based particle swarm optimization, parameter tuning, proportional-integral-derivative controller, Algorithm design and analysis, Genetic algorithms, Heuristic algorithms, PD control, Particle swarm optimization, Steady-state, Tuning, Knowledge, PID Controller}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969522}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969522}}, } @INPROCEEDINGS{azzouz:2017:CEC, author={A. Azzouz and M. Ennigrou and L. B. Said}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A self-adaptive evolutionary algorithm for solving flexible job-shop problem with sequence dependent setup time and learning effects}, year={2017}, editor = {Jose A. Lozano}, pages = {1827--1834}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Flexible job shop problems (FJSP) are among the most intensive combinatorial problems studied in literature. These latters cover two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, two others constraints are taken into consideration which are: (1) The sequence dependent setup time and (2) the learning effects. For solving such complex problem, we propose an evolutionary algorithm (EA) based on genetic algorithm (GA) combined with two efficient local search methods, called, variable neighborhood search (VNS) and iterated local search (ILS). It is well known that the performance of EA is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on: (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA, VNS and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm is more effective and robust with respect to other well known effective algorithms.}, keywords = {combinatorial mathematics, genetic algorithms, iterative methods, job shop scheduling, search problems, EA, FJSP, GA, ILS, VNS, combinatorial problems, efficient local search methods, flexible job shop problems, genetic algorithm, iterated local search, learning effects, machine assignment problem, operation sequencing problem, search direction, self-adaptive evolutionary algorithm, self-adaptive strategy, sequence dependent setup time, variable neighborhood search, Biological cells, Evolutionary computation, Industries, Manufacturing systems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969523}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969523}}, } @INPROCEEDINGS{kumar:2017:CEC, author={A. Kumar and R. K. Misra and D. Singh}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving the local search capability of Effective Butterfly Optimizer using Covariance Matrix Adapted Retreat Phase}, year={2017}, editor = {Jose A. Lozano}, pages = {1835--1842}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Effective Butterfly Optimizer(EBO) is a self-adaptive Butterfly Optimizer which incorporates a crossover operator in Perching and Patrolling to increase the diversity of the population. This paper proposes a new retreat phase called Covariance Matrix Adapted Retreat Phase (CMAR), which uses covariance matrix to generate a new solution and thus improves the local search capability of EBO. This version of EBO is called EBOwithCMAR. We evaluated the performance of EBOwithCMAR on CEC-2017 benchmark problems and compared with the results of winners of a special session of CEC-2016 for bound-constrained problems. The experimental results show that EBOwithCMAR is competitive with the compared algorithms.}, keywords = {covariance analysis, covariance matrices, optimisation, search problems, EBOwithCMAR, Effective Butterfly Optimizer, butterfly optimizer, covariance matrix adapted retreat phase, local search capability, patrolling, perching, self-adaptive Butterfly Optimizer, Electrical engineering, Heuristic algorithms, Mathematical model, Optimization, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969524}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969524}}, } @INPROCEEDINGS{bernardo:2017:CEC, author={F. Bernardo and G. Moreira and E. Luz and P. H. C. Oliveira and Á. Gaurda}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Exploring the scalability of multiple signatures in iris recognition using GA on the acceptance frontier search}, year={2017}, editor = {Jose A. Lozano}, pages = {1843--1847}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={For decades iris recognition has been widely studied by the scientific community due to its almost unique and stable patterns. Iris recognition biometric systems apply mathematical pattern-recognition techniques to an iris' image of an individual's eye to extract its feature vector. Comparing the dissimilarities from two feature vectors with an acceptance threshold, the system decides if the two vectors are from the same individual's eye. If applied in a well-controlled environment, iris recognition can achieve outstanding accuracies, however, to accomplish that in non-controlled environments is still a challenge researchers are constantly trying to compensate open issues in this context. In order to better explore the patterns found in the iris, researchers have recently begun using a classification approach using multiple signatures, hoping to improve the algorithm robustness. This work aims to explore the effectiveness and scalability of using multiple signatures with a 2-D Gabor filter in a biometric verification system through iris recognition. This is done using two independent Genetic Algorithms to search for the best parameters to the feature extraction technique and on the acceptance frontier search. The method was evaluated by analyzing the behavior of the Half Total Error Rate (HTER) when the number of partitions varies. The experiments showed that the best result was found with 12 partitions on the iris, reaching 0.21% of HTER.}, keywords = {Gabor filters, feature extraction, genetic algorithms, image filtering, iris recognition, 2-D Gabor filter, GA, HTER, acceptance frontier search, biometric verification system, feature extraction technique, half total error rate, multiple signatures scalability, Databases, Image segmentation, Iris, Robustness}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969525}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969525}}, } @INPROCEEDINGS{fontoura:2017:CEC, author={V. D. Fontoura and A. T. R. Pozo and R. Santana}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Automated design of hyper-heuristics components to solve the PSP problem with HP model}, year={2017}, editor = {Jose A. Lozano}, pages = {1848--1855}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Protein Structure Prediction (PSP) problem is one of the modern most challenging problems from science. Simplified protein models are usually applied to simulate and study some characteristics of the protein folding process. Hence, many heuristic strategies have been applied in order to find simplified protein structures in which the protein configuration has the minimal energy. However, these strategies have difficulties in finding the optimal solutions to the longer sequences of amino-acids, due to the complexity of the problem and the huge amount of local optima. Hyper heuristics have proved to be useful in this type of context since they try to combine different heuristics strengths into a single framework. However, there is lack of work addressing the automated design of hyper-heuristics components. This paper proposes GEHyPSP, an approach which aims to achieve generation, through grammatical evolution, of selection mechanisms and acceptance criteria for a hyper-heuristic framework applied to PSP problem. We investigate the strengths and weaknesses of our approach on a benchmark of simplified protein models. GEHyPSP was able to reach the best known results for 7 instances from 11 that composed the benchmark set used to evaluate the approach.}, keywords = {biology computing, evolutionary computation, grammars, proteins, GEHyPSP, HP model, PSP problem, acceptance criteria, automated hyper-heuristics component design, grammatical evolution, hyper-heuristic framework, protein folding process, protein structure prediction problem, selection mechanisms, simplified protein models, Context, Grammar, Production, Sociology, Statistics, Two dimensional displays}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969526}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969526}}, } @INPROCEEDINGS{bevilacqua:2017:CEC, author={V. Bevilacqua and A. Brunetti and G. F. Trotta and G. Dimauro and K. Elez and V. Alberotanza and A. Scardapane}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A novel approach for Hepatocellular Carcinoma detection and classification based on triphasic CT Protocol}, year={2017}, editor = {Jose A. Lozano}, pages = {1856--1863}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: In our previous study, materials consisted in abdominal CT hepatic lesions of only three patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. In this paper, thanks to a more extensively phase of data collection, available materials impressively grew to 18 patients belonging to 2-balanced classes. Several approaches were implemented to segment the region of liver and, then, to detect the ROI of the lesions. At the end of these preprocessing phases, we extracted the same morphological features of the previous work and designed an evolutionary algorithm to optimize neural network classifiers based on different subsets of features. Results and conclusion: Tests conducted on the new ANN topologies showed a higher generalization of the average performance indices regardless of the applied training, validation and test sets, confirming both the validity and the robustness of the approach of previous study even though the limited number of patients.}, keywords = {computerised tomography, evolutionary computation, image segmentation, medical image processing, neural nets, CAD systems, CT images, ROI, US-guided needle biopsy, computed tomography, computer aided decision systems, evolutionary algorithm, hepatocellular carcinoma classification, hepatocellular carcinoma detection, lesion segmentation, liver isolation, liver transplant, medical imaging, neural network classifiers, partial hepatectomy, regions of interest, triphasic CT protocol, Cancer, Feature extraction, Lesions, Liver}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969527}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969527}}, } @INPROCEEDINGS{castellini:2017:CEC, author={A. Castellini and G. Franco and A. Vella}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Age-related relationships among peripheral B lymphocyte subpopulations}, year={2017}, editor = {Jose A. Lozano}, pages = {1864--1871}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={An immunological data-driven model is proposed, for age related changes in the network of relationships among cell quantities of eight peripheral B lymphocyte subpopulations, that is, cells exhibiting all combinations of three specific receptor clusters (CD27, CD23, CD5). The model is based on immunological data (quantities of cells exhibiting CD19, characterizing B lymphocytes) from about six thousands patients, having an age ranging between one day and ninety-five years, by means of a suitably combination of data analysis methods, such as piecewise linear regression models. With relaxed values for statistically significant models (coefficient p-values bounded by 0.05), we found a network holding for all ages, that likely represents the general assessment of adaptive immune system for healthy human beings. When statistical validation comes to be more restrictive, we found that some of these interactions are lost with aging, as widely observed in medical literature. Namely, interesting (inverse or directed) proportions are highlighted among mutual quantities of a partition of peripheral B lymphocytes.}, keywords = {age issues, bioinformatics, cellular biophysics, data analysis, piecewise linear techniques, regression analysis, B lymphocyte characterizaption, CD19, adaptive immune system, age related changes, age-related relationship, aging, cell quantities, coefficient p-values, data analysis method, healthy human beings, immunological data-driven model, peripheral B lymphocyte subpopulation, piecewise linear regression models, receptor clusters, statistically significant model, Blood, Computational modeling, Data models, Immune system, Linear regression, Time series analysis}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969528}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969528}}, } @INPROCEEDINGS{jagodziński:2017:CEC, author={D. Jagodziński and J. Arabas}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A differential evolution strategy}, year={2017}, editor = {Jose A. Lozano}, pages = {1872--1876}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This contribution introduces an evolutionary algorithm (EA) for continuous optimization in ℝ^n . The algorithm generates new individuals by the standard nonelitist truncation selection and the differential mutation to generate new individuals. The differential mutation is enriched by adding a random vector in the direction of the shift of population midpoint. Difference vectors are generated with the use of the archive of previous populations. Boundary constraints are handled by penalty function.}, keywords = {evolutionary computation, random processes, vectors, boundary constraints, continuous optimization, differential evolution strategy, differential mutation, evolutionary algorithm, penalty function, population midpoint, random vector, standard nonelitist truncation selection, Benchmark testing, Covariance matrices, Indexes, Optimization, Sociology, Standards, Differential Evolution}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969529}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969529}}, } @INPROCEEDINGS{matos:2017:CEC, author={J. L. Matos and A. Britto}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Swarm Algorithm Based on Archiving and Topologies for Many-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {1877--1884}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Many-objective optimization problems are problems that have more than three objective functions. Traditional multi-objective evolutionary algorithms scale poorly when the number of objective functions increases. Recently, different approaches have been proposed for improving the performance of these algorithms on many-objective optimization problems. One of these approaches is the use of multiple populations on multi-objective particle swarm optimization, called multi-swarm. Multi-swarm techniques explore parallel populations to decompose the problem and optimize them in a collaborative manner. This paper presents a new multi-swarm algorithm, called Multi-Swarm Algorithm Based on Archiving and Topologies (MSAT). MSAT combines different archiving methods and communication topologies aiming to obtain good convergence and diversity in many-objective optimization problems. An experimental set is performed seeking to find the best combination of archiving and topology methods. Furthermore, MSAT is confronted to NSGA-III algorithm in different scenarios. MSAT outperformed NSGA-III both in terms of convergence and diversity, for concave and convex problems.}, keywords = {concave programming, convergence, convex programming, particle swarm optimisation, topology, MSAT, archiving methods, collaborative manner, communication topologies, concave problem, convex problem, many-objective optimization, multiobjective particle swarm optimization, multiswarm algorithm, parallel populations, Linear programming, Optimization, Particle swarm optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969530}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969530}}, } @INPROCEEDINGS{guerrero-peña:2017:CEC, author={E. Guerrero-Peña and A. F. R. Araújo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A Gaussian Mixture Model based local search for Differential Evolution Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1885--1892}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary algorithms have been extensively explored and applied in optimization problems. They allow work with multiple solutions simultaneously, with multimodal functions and dynamic problems, and do not require additional information. Several algorithms have been developed over the years for this task. Yet special attention is needed in the area of increasing the convergence speed of evolutionary algorithms. This study is aimed at developing a framework capable of addressing this new line of research in the field of evolutionary computation. We used the Gaussian Mixture Model to do a local search, and generated a new population through the use of Variational Inference. To implement the proposed framework (GMM-Local Search), NSDE both static and dynamic with multiple objectives were used as basic algorithms. Experiments were performed with different test functions for static and dynamic multi-objective optimization problems. The comparison of the algorithms using the proposed framework with the basic algorithms are presented here, thus evidencing that an improvement in the convergence can be achieved.}, keywords = {Gaussian processes, evolutionary computation, inference mechanisms, mixture models, optimisation, search problems, GMM-local search, Gaussian mixture model based local search, convergence speed, differential evolution algorithm, dynamic multiobjective optimization problem, dynamic problems, multimodal functions, optimization problems, static multiobjective optimization problem, test functions, variational inference, Convergence, Gaussian mixture model, Heuristic algorithms, Optimization, Sociology, Dynamic Multiobjective Optimization, Static Multiobjective Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969531}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969531}}, } @INPROCEEDINGS{wu:2017:CEC, author={Rui Wu and J. T. Painumkal and J. M. Volk and S. Liu and S. J. Louis and S. Tyler and S. M. Dascalu and F. C. Harris}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Parameter estimation of nonlinear nitrate prediction model using genetic algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {1893--1899}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We attack the problem of predicting nitrate concentrations in a stream by using a genetic algorithm to minimize the difference between observed and predicted concentrations on hydrologic nitrate concentration model based on a US Geological Survey collected data set. Nitrate plays a significant role in maintaining ecological balance in aquatic ecosystems and any advances in nitrate prediction accuracy will improve our understanding of the non-linear interplay between the factors that impact aquatic ecosystem health. We compare the genetic algorithm tuned model against the LOADEST estimation tool in current use by hydrologists, and against a random forest, generalized linear regression, decision tree, and gradient booted tree and show that the genetic algorithm does statistically significantly better. These results indicate that genetic algorithms are a viable approach to tuning such non-linear, hydrologic models.}, keywords = {decision trees, ecology, estimation theory, genetic algorithms, geophysics, gradient methods, hydrology, nitrogen, random functions, regression analysis, LOADEST estimation tool, US geological survey collected data set, aquatic ecosystem health, decision tree, ecological balance, generalized linear regression, genetic algorithm tuned model, gradient booted tree, hydrologic nitrate concentration, minimization, nonlinear interplay, nonlinear nitrate prediction model, observed concentrations, parameter estimation, predicted concentrations, random forest, stream nitrate concentrations, Biological system modeling, Estimation, Linear regression, Load modeling, Predictive models, Regression tree analysis, genetic algorithm, model, nitrate, prediction}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969532}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969532}}, } @INPROCEEDINGS{meneghini:2017:CEC, author={I. R. Meneghini and F. G. Guimarães}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary method for weight vector generation in Multi-Objective Evolutionary Algorithms based on decomposition and aggregation}, year={2017}, editor = {Jose A. Lozano}, pages = {1900--1907}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The generation of weight vectors is the primary step in MOEA based on decomposition and aggregation methods, affecting the diversity of the Pareto approximation and overall performance of the algorithm. The basic methods, following the method proposed by Scheffé, have some limitations mainly when the number of objectives increases, because the number of weight vectors and hence the population size becomes very large. In this paper, we present a new method for weight vector generation that can create an arbitrary number of weight vectors, almost equally spaced, located in a surface in the first orthant of the objective space, with free choice of norm. The proposed evolutionary algorithm is able to prevent the creation of weight vectors along the border of the orthant, which is a region that contains solutions of little interest to the decision maker. With a small modification in the proposed method it is also possible to create cones of weight vectors, useful to explore specific regions of the decision space defined by preference directions. In our experiments, different sets of weight vectors were generated, varying the number of vectors and the dimension of the space. The validation of the results was given by the mean distance of each vector to its nearest neighbor, as well as the standard deviation and the Pearson coefficient of variation for this mean value. The results indicate that the proposed method is able to produce a distribution of vectors close to a uniform distribution, with no clustering of points, being useful for guiding decomposition-based MOEA.}, keywords = {Pareto optimisation, approximation theory, decision making, evolutionary computation, matrix algebra, vectors, Pareto approximation diversity, Pearson coefficient, aggregation method, decision maker, decision space, decomposition method, decomposition-based MOEA, evolutionary method, multiobjective evolutionary algorithms, nearest neighbor, objective space, preference directions, standard deviation, vector mean distance, weight vector generation, Approximation algorithms, Frequency modulation, Lattices, Sociology, Standards, Statistics, Decomposition, MOEA/D, Mixture Experiments, Multi-objective Evolutionary Algorithms, Weight Vectors Generation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969533}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969533}}, } @INPROCEEDINGS{meyer:2017:CEC, author={O. Meyer and F. Wessling and C. Klüver}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Finding optimized configurations for variability-intensive systems without constraint violations using a Regulatory Algorithm (RGA)}, year={2017}, editor = {Jose A. Lozano}, pages = {1908--1915}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Feature selection is one of the important challenges in variability-intensive systems. The FCORE model is used for the description of the functional and non-functional requirements of a system from a systems engineering point of view. In addition we demonstrate a solution for feature selection using a regulator algorithm (RGA). The RGA is a two dimensional evolutionary algorithm, with regulator genes controlling the structural genes. This allows a direct transfer of the FCORE model into the RGA, which optimizes the feature selection without constraint violations.}, keywords = {evolutionary computation, feature selection, formal specification, formal verification, software product lines, systems analysis, systems engineering, FCORE model, RGA, evolutionary algorithm, functional system requirements, regulator genes, regulatory algorithm, structural genes, variability-intensive system configuration, Modeling, Smart homes, Software, Software algorithms, constraints, evolutionary algorithms, feature models, optimization, software product line}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969534}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969534}}, } @INPROCEEDINGS{krömer:2017:CEC, author={P. Krömer and M. Kudělka and R. Senkerik and M. Pluhacek}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Differential evolution with preferential interaction network}, year={2017}, editor = {Jose A. Lozano}, pages = {1916--1923}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Population-based metaheuristic optimization methods are built upon an algorithmic implementation of different types of complex dynamic behaviours. The problem-solving strategies they implement are often inspired by various natural and social phenomena whose fundamental principles were adopted for the use in practical search and optimization problems. New insights into complex systems, attained among others within the fields of network science and social network analysis, can be successfully incorporated into the study of evolutionary and swarm methods and used to improve their efficiency. Preferential attachment is a principle governing the growth of many real-world networks. That makes it a natural candidate for the use with network-based models of artificial evolution. Differential evolution is a widely-used evolutionary algorithm valued for its efficiency and versatility as well as simplicity and ease of implementation. In this paper, a variant of differential evolution, guided by an auxiliary model of population dynamics built with the help of the preferential attachment principle, is designed. The efficiency of the proposed approach is analyzed on the CEC 2017 real-parameter optimization benchmark.}, keywords = {evolutionary computation, network theory (graphs), search problems, CEC 2017 real-parameter optimization benchmark, artificial evolution, complex dynamic behaviours, differential evolution, evolutionary algorithm, network science, population dynamics, population-based metaheuristic optimization, preferential attachment principle, preferential interaction network, social network analysis, Algorithm design and analysis, Benchmark testing, Heuristic algorithms, Information exchange, Optimization, Sociology, Statistics, competition, experiments, preferential attachment}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969535}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969535}}, } @INPROCEEDINGS{vissol-gaudin:2017:CEC, author={E. Vissol-Gaudin and A. Kotsialos and M. K. Massey and C. Groves and C. Pearson and D. A. Zeze and M. C. Petty}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving binary classification problems with carbon nanotube / liquid crystal composites and evolutionary algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {1924--1931}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The training process is formulated as an optimisation problem with hardware in the loop. The liquid SWCNT/LC samples used here are un-configured and with nonlinear current-voltage relationship, thus presenting a potential for being evolved. The nature of the problem means that derivative-free stochastic search algorithms are required. Results presented here are based on differential evolution (DE) and particle swarm optimisation (PSO). Further investigations using DE, suggest that a SWCNT/LC material is capable of being reconfigured for different binary classification problems, corroborating previous research. In addition, it is able to retain a physical memory of each of the solutions to the problems it has been trained to solve.}, keywords = {evolutionary computation, particle swarm optimisation, search problems, DE, PSO, SWCNT/LC material, binary classification problems, carbon nanotube, derivative-free stochastic search algorithms, differential evolution, evolutionary algorithms, liquid SWCNT/LC samples, liquid crystal composites, nonlinear binary datasets, nonlinear current-voltage relationship, single-walled carbon-nanotube, Carbon nanotubes, Computers, Current measurement, Electrodes, Hardware, Optimization, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969536}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969536}}, } @INPROCEEDINGS{goribar-jimenez:2017:CEC, author={C. Goribar-Jimenez and Y. Maldonado and L. Trujillo and M. Castelli and I. Gonçalves and L. Vanneschi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Towards the development of a complete GP system on an FPGA using geometric semantic operators}, year={2017}, editor = {Jose A. Lozano}, pages = {1932--1939}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Genetic Programming (GP) has been around for over two decades and has been used in a wide range of practical applications producing human competitive results in several domains. In this paper we present a discussion and a proposal of a GP algorithm that could be conveniently implemented on an embedded system, as part of a broader research project that pursues the implementation of a complete GP system in a Field Programmable Gate Array (FPGA). Motivated by the significant time savings associated with such a platform, as well as low power consumption, low maintenance requirements, small size of the system and the possibility of performing several parallel processes. The proposal is focused on the Geometric Semantic Genetic Programming (GSGP) approach that has been recently introduced with promising results. GSGP induces a unimodal fitness landscape, simplifying the search process. The experimental work considers five variants of GSGP, that incorporate local search strategies, optimal mutations and alignment in error space. Best results were obtained by a simple variant that uses both the optimal mutation step and the standard geometric semantic mutation, using three difficult real-world problems to evaluate the methods, outperforming the original GSGP formulation in terms of fitness and empirical convergence.}, keywords = {genetic algorithms, genetic programming, convergence, embedded systems, field programmable gate arrays, geometry, parallel processing, search problems, FPGA, GP algorithm, GP system development, GSGP, embedded system, empirical convergence, error space alignment, field programmable gate array, geometric semantic genetic programming, geometric semantic operators, local search strategies, maintenance requirements, optimal mutation step, parallel processes, power consumption, standard geometric semantic mutation, time savings, unimodal fitness landscape, Arrays, GSM, Proposals, Semantics, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969537}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969537}}, } @INPROCEEDINGS{tangherloni:2017:CEC, author={A. Tangherloni and L. Rundo and M. S. Nobile}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {1940--1947}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Particle Swarm Optimization (PSO) is an effective Swarm Intelligence technique for the optimization of non-linear and complex high-dimensional problems. Since PSO's performance is strongly dependent on the choice of its functioning settings, in this work we consider a self-tuning version of PSO, called Proactive Particles in Swarm Optimization (PPSO). PPSO leverages Fuzzy Logic to dynamically determine the best settings for the inertia weight, cognitive factor and social factor. The PPSO algorithm significantly differs from other versions of PSO relying on Fuzzy Logic, because specific settings are assigned to each particle according to its history, instead of being globally assigned to the whole swarm. In such a way, PPSO's particles gain a limited autonomous and proactive intelligence with respect to the reactive agents proposed by PSO. Our results show that PPSO achieves overall good optimization performances on the benchmark functions proposed in the CEC 2017 test suite, with the exception of those based on the Schwefel function, whose fitness landscape seems to mislead the fuzzy reasoning. Moreover, with many benchmark functions, PPSO is characterized by a higher speed of convergence than PSO in the case of high-dimensional problems.}, keywords = {algorithm theory, fuzzy logic, particle swarm optimisation, PPSO algorithm, Schwefel function, benchmark functions, fitness landscape, fuzzy reasoning, particle swarm optimization, proactive intelligence, proactive particles, reactive agents, settings-free algorithm, single objective optimization problems, swarm intelligence, Benchmark testing, Heuristic algorithms, Optimization, Pragmatics, Social factors, CEC 2017 competition, Proactive Particles in Swarm Optimization, Real-parameter single objective optimization, Settings-free algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969538}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969538}}, } @INPROCEEDINGS{jacobsen-grocott:2017:CEC, author={J. Jacobsen-Grocott and Yi Mei and Gang Chen and M. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming}, year={2017}, editor = {Jose A. Lozano}, pages = {1948--1955}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Dynamic vehicle routing problem with time windows is an important combinatorial optimisation problem in many real-world applications. The most challenging part of the problem is to make real-time decisions (i.e. whether to accept the newly arrived service requests or not) during the execution of the routes. It is hardly applicable to use the optimisation methods such as mathematical programming and evolutionary algorithms that are competitive for static problems, since they are usually time-consuming, and cannot give real-time responses. In this paper, we consider solving this problem using heuristics. A heuristic gradually builds a solution by adding the requests to the end of the route one by one. This way, it can take advantage of the latest information when making the next decision, and give immediate response. In this paper, we propose a meta-algorithm to generate a solution given any heuristic. The meta-algorithm maintains a set of routes throughout the scheduling horizon. Whenever a new request arrives, it tries to re-generate new routes to include the new request by the heuristic. It accepts the new request if successful, and reject otherwise. Then we manually designed several heuristics, and proposed a genetic programming-based hyper-heuristic to automatically evolve heuristics. The results showed that the heuristics evolved by genetic programming significantly outperformed the manually designed heuristics.}, keywords = {genetic algorithms, genetic programming, combinatorial mathematics, mathematical programming, scheduling, vehicle routing, combinatorial optimisation problem, dynamic vehicle routing problem, evolutionary algorithms, evolving heuristics, genetic programming-based hyper-heuristic, manually designed heuristics, meta-algorithm, optimisation methods, scheduling horizon, static problems, time windows, Optimization, Real-time systems, Time factors, Vehicle dynamics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969539}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969539}}, } @INPROCEEDINGS{gaina:2017:CEC, author={R. D. Gaina and S. M. Lucas and D. Pérez-Liébana}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing}, year={2017}, editor = {Jose A. Lozano}, pages = {1956--1963}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.}, keywords = {Monte Carlo methods, computer games, evolutionary computation, tree searching, Monte Carlo tree search algorithm, general video game playing, one step look ahead, population initialization techniques, population seeding techniques, rolling horizon evolutionary algorithms, Algorithm design and analysis, Artificial intelligence, Games, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969540}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969540}}, } @INPROCEEDINGS{hernando:2017:CEC, author={L. Hernando and F. Daolio and N. Veerapen and G. Ochoa}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Local Optima Networks of the Permutation Flowshop Scheduling Problem: Makespan vs. total flow time}, year={2017}, editor = {Jose A. Lozano}, pages = {1964--1971}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Local Optima Networks were proposed to understand the structure of combinatorial landscapes at a coarse-grained level. We consider a compressed variant of such networks with features that are meaningful for the study of search difficulty in the context of local search. In particular, we investigate different landscapes of the Permutation Flowshop Scheduling Problem. The insert and 2-exchange neighbourhoods are considered, and two different objective functions are taken into account: the makespan and the total flow time. The aim is to analyse the network features in order to find differences between the landscape structures, giving insights about which features impact algorithm performance. We evaluate the correlation between landscape properties and the performance of an Iterated Local Search algorithm. Visualisation of the network structure is also given, where evident differences between the makespan and total flow time are observed.}, keywords = {flow shop scheduling, iterative methods, network theory (graphs), search problems, iterated local search algorithm, local optima networks, permutation flowshop scheduling problem, Algorithm design and analysis, Correlation, Feature extraction, Linear programming, Thin film transistors, Visualization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969541}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969541}}, } @INPROCEEDINGS{manjarres:2017:CEC, author={D. Manjarres and I. Landa-Torres and J. Del Ser}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A local search method for graph clustering heuristics based on partitional Distribution learning}, year={2017}, editor = {Jose A. Lozano}, pages = {1972--1977}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The community structure of complex networks reveals hidden relationships in the organization of their constituent nodes. Indeed, many practical problems stemming from different fields of knowledge such as Biology, Sociology, Chemistry and Computer Science can be modeled as a graph. Therefore, graph analysis and community detection have become a key component for understanding the inherent relational characteristics underlying different systems and processes. In this regard, distinct unsupervised quality metrics such as conductance, coverage and modularity, have upsurged in order to evaluate the clustering arrangements based on structural and topological characteristics of the cluster space. In this regard graph clustering can be formulated as an optimization problem based on the maximization of one of such metrics, for which a number of nature-inspired heuristic solvers has been proposed in the literature. This paper elaborates on a novel local search method that allows boosting the convergence of such heuristics by estimating and sampling the cluster arrangement distribution from the set of intermediate produced solutions of the algorithm at hand. Simulation results reveal a generalized better performance compared towards other community detection algorithms in synthetic and real datasets.}, keywords = {biomimetics, complex networks, graph theory, learning (artificial intelligence), optimisation, pattern clustering, search problems, statistical distributions, cluster arrangement distribution, cluster space, clustering arrangements, community detection algorithms, community structure, graph analysis, graph clustering heuristics, inherent relational characteristics, local search method, nature-inspired heuristic solvers, optimization problem, partitional distribution learning, topological characteristics, unsupervised quality metrics, Brightness, Clustering algorithms, Measurement, Optimization, Search methods, Simulation, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969542}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969542}}, } @INPROCEEDINGS{leon:2017:CEC, author={M. Leon and N. Xiong}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Alopex-based mutation strategy in Differential Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {1978--1984}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies.}, keywords = {evolutionary computation, optimisation, probability, CEC2013, DE/Alopex/1, alopex-based mutation strategy, benchmark suite, differential evolution algorithm, fitness values, numerical optimization problems, Algorithm design and analysis, Benchmark testing, Correlation, Optimization, Sociology, Alopex, Differential Evolution, Evolutionary Algorithm, Mutation strategy}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969543}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969543}}, } @INPROCEEDINGS{baldini:2017:CEC, author={A. Baldini and L. Ciabattoni and R. Felicetti and F. Ferracuti and A. Freddi and A. Monteriù}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Nonlinear control of a photovoltaic battery system via ABC-tuned Dynamic Surface Controller}, year={2017}, editor = {Jose A. Lozano}, pages = {1985--1991}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper proposes a control methodology based on Dynamic Surface Control (DSC) to manage the power flow of a photovoltaic (PV) battery system. In particular, due to the inner stochastic nature and intermittency of the solar production and in order to face the irradiance rapid changes, a robust and fast controller is needed. Dynamic Surface Control is a modified version of Backstepping control that avoids the explosion of terms, which is a typical drawback of the Backstepping control and furthermore it is not affected by the well known problem of chattering, which affects Sliding Mode controllers. Dynamic Surface Control is compared to the conventional Proportional-Integral-Derivative controller (PID). In particular, DSC shows better performances in terms of steady state chattering and transient response, as confirmed by the Integral of the Absolute value of Error (IAE), Integral of the Squared Error (ISE) and Integral of Time multiplied by the Absolute value of Error (ITAE) performance indexes.}, keywords = {control nonlinearities, load flow control, nonlinear control systems, performance index, photovoltaic power systems, robust control, transient response, ABC-tuned dynamic surface controller, backstepping control, nonlinear control, performance indexes, photovoltaic battery system, power flow, robust controller, solar production, steady state chattering, Backstepping, Batteries, Control systems, Integrated circuit modeling, Mathematical model, Photovoltaic systems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969544}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969544}}, } @INPROCEEDINGS{oregi:2017:CEC, author={I. Oregi and J. Del Ser and A. Pérez and J. A. Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Nature-inspired approaches for distance metric learning in multivariate time series classification}, year={2017}, editor = {Jose A. Lozano}, pages = {1992--1998}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular class of data. In this context the related literature has extensively shown that dynamic time warping is the similarity measure of choice when univariate time series are considered. However, possible statistical coupling among different dimensions make the generalization of this metric to the multivariate case all but obvious. This has ignited the interest of the community in new distance definitions capable of capturing such inter-dimension dependences. In this paper we propose a simple dynamic time warping based distance that finds the best weighted combination between the dependent - where multivariate time series are treated as whole - and independent approaches - where multivariate time series are just a collection of unrelated univariate time series - of the time series to be classified. A benchmark of four heuristic wrappers, namely, simulated annealing, particle swarm optimization, estimation of distribution algorithms and genetic algorithms are used to evolve the set of weighting coefficients towards maximizing the cross-validated predictive score of the classifiers. In this context one of the most recurring classifiers is nearest neighbor. This classifier is couple with a distance that as afore mentioned, in most cases, have been dynamic time warping. The performance of the proposed approach is validated over datasets widely utilized in the related literature, from which it is concluded that the obtained performance gains can be enlarged by properly decoupling the influence of each dimension in the definition of the dependent dynamic time warping distance.}, keywords = {genetic algorithms, learning (artificial intelligence), particle swarm optimisation, pattern classification, simulated annealing, time series, cross-validated predictive score, dependent dynamic time warping distance, distance metric learning, distribution algorithms, heuristic wrappers, inter-dimension dependences, multivariate time series classification, nearest neighbor, particle swarm optimization, recurring classifiers, similarity measure, statistical coupling, time series data mining, univariate time series, Computational modeling, Heuristic algorithms, Optimization, Time measurement, Time series analysis, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969545}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969545}}, } @INPROCEEDINGS{stefano:2017:CEC, author={C. De Stefano and F. Fontanella and A. S. di Freca}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A novel mutation operator for the evolutionary learning of Bayesian networks}, year={2017}, editor = {Jose A. Lozano}, pages = {1999--2006}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Structure learning is a very important problem in the context of Bayesian networks (BNs). For this reason, it has been largely studied in the last few years and many approaches have been presented to find an optimal structure based on training samples. In a previous paper, we proposed an evolutionary algorithm for BN structure learning based on a data structure specifically devised for encoding BNs. The proposed approach was used to combine the responses of classifier ensembles. In this paper, we present a further improvement along this direction, in that we have developed a novel mutation operator that allows a more effective exploration of the search space. The devised operator is able to modify both node ordering and the connection topology of the encoded BN. The experimental results, obtained by using five benchmark datasets, confirmed the effectiveness of the proposed approach.}, keywords = {belief networks, data structures, evolutionary computation, learning (artificial intelligence), mathematical operators, pattern classification, search problems, BN encoding, BN structure learning, Bayesian network evolutionary learning, Mutation Operator, classifier ensembles, connection topology, data structure, evolutionary algorithm, node ordering, search space, training samples, Bayes methods, Encoding, Genetics, Heuristic algorithms, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969546}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969546}}, } @INPROCEEDINGS{padillo:2017:CEC, author={F. Padillo and J. M. Luna and S. Ventura}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary algorithm for mining rare association rules: A Big Data approach}, year={2017}, editor = {Jose A. Lozano}, pages = {2007--2014}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Association rule mining is one of the most wellknown techniques to discover interesting relations between items in data. To date, this task has been mainly focused on the discovery of frequent relationships. However, it is often interesting to focus on those that do not occur frequently. Rare association rule mining is an alluring field aiming at describing rare cases or unexpected behavior. This field is really useful over Big Data where abnormal endeavor are more curious than common behavior. In this sense, our aim is to propose a new evolutionary algorithm based on grammars to obtain rare association rules on Big Data. The novelty of our work is that it is eminently designed to be parallel, enabling its use over emerging technologies as Spark and Flink. Furthermore, while other algorithms focus on maximizing a couple of quality measure ignoring the rest, our fitness function has been precisely designed to obtain a trade-off while maximizing a set of well-known quality measures. The experimental study includes more than 70 datasets revealing alluring results in efficiency when more than 300 million of instances and file sizes up to 250 GBytes are considered, and proving that it is able to run efficiently in huge volumes of data.}, keywords = {data mining, Big Data, Flink, Spark, association rules mining, evolutionary algorithm, grammars, quality measure, Algorithm design and analysis, Grammar, Proposals, Sparks, Syntactics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969547}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969547}}, } @INPROCEEDINGS{moyano:2017:CEC, author={J. M. Moyano and E. L. Gibaja and S. Ventura}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary algorithm for optimizing the target ordering in Ensemble of Regressor Chains}, year={2017}, editor = {Jose A. Lozano}, pages = {2015--2021}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this article we present an evolutionary algorithm for the optimization of sequences of targets for the multi-target regression algorithm Ensemble of Regressor Chains. This algorithm selects several random sequences or chains of targets where to predict each target, the values of previous targets in the chain are included as features, considering in this way the relationship among them. Under the assumption that a target may be better predicted if it is highly correlated with the targets which were included as feature, our proposal, called CCO-ERC, looks for chains where each target is highly correlated with previous targets in the chain. Several methods for the combination of predictions in the ensemble and for the selection of the chains which forms the ensemble are also proposed. CCO-ERC is compared to other state-of-the-art algorithms in multi-target regression, presenting statistically better performance than them.}, keywords = {algorithm theory, evolutionary computation, regression analysis, CCO-ERC, ensemble, evolutionary algorithm, multitarget regression algorithm, optimization, random sequences, regressor chains, target ordering, 5G mobile communication, Correlation, Prediction algorithms, Predictive models, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969548}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969548}}, } @INPROCEEDINGS{cagnini:2017:CEC, author={H. E. L. Cagnini and R. C. Barros and M. P. Basgalupp}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Estimation of distribution algorithms for decision-tree induction}, year={2017}, editor = {Jose A. Lozano}, pages = {2022--2029}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Decision trees are one of the most widely employed classification models, mainly due to their capability of being properly interpreted and understood by the domain specialist. However, decision-tree induction algorithms have limitations due to the typical recursive top-down greedy search they implement. Such local search may often lead to quality loss while the partitioning process occurs, generating statistically insignificant rules. In order to avoid the typical greedy strategy and to prevent convergence to local optima, we present a novel Estimation of Distribution Algorithm (EDA) for decision-tree induction, namely Ardennes. For evaluating the proposed approach, we present results of an empirical analysis in 10 real-world classification datasets. We compare Ardennes with both a well-known traditional greedy algorithm for decision-tree induction and also with a more recent global population-based approach. Results show the feasibility of using EDAs as a means to avoid the previously-described problems. We report gains when using Ardennes in terms of accuracy and - equally important - tree comprehensibility.}, keywords = {convergence, decision trees, greedy algorithms, pattern classification, search problems, Ardennes, EDA, classification model, convergence prevention, decision-tree induction algorithms, distribution algorithm estimation, empirical analysis, estimation-of-distribution algorithm, greedy algorithm, local optima, local search, partitioning process, quality loss, real-world classification datasets, recursive top-down greedy search, tree comprehensibility, Electronic mail, Entropy, Estimation, Sociology, Statistics, Vegetation}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969549}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969549}}, } @INPROCEEDINGS{rezoug:2017:CEC, author={A. Rezoug and M. Bader-El-Den and D. Boughaci}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Knowledge-based Genetic Algorithm for the 0 #x2013;1 Multidimensional Knapsack Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2030--2037}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents an improved version of Genetic Algorithm (GA) to solve the 0-1 Multidimensional Knapsack Problem (MKP01), which is a well-known NP-hard combinatorial optimisation problem. In combinatorial optimisation problems, the best solutions have usually a common partial structure. For MKP01, this structure contains the items with a high values and low weights. The proposed algorithm called Genetic Algorithm Guided by Pretreatment information (GAGP) calculates these items and uses this information to guide the search process. Therefore, GAGP is divided into two steps, in the first, a greedy algorithm based on the efficiency of each item determines the subset of items that are likely to appear in the best solutions. In the second, this knowledge is utilised to guide the GA process. Strategies to generate the initial population and calculate the fitness function of the GA are proposed based on the pretreatment information. Also, an operator to update the efficiency of each item is suggested. The pretreatment information has been investigated using the CPLEX deterministic optimiser. In addition, GAGP has been examined on the most used MKP01 data-sets, and compared to several other approaches. The obtained results showed that the pretreatment succeeded to extract the most part of the important information. It has been shown, that GAGP is a simple but very competitive solution.}, keywords = {computational complexity, genetic algorithms, greedy algorithms, knapsack problems, search problems, CPLEX deterministic optimiser, MKP01, NP-hard combinatorial optimisation problem, genetic algorithm guided by pretreatment information GAGP, greedy algorithm, knowledge-based genetic algorithm, multidimensional knapsack problem, search process, Biological cells, Electronic mail, Genetics, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969550}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969550}}, } @INPROCEEDINGS{ramírez:2017:CEC, author={A. Ramírez and J. R. Romero and S. Ventura}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On the effect of local search in the multi-objective evolutionary discovery of software architectures}, year={2017}, editor = {Jose A. Lozano}, pages = {2038--2045}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Software architects devote substantial efforts to find the most fitting architectural description for their system, which should not only specify its structure, but is also required to meet multiple, simultaneous quality criteria. Evolutionary computation has recently demonstrated to provide insightful support during the design phase by automatically deciding how to organise internal software components and how they should interact each other. Observed from a multi-objective perspective, particular care has to be taken in order to reach an appropriate trade-off among design metrics, while providing the software engineer with diverse alternatives to choose among. However, multi-objective evolutionary algorithms may find difficulties to control both aspects and, at the same time, to explore the entire search space in depth. Under these circumstances, local search can be applied to complement the evolution by scrutinising the most promising search directions. This paper proposes two different approaches that take advantage of the benefits of local search within the multi-objective evolutionary discovery of component-based software architectures. A detailed analysis and comparative study provides interesting findings like the importance of assigning a sufficient number of evaluations to the local improvement. The way in which local search explores and compares solutions for acceptance is a relevant aspect to promote diversity during the discovery process as well.}, keywords = {evolutionary computation, object-oriented programming, search problems, software architecture, component-based software architectures, local search, multiobjective evolutionary algorithms, search space, software engineer, Computer architecture, Mathematical model, Measurement, Memetics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969551}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969551}}, } @INPROCEEDINGS{marchi:2017:CEC, author={M. Marchi and L. Rizzian and S. Costanzo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multiobjective sizing optimization of a steel girder bridge with a simple Target-driven approach}, year={2017}, editor = {Jose A. Lozano}, pages = {2046--2053}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We present a simple strategy for multiobjective target-driven optimization and apply it to the sizing optimization of a steel girder bridge. Users or decision makers are asked to express their preferences (based on their previous experience) in terms of desired target objective values to drive the optimization towards the most preferred regions of the Pareto front. This can lead to a more efficient exploration of specific regions of the objective space and reduce the computational cost of finding desirable solutions. This strategy combines a-priori with interactive preference-handling approaches. These methods have recently received more attention in the evolutionary multiobjective optimization community. The proposed algorithm is described in detail and compared with existing methods. Benchmarks on standard mathematical test functions as well as on a realistic structural engineering sizing optimization problem are provided.}, keywords = {Pareto optimisation, bridges (structures), evolutionary computation, steel, supports, Pareto front, computational cost reduction, evolutionary multiobjective optimization community, interactive preference-handling approach, multiobjective sizing optimization, simple target-driven approach, steel girder bridge, structural engineering sizing optimization problem, Algorithm design and analysis, Genetic algorithms, Linear programming, Optimization, Search problems, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969552}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969552}}, } @INPROCEEDINGS{triguero:2017:CEC, author={I. Triguero and M. Galar and H. Bustince and F. Herrera}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A first attempt on global evolutionary undersampling for imbalanced big data}, year={2017}, editor = {Jose A. Lozano}, pages = {2054--2061}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models. In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance the dataset by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work with very large chromosomes and reduce the costs associated to the fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model.}, keywords = {Big Data, evolutionary computation, learning (artificial intelligence), pattern classification, Apache Spark, Big Data learning models, CHC algorithm, fitness evaluation, global evolutionary undersampling model, highly skewed distribution, imbalanced classification, very large chromosomes, Biological cells, Context, Data mining, Data models, Machine learning algorithms, Sparks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969553}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969553}}, } @INPROCEEDINGS{silva:2017:CECa, author={J. G. R. Silva and H. S. Bernardino and H. J. C. Barbosa and I. A. de Carvalho and V. da Fonseca Vieira and M. M. S. Loureiro and C. R. Xavier}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving a Multiobjective Caloric-Restricted Diet Problem using Differential Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {2062--2069}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Caloric-Restricted Diet Problem (CRDP) aims at finding diets with a reduced caloric count that also respects the nutritional needs of an individual. Thus, it is possible to achieve weight loss without compromising the individual's health. However, due to the small amount of energy contained in such diets, one may not be fully satisfied after a meal. It is possible to overcome this drawback by inserting a larger amount of protein in the diet, as it was shown to be the most effective macronutrient that provides satiety. Thus, this work presents a multi-objective mathematical formulation for the CRDP that minimizes the calorie count of the diet and maximizes the number of proteins ingested. Besides that, a Generalized Differential Evolution algorithm (GDE3) is proposed to solve the resulting problem. Computational experiments are performed with both mono-objective and multi-objective CRDP and two example diets are presented. It shows that it is possible to achieve a diet with a large amount of proteins, while restricting the caloric number.}, keywords = {evolutionary computation, health care, proteins, GDE3, caloric number restriction, differential evolution, generalized differential evolution algorithm, macronutrient, monoobjective CRDP, multiobjective CRDP, multiobjective caloric-restricted diet problem, multiobjective mathematical formulation, nutritional needs, protein, reduced caloric count, weight loss, Linear programming, Mathematical model, Optimization, Sociology, Statistics, Zinc, Multiobjective optimization, hipocaloric diet}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969554}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969554}}, } @INPROCEEDINGS{dali:2017:CEC, author={N. Dali and S. Bouamama}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Different parallelism levels using GPU for solving Max-CSPs with PSO}, year={2017}, editor = {Jose A. Lozano}, pages = {2070--2077}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Thanks to the appearance of the General-Purpose computing on Graphics Processing Units (GPGPU), researchers have benefited from the spectacular High Performance Computing (HPC) provided by GPUs. Different research fields, such as combinatorial optimization, have taken advantages from the GPUs HPC. In this context, our paper introduces some different Particle Swarm Optimization (PSO) implementations for solving Maximal-Constraint Satisfaction Problems (Max CSPs) using GPU, based on different parallelism levels. These implementations are then compared. The experimental results, presented at the end, show the effectiveness and the efficiency of using GPU to optimize Max-CSPs by PSO.}, keywords = {combinatorial mathematics, constraint satisfaction problems, graphics processing units, parallel processing, particle swarm optimisation, GPU, HPC, Max-CSP, PSO, combinatorial optimization, general-purpose computing, high performance computing, maximal-constraint satisfaction problems, parallelism levels, particle swarm optimization, Computer architecture, Context, Instruction sets, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969555}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969555}}, } @INPROCEEDINGS{kunanusont:2017:CEC, author={K. Kunanusont and S. M. Lucas and D. Pérez-Liébana}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={General Video Game AI: Learning from screen capture}, year={2017}, editor = {Jose A. Lozano}, pages = {2078--2085}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm.}, keywords = {artificial intelligence, computer games, Artificial General Intelligence research, deep Q-Network algorithm, general game playing framework, general video game AI framework, general video game artificial intelligence, learning algorithm, screen capture learning agent, video-games domain, Biological neural networks, Convolution, Feature extraction, Games, Learning (artificial intelligence), Visualization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969556}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969556}}, } @INPROCEEDINGS{altin:2017:CEC, author={L. Altin and H. R. Topcuoglu and M. Ermis}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Hybridizing change detection schemes for dynamic optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {2086--2093}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Detecting the points in time where a change occurs in the landscape can have an important role for a number of evolutionary dynamic optimization techniques presented in the literature. The two common ways for change detection are the population-based scheme and the sensor-based scheme. The former one requires statistical hypothesis testing, which periodically checks whether two consecutive populations are derived from different distributions or not. On the other hand, the latter one utilizes re-evaluation of a set of sensors, throughout the search process. The population-based change detectors may cause false positives and the sensor-based detectors may lack of distinction between changes and noise in fitness functions. In this paper, we propose a hybrid technique to overcome the limitations of the change detection schemes and validate it by using Moving Peaks Benchmark (MPB).}, keywords = {evolutionary computation, optimisation, search problems, statistical testing, MPB, change detection schemes, dynamic optimization problems, evolutionary dynamic optimization, moving peaks benchmark, population-based change detectors, search process, sensor reevaluation, sensor-based change detectors, statistical hypothesis testing, Aerodynamics, Benchmark testing, Optimization, Sensors, Sociology, Statistics, Vehicle dynamics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969557}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969557}}, } @INPROCEEDINGS{musilek:2017:CEC, author={P. Musilek and P. Krömer and R. Martins and H. C. Hesse}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal energy management of residential PV/HESS using evolutionary fuzzy control}, year={2017}, editor = {Jose A. Lozano}, pages = {2094--2101}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The adoption of residential photovoltaic power generators combined with energy storage system can reduce the energy dependency of individual households while alleviating the impact of intermittent solar energy on the electric power grid. However, to maximize the benefits, energy in such systems must be carefully managed. The first step towards development of such energy management system, described in our previous work, is determination of the optimal power flows that reflects the current and future solar energy availability and household load, as well as the state of the energy storage system. This paper builds on the optimal power flows to develop an advanced energy management system in form of a fuzzy rule base system. The time series of the optimal flows, determined using linear programming, are used to determine the parameters of a Takagi-Sugeno fuzzy controller through differential evolution. The resulting system can be implemented to control power flows in other systems composed of photovoltaic generation and energy storage. The results confirm the operational and economic benefits of using the optimal operational strategy, while allowing its in-depth analysis through the evolved fuzzy rule base.}, keywords = {energy storage, evolutionary computation, fuzzy control, linear programming, load flow control, photovoltaic power systems, power grids, solar power stations, time series, Takagi-Sugeno fuzzy controller, differential evolution, electric power grid, energy dependency, energy storage system, evolutionary fuzzy control, fuzzy rule base system, intermittent solar energy, optimal energy management system, optimal power flows, residential PV/HESS, residential photovoltaic power generators, Batteries, Energy management, Fuzzy logic, Inverters, Load flow, Production}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969558}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969558}}, } @INPROCEEDINGS{dhifli:2017:CEC, author={W. Dhifli and N. O. Da Costa and M. Elati}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary schema for mining skyline clusters of attributed graph data}, year={2017}, editor = {Jose A. Lozano}, pages = {2102--2109}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Graph clustering is one of the most important research topics in graph mining and network analysis. With the abundance of data in many real-world applications, the graph nodes and edges could be annotated with multiple sets of attributes that could be derived from heterogeneous data sources. Considering these attributes during the graph clustering could help in generating graph clusters with balanced and cohesive intra-cluster structure and nodes having homogeneous properties. In this paper, we propose a genetic algorithm-based graph clustering approach for mining skyline clusters over large attributed graphs based on the dominance relationship. Each skyline solution is optimized with respect to multiple fitness functions simultaneously where each function is defined over the graph topology or over a particular set of attributes that are derived from multiple data sources. We experimentally evaluate our approach on a real-world large protein-protein interaction network of the human interactome enriched with large sets of heterogeneous cancer associated attributes. The obtained results show the efficiency of our approach and how integrating node attributes of multiple data sources allows to obtain a more robust graph clustering than by considering only the graph topology.}, keywords = {data mining, genetic algorithms, graph theory, pattern clustering, attributed graph data mining, evolutionary schema, genetic algorithm-based graph clustering approach, graph mining, graph topology, heterogeneous cancer associated attributes, human interactome protein-protein interaction network, network analysis, skyline clusters, Biological cells, Clustering algorithms, Sociology, Statistics, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969559}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969559}}, } @INPROCEEDINGS{richter:2017:CEC, author={A. Richter and S. Menzel and M. Botsch}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Preference-guided adaptation of deformation representations for evolutionary design optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2110--2119}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A dynamic industrial design optimization requires high-quality optimization algorithms as well as adaptive representations to find the global solution for a given problem. For adapting the representation to changing environments or to new input we utilize the concept of evolvability, which in our interpretation consists of three criteria: variability, regularity, and improvement potential, where regularity and improvement potential characterize conflicting goals between exploration and exploitation. Our goal is the efficient adaptation of the representation according to a given preference weight between regularity and improvement potential. We propose a combination of two heuristics, Lloyd sampling and orthogonal least squares sampling, to initialize the adaptation process for a given preference weight. We show that this initialization improves the convergence speed of the adaptation process as well as the resulting fitness. We then realize a stepwise design optimization procedure by alternating the adaptation of the representation with optimization of the design. During the design optimization process we extract information which we exploit in the next adaptation phase. We show that an intermediate preference weight, balancing between regularity and improvement potential, allows to exploit this information and is robust to erroneous initial information. Thereby, we increase the performance of the whole design optimization process.}, keywords = {deformation, design engineering, evolutionary computation, least squares approximations, sampling methods, Lloyd sampling, deformation representation, dynamic industrial design optimization, evolutionary design optimization, evolvability concept, improvement potential criteria, orthogonal least squares sampling, preference-guided adaptation, regularity criteria, variability criteria, Aerodynamics, Data mining, Design optimization, Kernel, Shape, Space exploration}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969560}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969560}}, } @INPROCEEDINGS{strumberger:2017:CEC, author={I. Strumberger and N. Bacanin and M. Tuba}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Enhanced firefly algorithm for constrained numerical optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2120--2127}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Firefly algorithm is one of the recent and very promising swarm intelligence metaheuristics for tackling hard optimization problems. While firefly algorithm has been proven on various numerical and engineering optimization problems as a robust metaheuristic, it was not properly tested on a wide set of constrained benchmark functions. We performed testing of the original firefly algorithm on a set of standard 13 benchmark functions for constrained problems and it exhibited certain deficiencies, primarily insufficient exploration during early stage of the search. In this paper we propose enhanced firefly algorithm where main improvements are correlated to the hybridization with the exploration mechanism from another swarm intelligence algorithm, introduction of new exploitation mechanism and parameter-based tuning of the exploration-exploitation balance. We tested our approach on the same standard benchmark functions and showed that it not only overcame weaknesses of the original firefly algorithm, but also outperformed other state-of-the-art swarm intelligence algorithms.}, keywords = {evolutionary computation, constrained numerical optimization, enhanced firefly algorithm, exploitation mechanism, exploration-exploitation balance, hard optimization problems, parameter-based tuning, swarm intelligence algorithm, swarm intelligence metaheuristics, Algorithm design and analysis, Benchmark testing, Optimization, Particle swarm optimization, Robustness, Standards}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969561}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969561}}, } @INPROCEEDINGS{ruotsalainen:2017:CEC, author={M. Ruotsalainen and J. Jylhä}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning of a tracker model from multi-radar data for performance prediction of air surveillance system}, year={2017}, editor = {Jose A. Lozano}, pages = {2128--2136}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A valid model of the air surveillance system performance is highly valued when making decisions related to the optimal control of the system. We formulate a model for a multi-radar tracker system by combining a radar performance model with a tracker performance model. A tracker as a complex software system is hard to model mathematically and physically. Our novel approach is to utilize machine learning to create a tracker model based on measurement data from which the input and target output for the model are calculated. The measured data comprises the time series of 3D coordinates of cooperative aircraft flights, the corresponding target detection recordings from multiple radars, and the related multi-radar track recordings. The collected data is used to calculate performance measures for the radars and the tracker at specific locations in the air space. We apply genetic programming to learning such rules from radar performance measures that explain tracker performance. The easily interpretable rules are intended to reveal the real behavior of the system providing comprehension for its control and further development. The learned rules allow predicting tracker performance level for the system control in all radar geometries, modes, and conditions at any location. In the experiments, we show the feasibility of our approach to learning a tracker model and compare our rule learner with two tree classifiers, another rule learner, a neural network, and an instance-based classifier using the real air surveillance data. The tracker model created by our rule learner outperforms the models by the other methods except for the neural network whose prediction performance is equal.}, keywords = {genetic algorithms, genetic programming, aircraft control, learning (artificial intelligence), neurocontrollers, object detection, optimal control, radar tracking, surveillance, time series, air space, air surveillance system performance, aircraft flights, instance-based classifier, machine learning, multiradar data, multiradar track recordings, multiradar tracker system, neural network, radar geometries, radar performance model, rule learner, target detection, tracker performance model, Atmospheric modeling, Radar detection, Radar measurements, Spaceborne radar, Target tracking}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969562}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969562}}, } @INPROCEEDINGS{picek:2017:CEC, author={S. Picek and K. Knezevic and D. Jakobovic}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={On the evolution of bent (n, m) functions}, year={2017}, editor = {Jose A. Lozano}, pages = {2137--2144}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Boolean functions and their generalizations, vectorial Boolean functions, are extremely active areas of research. Their applications can be found in domains such as error correcting codes, communication, and cryptography. Accordingly, various methods of obtaining Boolean functions are explored where one group belongs to heuristic techniques and, more precisely, evolutionary algorithms. In this paper we explore how to evolve (vectorial) Boolean functions with specific properties by utilizing several different algorithms and encodings. As far as we are aware, we are the first to explore the topic of evolution of vectorial Boolean functions where the output dimension is strictly smaller than the input dimension. Our results show that evolutionary algorithms can represent a valuable option to produce vectorial Boolean functions where good results are obtained for various sizes. On the other hand, as the number of outputs grows, we can observe that evolutionary algorithms are still able to obtain high quality results but with increasing difficulty.}, keywords = {genetic algorithms, genetic programming, Boolean functions, evolutionary computation, vectors, bent functions, evolutionary algorithms, heuristic techniques, input dimension, output dimension, vectorial Boolean functions, Ciphers, Encoding}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969563}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969563}}, } @INPROCEEDINGS{liu:2017:CECc, author={J. Liu and D. Pérez-Liébana and S. M. Lucas}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Bandit-based Random Mutation Hill-Climbing}, year={2017}, editor = {Jose A. Lozano}, pages = {2145--2151}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi-armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm significantly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive.}, keywords = {evolutionary computation, OneMax problem, Royal Road problem, bandit-based random mutation hill-climbing algorithm, direct search technique, discrete optimisation problem, fitness evaluation, independent multiarmed bandit-style selection units, Bioinformatics, Genomics, Noise measurement, Optimization, Roads, Standards, OneMax, RMHC, Royal Road, bandit}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969564}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969564}}, } @INPROCEEDINGS{segura:2017:CEC, author={C. Segura and E. Segredo and G. Miranda}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles}, year={2017}, editor = {Jose A. Lozano}, pages = {2152--2160}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In recent years, several memetic algorithms with explicit mechanisms to delay convergence have shown great promise when solving 9×9 Sudoku puzzles. This paper analyzes and extends state-of-the-art schemes for dealing with Sudoku puzzles of larger dimensionality. Two interesting aspects are analyzed: the importance of the encoding and its relation with the way of managing the diversity. Specifically, three different ways of encoding the individuals and six different methods, including four that control the diversity in a special way, are studied. Computational results are shown with twenty 16×16 Sudoku puzzles. Contrary to the low-dimensional case, important differences appear among the several ways of controlling diversity. Specifically, a method that incorporates multi-objective concepts in the replacement phase to deal with the diversity, resulted in the most promising method. Results show that both the encoding and the way of managing diversity are crucial to attain high success probabilities in large Sudoku puzzles. They also show that, while the analyzed encodings induce different search space sizes, this feature is not enough to justify the differences in the performance attained by them.}, keywords = {encoding, game theory, search problems, diversity control, individual encoding, large Sudoku puzzles, low-dimensional case, memetic algorithms, multiobjective concepts, replacement phase, search space sizes, success probabilities, Convergence, Electronic mail, Genetics, Memetics, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969565}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969565}}, } @INPROCEEDINGS{lipinsk:2017:CEC, author={P. Lipinski}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving the Firefighter Problem with two elements using a multi-modal Estimation of Distribution Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {2161--2168}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Firefighter Problem (FFP) is an optimization problem of developing an optimal strategy for assigning firemen to nodes of a given graph in successive iterations of a simulation of spread of fires in the graph. This paper focusses on an extension of the original FFP, namely the Bi-Firefighter Problem (FFP2), where the second element (water) is introduced. FFP2 corresponds to the practical optimization problems, where more than one disease is spreading in the environment, and the objective is to minimize the total loss. Since the loss may come from two different sources, each of which causes different damages, the objective function is more complex than in the case of the original FFP. In this paper, an evolutionary approach to FFP2, the EA-FFP2 algorithm, based on a multi-modal Estimation of Distribution Algorithm (EDA), is proposed. EA-FFP2 was validated on a number of benchmark FFP2 instances that were also solved by the branch and bound algorithms or the heuristic local search algorithms run for a large number of iterations for a long time. Computational experiments confirmed that EA-FFP2 was capable of solving FFP2 and finding solutions close to the optima determined by the branch and bound algorithms or to the quasi-optima determined by exhaustive local search.}, keywords = {evolutionary computation, fires, graph theory, iterative methods, optimisation, tree searching, EA-FFP2 algorithm, EDA, bi-firefighter problem, branch and bound algorithms, evolutionary approach, heuristic local search algorithms, multi-modal estimation of distribution algorithm, optimization problem, Estimation, Floods, Heuristic algorithms, Linear programming, Optimization, Social network services}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969566}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969566}}, } @INPROCEEDINGS{antipov:2017:CEC, author={D. Antipov and A. Buzdalova}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives}, year={2017}, editor = {Jose A. Lozano}, pages = {2169--2176}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In certain optimization problems, aside from the target objective, auxiliary objectives can be used. These auxiliary objectives may be either helpful or not. Often we can not determine whether an auxiliary objective is helpful. In this work we consider the EA+RL method that dynamically chooses auxiliary objectives in random local search using reinforcement learning. This method's runtime has already been theoretically analysed on different monotonic functions, and it was shown that EA+RL can exclude harmful auxiliary objectives from consideration. EA+RL has also shown good results on different real-world problems. However, it has not been theoretically analysed whether this method can efficiently optimize non-monotonic functions using simple evolutionary algorithms and reinforcement learning agents. In this paper we consider optimization of the non-monotonic JUMP function with the EA+RL method. We use two auxiliary objectives. One of them is helpful during the first phase of optimization and another one is helpful during the last phase. On other stages they are constant, so they neither help nor slow optimization down. We show that EA+RL has at least Ω(ℓ/n) probability of solving this problem in polynomial time using random local search, which is impossible for the conventional random local search without learning. We also propose a modification of EA+RL that is guaranteed to find the optimum.}, keywords = {evolutionary computation, learning (artificial intelligence), search problems, EA+RL method, auxiliary objectives, evolutionary algorithms, nonmonotonic JUMP function, random local search, reinforcement based selection, reinforcement learning, runtime analysis, Algorithm design and analysis, Optimization, Runtime, Upper bound}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969567}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969567}}, } @INPROCEEDINGS{gaur:2017:CEC, author={A. Gaur and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Effect of size and order of variables in rules for multi-objective repair-based innovization procedure}, year={2017}, editor = {Jose A. Lozano}, pages = {2177--2184}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Innovization is a task of learning common principles that exist among some or all of the Pareto-optimal solutions of a multi-objective optimization problem. Except a few earlier studies, most innovization related studies were performed on the final non-dominated solutions found by an EMO algorithm. Recently, authors showed that these principles can be learned during an optimization run and simultaneously utilized in the same optimization run to repair variables to achieve a faster convergence to the Pareto-optimal set. Different principles learned during an optimization run can not only have different number of variables but, may also have variables that are common among a number of principles. Moreover, a preference order for repairing variables may play an important role for proper convergence. Thus, when multiple principles exist, it is important to use a strategy that is most beneficial for repairing evolving population of solutions. This paper makes a first attempt to assess and understand the effect of different strategies to make innovization-based repair of variables most useful. Based on results on test problems, the paper also makes useful suggestions, which require immediate further experimentation on more complex and real-world problems.}, keywords = {Pareto optimisation, convergence, EMO algorithm, Pareto-optimal set, Pareto-optimal solutions, common principles learning, multiobjective optimization problem, multiobjective repair-based innovization procedure, repair variables, repairing variables, Algorithm design and analysis, Maintenance engineering, Mathematical model, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969568}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969568}}, } @INPROCEEDINGS{baldominos:2017:CEC, author={A. Baldominos and P. Isasi and Y. Saez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Feature selection for physical activity recognition using genetic algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {2185--2192}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Physical activity is widely known to be a key factor towards achieving a healthy life and reducing the chance of developing certain diseases. However, there are many different physical activities having different effort requirements or having different benefits on health. The reason why automatic recognition of physical activity is useful is twofold: first, it raises personal awareness about the physical activity a user is carrying out and its impact on health, allowing some apps to give proper credit for it; second, it allows medical staff to monitor the activity levels of patients. In this paper, we follow a proven activity recognition chain to learn a classifier for physical activity recognition, which is trained using data from PAMAP2, a dataset publicly available in UCI ML repository. Once a machine learning dataset is created after signal preprocessing, segmentation and feature extraction, we will explore and compare different feature selection techniques using genetic algorithms in order to maximize the accuracy and reduce the number of dimensions. This reduction improves classification times and reduces costs and energy consumption of sensor devices. By doing so, we have reduced dimensions to almost a half and we have outperformed the best results found so far in literature with an average accuracy of 97.45%.}, keywords = {feature extraction, feature selection, genetic algorithms, learning (artificial intelligence), medical signal processing, PAMAP2, UCI ML repository, feature selection techniques, machine learning dataset, personal awareness, physical activity recognition, Activity recognition, Biomedical monitoring, Diseases, Heart rate, Monitoring}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969569}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969569}}, } @INPROCEEDINGS{velasco:2017:CEC, author={J. M. Velasco and O. Garnica and S. Contador and J. Lanchares and E. Maqueda and M. Botella and J. I. Hidalgo}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data}, year={2017}, editor = {Jose A. Lozano}, pages = {2193--2200}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Diabetes Mellitus Type 1 patients are waiting for the arrival of the Artificial Pancreas. Artificial Pancreas systems will control the blood glucose of patients, improving their quality of life and reducing the risks they face daily. At the core of the Artificial Pancreas, an algorithm will forecast future glucose levels and estimate insulin bolus sizes. Grammatical Evolution has been proved as a suitable algorithm for predicting glucose levels. Nevertheless, one of the main obstacles that researches have found for training the Grammatical Evolution models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is very complex along with the fact that the patient's response can vary in a high degree due to a lot of personal factors which can be seen as different scenarios. In this paper, we propose both a classification system for scenario selection and a data augmentation algorithm that generates synthetic glucose time series from real data. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using scenario selection and data augmentation.}, keywords = {biochemistry, blood, diseases, evolutionary computation, learning (artificial intelligence), medical computing, pattern classification, sugar, artificial pancreas systems, blood glucose control, blood glucose level prediction, classification system, data augmentation, data collection, diabetes mellitus type 1 patients, evolutionary algorithms, grammatical evolution model, insulin bolus sizes, patient response, personal factors, scenario selection, training data scarcity, Data models, Grammar, Insulin, Predictive models, Time series analysis}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969570}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969570}}, } @INPROCEEDINGS{kunanusont:2017:CECa, author={K. Kunanusont and R. D. Gaina and J. Liu and D. Perez-Liebana and S. M. Lucas}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The N-Tuple bandit evolutionary algorithm for automatic game improvement}, year={2017}, editor = {Jose A. Lozano}, pages = {2201--2208}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple algorithm.}, keywords = {computer games, evolutionary computation, mobile agents, search problems, AI agents, AI-assisted game design, N-tuple bandit evolutionary algorithm, Space Battle game optimisation, automatic game improvement, fitness estimation, game quality estimation, general video game AI agents, search space exploitation, search space exploration, stochastic algorithms, unsampled points, Aerospace electronics, Artificial intelligence, Games, Marine vehicles, Missiles, Tuning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969571}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969571}}, } @INPROCEEDINGS{pascual:2017:CEC, author={J. A. Pascual and J. Lant and A. Attwood and C. Concatto and J. Navaridas and M. Luján and J. Goodacre}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Designing an exascale interconnect using multi-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2209--2216}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Exascale performance will be delivered by systems composed of millions of interconnected computing cores. The way these computing elements are connected with each other (network topology) has a strong impact on many performance characteristics. In this work we propose a multi-objective optimization-based framework to explore possible network topologies to be implemented in the EU-funded ExaNeSt project. The modular design of this system's interconnect provides great flexibility to design topologies optimized for specific performance targets such as communications locality, fault tolerance or energy-consumption. The generation procedure of the topologies is formulated as a three-objective optimization problem (minimizing some topological characteristics) where solutions are searched using evolutionary techniques. The analysis of the results, carried out using simulation, shows that the topologies meet the required performance objectives. In addition, a comparison with a well-known topology reveals that the generated solutions can provide better topological characteristics and also higher performance for parallel applications.}, keywords = {evolutionary computation, multiprocessor interconnection networks, network topology, parallel processing, EU-funded ExaNeSt project, computing core interconnection, evolutionary techniques, exascale interconnect design, exascale performance, multiobjective optimization, network topologies, parallel computing, Fault tolerance, Fault tolerant systems, Measurement, Optimization, Sociology, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969572}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969572}}, } @INPROCEEDINGS{li:2017:CECl, author={H. Li and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Challenges for evolutionary multiobjective optimization algorithms in solving variable-length problems}, year={2017}, editor = {Jose A. Lozano}, pages = {2217--2224}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In recent years, research interests have been paid in solving real-world optimization problems with variable-length representation. For population-based optimization algorithms, the challenge lies in maintaining diversity in sizes of solutions and in designing a suitable recombination operator for achieving an adequate diversity. In dealing with multiple conflicting objectives associated with a variable-length problem, the resulting multiple trade-off Pareto-optimal solutions may inherently have different variable sizes. In such a scenario, the fixed recombination and mutation operators may not be able to maintain large-sized solutions, thereby not finding the entire Pareto-optimal set. In this paper, we first construct multiobjective test problems with variable-length structures, and then analyze the difficulties of the constructed test problems by comparing the performance of three state-of-the-art multiobjective evolutionary algorithms. Our preliminary experimental results show that MOEA/D-M2M shows good potential in solving the multiobjective test problems with variable-length structures due to its diversity strategy along different search directions. Our correlation analysis on the Pareto solutions with variable sizes in the Pareto front indicates that mating restriction is necessary in solving variable-length problem.}, keywords = {Pareto optimisation, evolutionary computation, Pareto-optimal solution, correlation analysis, evolutionary multiobjective optimization algorithms, mating restriction, multiobjective test problems, mutation operator, population-based optimization algorithm, recombination operator, variable-length problems, variable-length representation, variable-length structures, Electronic mail, Optimization, Shape, Sociology, Space exploration, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969573}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969573}}, } @INPROCEEDINGS{jaiswal:2017:CEC, author={S. K. Jaiswal and H. Iba}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Coevolution of mapping functions for linear SVM}, year={2017}, editor = {Jose A. Lozano}, pages = {2225--2232}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A linear SVM scales linearly with the size of a dataset, and hence is very desirable as a classifier for large datasets. However, it is not able to classify a dataset having a nonlinear decision boundary between the classes unless the dataset has been transformed by some mapping function so that the decision boundary becomes linear or it is a good approximation to a linear boundary. Often these mapping functions may result in a dataset with very large dimension or even infinite dimension. To avoid the curse of dimensionality, kernel functions are used as mapping functions. However, a kernel SVM has quadratic time complexity, and hence does not scale very well with large datasets. Moreover, the choice of a kernel function and its parameter optimization are arduous tasks. Therefore, a replacement of kernel function with an explicit mapping function is desirable in the case of large datasets. In this paper, we propose a novel co-evolutionary approach to find an explicit mapping function. We use GA to evolve an n-tuple of GP trees as a mapping function, and GP to evolve each individual GP tree. The dataset is then transformed using the found mapping function so that a linear SVM can be used. Besides the fact that the proposed algorithm allows us to use a fast linear SVM, the results also show that the proposed algorithm outperforms the kernel trick and even performs as good as the kernel trick combined with feature selection.}, keywords = {genetic algorithms, genetic programming, computational complexity, feature selection, pattern classification, support vector machines, trees (mathematics), GA, GP tree n-tuple evolution, coevolutionary approach, dataset classifier, explicit mapping function, infinite dimension, kernel functions, linear SVM, mapping function coevolution, nonlinear decision boundary, parameter optimization, quadratic time complexity, Kernel, Optimization, Sociology, Statistics, Symbiosis, Vegetation, co-evolutionary algorithm, feature extraction, feature map, genetic algorithm, mapping function}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969574}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969574}}, } @INPROCEEDINGS{carvalho:2017:CEC, author={L. C. F. Carvalho and M. A. Fernandes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A Simple Estimation of Distribution Algorithm for the Flexible Job-Shop Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2233--2239}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Flexible Job-Shop Problem (FJSP) is one of the most complicated scheduling problems. Estimation of Distribution Algorithms (EDA) are evolutionary techniques based on probabilistic models. In this paper, a Simple Estimation of Distribution Algorithms (SEDA) is presented to solve the Multi-Objective FJSP (MOFJSP). The probabilistic model proposed in SEDA is based on simple count operations, which consider the best solutions of one algorithm iteration. Despite the simplicity, the algorithm presents similar results when compared to other algorithms to the FJSP including some EDAs.}, keywords = {evolutionary computation, job shop scheduling, probability, estimation of distribution algorithms, evolutionary techniques, flexible job-shop problem, multiobjective FJSP, probabilistic models, simple estimation of distribution algorithms, Estimation, Indexes, Probabilistic logic, Scheduling, Sequential analysis, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969575}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969575}}, } @INPROCEEDINGS{rakshit:2017:CECa, author={P. Rakshit and A. Chowdhury and A. Konar and A. K. Nagar}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Differential evolution induced many objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2240--2247}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We propose a novel approach to solve the many objective optimization (MaOO) problem using a ranking policy, instead of the Pareto ranking, supposing that a solution is unlikely to perform well for all objectives in a MaOO problem. A solution is thus evolved with respect to a specific objective only, which it may proficiently optimize. First, all objectives of the MaOO problem are individually optimized by evolutionary algorithms in parallel. The second step is concerned with judiciously selecting and filtering the quality solutions obtained by individual optimization of all objectives in parallel. A unique ranking policy is proposed to grade the members of the union set of quality solutions based on their extent of optimization of individual objectives. The evolutionary algorithm used for parallel optimization of all objectives in a MaOO here has been realized with differential evolution (DE). The mutation strategy of DE is also amended here with an aim to allow controlled communication between population members, concerned with parallel optimization of different objectives of a MaOO problem. Experiments undertaken with DTLZ and WFG test suits reveal that the proposed algorithm outperforms the state-of-art techniques with respect to inverted generational distance and hypervolume metrics.}, keywords = {evolutionary computation, DE mutation strategy, MaOO problem, differential evolution induced many objective optimization, hypervolume metrics, inverted generational distance, parallel evolutionary algorithms, parallel optimization, quality solution filtering, quality solutions, ranking policy, union set grading, Convergence, Linear programming, Object recognition, Optimization, Search problems, Sociology, Statistics, differential evolution, hypervolume, many-objective optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969576}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969576}}, } @INPROCEEDINGS{feng:2017:CECa, author={X. Feng and M. Ji and Z. Li and X. Qu and B. Liu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Team effectiveness based optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2248--2257}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={During the past two decades, developing and improving intelligent optimization algorithms (IOAs) has been one of the hottest research topics in evolutionary computing. Most of the IOAs are inspired by natural phenomena, and shown to be highly effective in solving complex optimization problems. In this study, we propose a new population based metaheuristic algorithm named Team Effectiveness Based Optimization (TEBO), which is inspired from human society rather than natural world. The high-intelligence of human beings together with the research achievements in the field of team effectiveness motivate the development of this novel optimization algorithm. In this paper, we present how the members learn and cooperate in a team environment in order to improve the overall team performance. We investigate the proposed TEBO on a comprehensive set of 30 benchmark problems in CEC 2014 competition on Single Objective Real-Parameter Numerical Optimization. The results confirm the competitiveness of the proposed algorithm comparing to other state-of-the-art intelligent optimization algorithms, including invasive weed optimization (IWO), biogeography-based optimization (BBO), gravitational search algorithm (GSA), hunting search (HuS), bat algorithm (BA) and water wave optimization (WWO).}, keywords = {optimisation, search problems, BBO, GSA, HuS, IOA, IWO, TEBO, WWO, bat algorithm, biogeography-based optimization, gravitational search algorithm, human-inspired metaheuristic algorithm, hunting search, intelligent optimization algorithms, invasive weed optimization, novel optimization algorithm, single objective real-parameter numerical optimization, team effectiveness based optimization, water wave optimization, Algorithm design and analysis, Benchmark testing, Optimization, Organizations, Sociology, Statistics, Training, human-inspired algorithm, intelligent optimization algorithm (IOA), population based metaheuristic algorithm, team effective based optimization (TEBO)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969577}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969577}}, } @INPROCEEDINGS{adhikari:2017:CEC, author={D. Adhikari and E. Kim and H. Reza}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A fuzzy adaptive differential evolution for multi-objective 3D UAV path optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2258--2265}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a fuzzy adaptive differential evolution (DE) for 3D UAV path planning. The path-planning problem is formulated as a multi-objective unconstrained optimization problem in the aim of minimizing the fuel and the threat cost as well as finding the shortest path. A fuzzy logic controller is used to find the parameter values of DE during this optimization process. The mutation operation of DE is modified in a way to strike a balance between the DE/rand/1 and DE/best/1 strategies. This method is compared with both DE/rand/1 and DE/best/1 and it relatively outperformed both the classical variations. As it is often tedious to find the right parameter values for DE, this method will give freedom from the process of finding the parameter values for the acceptable performance in 3D path planning optimization.}, keywords = {adaptive control, autonomous aerial vehicles, evolutionary computation, fuzzy control, path planning, 3D UAV path planning, DE/best/1 strategies, DE/rand/1 strategies, fuel minimization, fuzzy adaptive differential evolution, fuzzy logic controller, multiobjective 3D UAV path optimization, multiobjective unconstrained optimization, mutation operation, Fuzzy logic, Fuzzy sets, Optimization, Sociology, Statistics, Unmanned aerial vehicles, UAV path planning, differential evolution}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969578}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969578}}, } @INPROCEEDINGS{liaw:2017:CEC, author={R. T. Liaw and C. K. Ting}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary many-tasking based on biocoenosis through symbiosis: A framework and benchmark problems}, year={2017}, editor = {Jose A. Lozano}, pages = {2266--2273}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary multitasking is an emergent topic in evolutionary computation area. Recently, a well-known evolutionary multitasking method, the multi-factorial evolutionary algorithm (MFEA), has been proposed and applied to concurrently solve two or three problems. In MFEA, individuals of different tasks are recombined in a predefined random mating probability. As the number of tasks increases, such recombination of different tasks becomes very frequent, thereby detracting the search from any specific problems and limiting the MFEA's capability to solve many-tasking problems. This study proposes a general framework, called the evolution of biocoenosis through symbiosis (EBS), for evolutionary algorithms to deal with the many-tasking problems. The EBS has two main features: the selection of candidates from concatenate offspring and the adaptive control of information exchange among tasks. The concatenate offspring represent a set of offspring used for all tasks. Moreover, this study presents a test suite of many-tasking problems (MaTPs), modified from the CEC 2014 benchmark problems. The Spearman correlation is adopted to analyze the effect of the shifts of optima on the MaTPs. Experimental results show that the effectiveness of EBS is superior to that of single task optimization and MFEA on the four MaTPs. The results also validate that EBS is capable of exploiting the synergy of fitness landscapes.}, keywords = {evolutionary computation, probability, random processes, search problems, EBS, MFEA, MaTP, Spearman correlation, adaptive control, concatenate offspring, evolution-of-biocoenosis-through-symbiosis, evolutionary many-tasking, evolutionary multitasking, fitness landscape synergy, information exchange, multifactorial evolutionary algorithm, random mating probability, Benchmark testing, Multitasking, Optimization, Sociology, Statistics, Symbiosis, CMAES, benchmark problems, framework, many-tasking}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969579}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969579}}, } @INPROCEEDINGS{lin:2017:CECa, author={Yuefeng Lin and Wenli Du and T. Stützle}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Three L-SHADE based algorithms on mixed-variables optimization problems}, year={2017}, editor = {Jose A. Lozano}, pages = {2274--2281}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Success History-based Adaptive Differential Evolution algorithm with Linear Population Size Reduction (L-SHADE) is a highly competitive continuous optimizer. In this paper, we extend L-SHADE towards handling mixed variable optimization problems, where some of the variables may be categorical, ordinal, integer or continuous. For handling the discrete variables, we consider three different options. The first is to adapt the mechanism for handling categorical variables introduced in an earlier proposed ant colony optimization algorithm, called ACO_MV to use it with L-SHADE (L-SHADE_MV ). The second is to apply a simpler scheme that relies on the mechanisms how ant colony optimization algorithms traditionally tackle discrete problems (L-SHADE_ACO ). The third is to introduce a mechanism that exploits the ideas of DE within the generation of new values for the categorical variables, by introducing specific rules to be applied in the mutation (L-SHADE_RULE ). We use an automatic algorithm configuration tool for optimizing the algorithms' parameters to allow an unbiased comparison of the three schemes. The performance of the proposed algorithms was evaluated on artificial mixed-variable optimization problems and compared with L-SHADE that incorporates a simple rounding scheme to handle categorical variables, which is used as a baseline method. The experimental results show that all three categorical handling methods outperform the baseline and L-SHADE_ACO is the best performing scheme in terms of accuracy and robustness.}, keywords = {ant colony optimisation, category theory, evolutionary computation, ACO_MV , L-SHADE based algorithms, L-SHADE_ACO , L-SHADE_MV , L-SHADE_RULE , algorithm parameter optimization, ant colony optimization algorithm, artificial mixed-variable optimization problems, automatic algorithm configuration tool, categorical variables, continuous variables, discrete problems, discrete variables, integer variables, ordinal variables, rounding scheme, specific rules, success history-based adaptive differential evolution algorithm-with-linear population size reduction, Algorithm design and analysis, Ant colony optimization, Benchmark testing, Optimization, Relaxation methods, Sociology, Statistics, L-SHADE, artificial mixed-variable benchmark functions, automatic parameter tuning, differential evolution, mixed-variable optimization problems}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969580}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969580}}, } @INPROCEEDINGS{lopes:2017:CEC, author={H. B. Lopes and F. V. C. Martins and R. T. N. Cardoso and V. F. dos Santos}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Combining rules and proportions: A multiobjective approach to algorithmic composition}, year={2017}, editor = {Jose A. Lozano}, pages = {2282--2289}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In recent times, several activities that lately were considered possible to do only by human work, have been replaced by the use of artificial intelligence. Recent researches have been developed to give computers a creativity ability as main objective. A type of related research is algorithmic composition. Traditionally evolutionary algorithms are successfully used for composing tasks, using a wide variety of fitness functions. This work presents a multiobjective approach that combines Fux's rules, from music theory, and functions that capture melodies' proportions, based on the Zipf's law, to compose monophonic melodies. Besides that, we propose a method that measures the amount of creativity contained in an algorithmic composition method. Experimental results show that the proposed methods have a good musical quality.}, keywords = {artificial intelligence, evolutionary computation, music, Fux rules, Zipf law, algorithmic composition, evolutionary algorithms, melodies proportions, monophonic melodies, multiobjective approach, music theory, Bars, Genetic algorithms, Genetics, Linear regression, Measurement, multiobjective algorithms, music composition}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969581}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969581}}, } @INPROCEEDINGS{mahdavi:2017:CECa, author={S. Mahdavi and S. Rahnamayan and C. Karia}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Analyzing effects of ordering vectors in mutation schemes on performance of Differential Evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {2290--2298}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Differential Evolution (DE) is a simple powerful evolutionary algorithm for solving global continuous optimization problems. The especial characteristic of DE algorithm is calculating a weighted difference vector of two random candidate solutions in the population to generate the new promising candidate solutions. A major operation of the DE algorithm is the mutation which can affect its performance. The main goal of this study is investigating the influence of ordering vectors on various mutation schemes. We design some Monte-Carlo based simulations to analyze several mutation schemes by calculating the probability of closeness of a new trial solutions to a random optimal solution. These simulations indicate that mutation schemes can enhance the performance of the DE algorithm which they consider right ordering of the vectors in their mutation operators. Also, we introduce a new mutation scheme which considers in ordering vectors in the mutation scheme. We benchmark the modified DE algorithm with the ordered mutation scheme (DE/order) on CEC-2014 test functions with three dimensions 30, 50, and 100. Simulation results confirm that DE/order obtains a promising performance on the majority of the test functions on all mentioned dimensions.}, keywords = {Monte Carlo methods, evolutionary computation, optimisation, probability, vectors, CEC-2014 test functions, DE algorithm, DE/order, Monte-Carlo based simulations, closeness probability calculation, differential evolution, evolutionary algorithm, global continuous optimization problems, mutation operators, ordered mutation scheme, ordering vectors, weighted difference vector, Algorithm design and analysis, Analytical models, Linear programming, Optimization, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969582}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969582}}, } @INPROCEEDINGS{liu:2017:CECd, author={J. Liu and J. Togelius and D. Pérez-Liébana and S. M. Lucas}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Game Skill-Depth using General Video Game AI agents}, year={2017}, editor = {Jose A. Lozano}, pages = {2299--2307}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games. One of the hardest parts of this approach is defining a suitable fitness function. In this paper we explore the possibility of using one of a growing set of General Video Game AI agents to perform automatic play-testing. This enables a very general approach to game evaluation based on estimating the skill-depth of a game. Agent-based play-testing is computationally expensive, so we compare two simple but efficient optimisation algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random Mutation Hill-Climber. For the test game we use a space-battle game in order to provide a suitable balance between simulation speed and potential skill-depth. Results show that both algorithms are able to rapidly evolve game versions with significant skill-depth, but that choosing a suitable resampling number is essential in order to combat the effects of noise.}, keywords = {computer games, multi-agent systems, optimisation, automatic play-testing, game skill-depth, general video game AI agents, multi armed bandit random mutation hill-climber, random mutation hill climbing algorithm, space-battle game, Algorithm design and analysis, Artificial intelligence, Evolutionary computation, Games, Marine vehicles, Missiles, Optimization, Automatic game design, GVG-AI, RMHC, game tuning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969583}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969583}}, } @INPROCEEDINGS{rios:2017:CEC, author={B. H. O. Rios and E. F. G. Goldbarg and G. Y. O. Quesquén}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A hybrid metaheuristic using a corrected formulation for the Traveling Car Renter Salesman Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2308--2314}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Traveling Car Renter Problem (CaRS) is a generalization of the Traveling Salesman Problem. This paper presents a hybrid metaheuristic approach to deal with CaRS: an evolutionary algorithm (ScA) and the hybrid method Adaptive Local Search Procedure (ALSP), denoted by ScA+ALSP. A mixed integer programming model proposed for CaRS is corrected and used within the ALSP. The results of experimental studies using a suite of 21 instances taken from the literature indicated that the hybrid ScA+ALSP is competitive regarding the best known algorithm in literature for non-Euclidean CaRS instances. Three new best results are reported.}, keywords = {evolutionary computation, integer programming, search problems, transportation, travelling salesman problems, ALSP, ScA, adaptive local search procedure, evolutionary algorithm, hybrid metaheuristic, mixed integer programming, non-Euclidean CaRS, traveling car renter salesman problem, Adaptation models, Automobiles, Linear programming, Reactive power, Traveling salesman problems, Urban areas}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969584}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969584}}, } @INPROCEEDINGS{sousa:2017:CEC, author={R. S. Sousa and T. W. de Lima and L. C. M. de Paula and R. L. Lima and A. R. G. Filho and A. S. Soares}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Integer-based genetic algorithm for feature selection in multivariate calibration}, year={2017}, editor = {Jose A. Lozano}, pages = {2315--2320}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Feature selection is a important tast to reduce dimensionality in large datasets. Datasets from multivariate calibration problems are a good example lf datasets with a large number of features. In literature, there are several types of techniques to reduce the number of features for this problem, among them, evolutionary algorithms such as genetic algorithms (GAs). They have been successfully used with binary encoding to select features in multivariate calibration. However, as far as we know, there is no work in literature which provides an integer encoding GA in such context. Thus, this paper presents an integer-based GA implementation for feature selection in multivariate calibration models. The results demonstrated that our proposal is able to outperform the outcomes of participants from 2014 IDRC regarding model prediction error as well as number of selected features. In this dataset, the samples correspond to oils from petroleum reservoirs around the world and gas mixtures in the gas phase measured in transmittance. The gain of our proposed implementation in relation to the winner was from 20.9% up to 88.8%.}, keywords = {feature selection, genetic algorithms, integer programming, 2014 IDRC, binary encoding, dimensionality reduction, evolutionary algorithms, gas mixtures, integer-based GA implementation, integer-based genetic algorithm, model prediction error, multivariate calibration models, petroleum reservoirs, Calibration, Encoding, Mathematical model, Predictive models, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969585}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969585}}, } @INPROCEEDINGS{oliveira:2017:CECb, author={P. H. C. Oliveira and G. Moreira and D. M. Ushizima and C. M. Carneiro and F. N. S. Medeiros and F. H. D. de Araujo and R. R. V. e Silva and A. G. C. Bianchi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A multi-objective approach for calibration and detection of cervical cells nuclei}, year={2017}, editor = {Jose A. Lozano}, pages = {2321--2327}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The automation process of Pap smear analysis holds the potential to address women's health care in the face of an increasing population and respective collected data. A fundamental step for automating analysis is cell detection from light microscopy images. Such information serves as input to cell classification algorithms and diagnostic recommendation tools. This paper describes an approach to nuclei cell segmentation, which critically impacts the following steps for cell analyses. We developed an algorithm combining clustering and genetic algorithms to detect image regions with high diagnostic value. A major problem when performing the segmentation of images is the cellular overlay. We introduce a new nuclear targeting approach using heuristics associated with a multi-objective genetic algorithm. Our experiments show results using a public 45-image dataset, including comparison to other cell detection approaches. The findings suggest an improvement in the nuclei segmentation and promise to support more sophisticated schemes for data quality control.}, keywords = {calibration, genetic algorithms, health care, image classification, image segmentation, medical image processing, nucleus, optical microscopy, Pap smear analysis, cell classification algorithms, cervical cells nuclei detection, data quality control, diagnostic recommendation tools, light microscopy images, multiobjective approach, nuclei segmentation, Automation, Clustering algorithms, Image color analysis, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969586}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969586}}, } @INPROCEEDINGS{kalra:2017:CEC, author={S. Kalra and S. Rahnamayan and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Enhancing clearing-based niching method using Delaunay Triangulation}, year={2017}, editor = {Jose A. Lozano}, pages = {2328--2337}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The interest in multi-modal optimization methods is increasing in the recent years since many of real-world optimization problems have multiple/many optima and decision makers prefer to find all of them. Multiple global/local peaks create difficulties for optimization algorithms. In this context, niching is well-known and widely used technique for finding multiple solutions in multi-modal optimization. One commonly used niching technique in evolutionary algorithms is the Clearing method. However, canonical clearing scheme reduces the exploration capacity of the evolutionary algorithms. In this paper, Delaunay Triangulation based Clearing (DT-Clearing) procedure is proposed to handle multi-modal optimizations more efficiently while preserving simplicity of canonical clearing approach. In DT-Clearing, cleared individuals are reallocated in the biggest empty spaces formed within the search space which are determined through Delaunay Triangulation. The reallocation of cleared individuals discourages wasting of the resources and allows better exploration of the landscape. The algorithm also uses an external memory, an archive of the explored niches, thus preventing the redundant visiting of the individuals, henceforth finding more solutions in lesser number of generations. The method is tested using multi-modal benchmark problems proposed for the IEEE CEC 2013, Special Session on Niching Methods for Multimodal Optimization. Our method obtains promising results in comparison with the canonical clearing and demonstrates to be a competitive niching algorithm.}, keywords = {evolutionary computation, mesh generation, optimisation, search problems, DT-clearing approach, Delaunay triangulation-based clearing procedure, canonical clearing approach, clearing-based niching method enhancement, evolutionary algorithm, external memory, multimodal benchmark problem, multimodal optimization, multimodal optimization method, search space, Context, Genetic algorithms, Genetics, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969587}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969587}}, } @INPROCEEDINGS{baldominos:2017:CECa, author={A. Baldominos and C. Ramón-Lozano}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimizing EEG energy-based seizure detection using genetic algorithms}, year={2017}, editor = {Jose A. Lozano}, pages = {2338--2345}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Epilepsy is one of the most common neurological conditions, affecting 2.2 million people only in the U.S., causing seizures that can have a very serious impact in affected people's lives, including death. Because of this, there is a remarkable research interest in detecting epilepsy as it occurs, so that it effects and consequences can be mitigated immediately. In this paper, we describe and implement an energy-based seizure detection algorithm which runs over electroencephalography (EEG) signals. Because this technique comprises different parameters that significantly affect the detection performance, we will use genetic algorithms (GAs) to optimize these parameters in order to improve the detection accuracy. In this paper, we describe the GA setup, including the encoding and fitness function. Finally, we evaluate the implemented algorithm with the optimized parameters over a subset of the CHB-MIT Scalp EEG Database, a public data set available in PhysioNet. Results have shown to be very diverse, attaining almost perfect accuracy for some patients with very low false positive rate, but failing to properly detect seizures in others. Thus, the limitations found for energy-based seizure detection are discussed and some actions are proposed to address these issues.}, keywords = {electroencephalography, genetic algorithms, medical disorders, medical signal processing, neurophysiology, signal detection, CHB-MIT scalp EEG database, EEG energy-based seizure detection optimization, PhysioNet public data set, electroencephalography signals, epilepsy detection, neurological conditions, Epilepsy, Feature extraction, Neurons, Scalp}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969588}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969588}}, } @INPROCEEDINGS{vitali:2017:CEC, author={J. L. Vitali and M. C. Riff and E. Montero}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Bus Routing for emergency evacuations: The case of the Great Fire of Valparaiso}, year={2017}, editor = {Jose A. Lozano}, pages = {2346--2353}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Bus Evacuation Problem is a route planning problem, in the context of an evacuation in an emergency situation. Considering that public transport is available to support the evacuation, the objective of the problem is to determine the best route for each vehicle, to move all the people from a risk zone to open shelters located in safe zones, such that the evacuation time is minimized. In this work we present a method based on the Greedy Randomized Adaptive Search Procedure metaheuristic to solve the problem, in order to apply the solution to a real-world scenario based on a recent wildfire on Valparaíso, Chile. In computational experiments we demonstrate that our approach is effective to solve real-world size problems, and able to outperform a commercial MIP solver.}, keywords = {search problems, transportation, bus evacuation problem, emergency evacuations, greedy randomized adaptive search procedure metaheuristic, route planning problem, Context, Proposals, Routing, Schedules, Vehicle routing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969589}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969589}}, } @INPROCEEDINGS{androulakakis:2017:CEC, author={P. Androulakakis and Z. E. Fuchs}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary design of engagement strategies for turn-constrained agents}, year={2017}, editor = {Jose A. Lozano}, pages = {2354--2363}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We examine the use of an evolutionary algorithm to design a feedback controller for a dual pursuit-evasion problem. In this problem, two players, Player A and Player B, move about an obstacle-free, two-dimensional plane with constant speeds and bounded turn rates. Player A strives to capture Player B by maneuvering behind and closing within a defined capture distance. Simultaneously, Player B is attempting to capture Player A while avoiding being captured itself. Although the general form of this problem is two-sided, we examine the design of strategies for Player A against a collection of possible adversarial strategies implemented by Player B. We pose a nearest neighbor switching control structure that is represented using a parameterized matrix. An evolutionary algorithm is utilized to evolve these parameters in order to develop a feedback controller for Player A to efficiently capture Player B while evading capture itself.}, keywords = {collision avoidance, evolutionary computation, feedback, game theory, matrix algebra, switching systems (control), adversarial strategies, bounded turn rates, capture evasion, constant speeds, dual pursuit-evasion problem, engagement strategies, evolutionary algorithm, evolutionary design, feedback controller, nearest neighbor switching control structure, obstacle-free two-dimensional plane, parameterized matrix, turn-constrained agents, Adaptive control, Aerospace electronics, Games, Optimal control, Switches}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969590}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969590}}, } @INPROCEEDINGS{ayodele:2017:CECa, author={M. Ayodele and J. McCall and O. Regnier-Coudert and L. Bowie}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A Random Key based Estimation of Distribution Algorithm for the Permutation Flowshop Scheduling Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2364--2371}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Random Key (RK) is an alternative representation for permutation problems that enables application of techniques generally used for continuous optimisation. Although the benefit of RKs to permutation optimisation has been shown, its use within Estimation of Distribution Algorithms (EDAs) has been a challenge. Recent research proposing a RK-based EDA (RK-EDA) has shown that RKs can produce competitive results with state of the art algorithms. Following promising results on the Permutation Flowshop Scheduling Problem, this paper presents an analysis of RK-EDA for optimising the total flow time. Experiments show that RK-EDA outperforms other permutation-based EDAs on instances of large dimensions. The difference in performance between RK-EDA and the state of the art algorithms also decreases when the problem difficulty increases.}, keywords = {flow shop scheduling, optimisation, RK-EDA, RK-based EDA, continuous optimisation, permutation flowshop scheduling problem, random key based estimation of distribution algorithm, Algorithm design and analysis, Estimation, Histograms, Optimization, Probabilistic logic, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969591}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969591}}, } @INPROCEEDINGS{ibrahim:2017:CEC, author={A. Ibrahim and M. V. Martin and S. Rahnamayan and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Fusion-based hybrid many-objective optimization algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {2372--2381}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the last three decades there have been a number of efficient multi-objective optimization algorithms capable of solving real-world problems. However, due to the complexity of most real-world problems (high-dimensionality of problems, computationally expensive, and unknown function properties) researchers and decision-makers are increasingly facing the challenge of selecting an optimization algorithm capable of solving their hard problems. In this paper, we propose a simple yet efficient hybridization of multi- and many-objective optimization algorithms framework called hybrid many-objective optimization algorithm using fusion of solutions obtained by several many-objective algorithms (fusion) to gain the combined benefits of several algorithms and reducing the challenge of choosing one optimization algorithm to solve complex problems. During the optimization process, the Fusion framework (1) executes all optimization algorithms in parallel, (2) it combines solutions of these algorithms and extracts well-distributed solutions using predefined structured reference points or user-defined reference points, and (3) adaptively selects best-performing algorithm to tackle the problem at different stages of the search process. A case study of the fusion framework by considering GDE3, SMPSO, and SPEA2 as multi-objective optimization algorithms is presented. Experimental results on five unconstrained and four constrained benchmark test problems with three to ten objectives show that the Fusion framework significantly outperforms all algorithms involved in the hybridization process as well as the NSGA-III algorithm in terms of diversity and convergence of obtained solutions. Furthermore, the proposed framework is consistently able to find accurate solutions for all test problems which can be interpreted as its high robustness characteristic.}, keywords = {genetic algorithms, search problems, sensor fusion, GDE3 algorithm, NSGA-III algorithm, SMPSO algorithm, SPEA2 algorithm, best-performing algorithm, constrained benchmark test problems, fusion-based hybrid many-objective optimization algorithm, multiobjective optimization algorithms framework, predefined structured reference points, search process, unconstrained benchmark test problems, user-defined reference points, Algorithm design and analysis, Computers, Evolutionary computation, Optimization, Sociology, Software algorithms, Statistics, Algorithms fusion, GDE3, Hybrid optimization, Many-objective optimization, NSGA-III, Reference-point-based optimization, SMPSO, SPEA2}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969592}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969592}}, } @INPROCEEDINGS{rojas:2017:CEC, author={A. Rojas and E. Montero and M. C. Riff}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={G-DMP: An algorithm without constraint relaxation to solve the train departure matching problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2382--2389}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this work we present a GRASP based approach for solving the departure matching problem, an important subproblem of the Rolling Stock Units Management problem. Our approach implements a constructive and a Local Search steps that are able to deal with all the constraints and costs related to the problem. We evaluate our approach using two sets of instances: a set of randomly generated instances and the set of instances used in ROADEF/EURO Challenge 2014.}, keywords = {railway engineering, railway rolling stock, search problems, G-DMP, GRASP based approach, constraint relaxation, local search, rolling stock units management, train departure matching problem, Linear programming, Maintenance engineering, Optimal scheduling, Routing, Schedules, Simulated annealing}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969593}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969593}}, } @INPROCEEDINGS{maharana:2017:CEC, author={D. Maharana and R. Kommadath and P. Kotecha}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Dynamic Yin-Yang Pair Optimization and its performance on single objective real parameter problems of CEC 2017}, year={2017}, editor = {Jose A. Lozano}, pages = {2390--2396}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Yin-Yang Pair Optimization is a recently developed metaheuristic technique which searches for the global optima through two stages namely splitting stage and archive stage. A variant of this algorithm is proposed in this work by converting a static archive updating interval to a dynamic one. The performance of this variant is evaluated on the CEC2017 test suite for single objective bound constrained real-parameter numerical optimization problems that is used for the Special Session and Competition of CEC 2017. The results obtained by this algorithm reveals its competitive performance.}, keywords = {evolutionary computation, CEC 2017, dynamic yin-yang pair optimization, metaheuristic technique, single objective bound constrained real-parameter numerical optimization problems, single objective real parameter problems, Chemical engineering, Heuristic algorithms, Hybrid power systems, Iron, Linear programming, Optimization, Two dimensional displays, Single Objective Optimization, Yin-Yang Pair Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969594}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969594}}, } @INPROCEEDINGS{kommadath:2017:CEC, author={R. Kommadath and P. Kotecha}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Teaching Learning Based Optimization with focused learning and its performance on CEC2017 functions}, year={2017}, editor = {Jose A. Lozano}, pages = {2397--2403}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this work, we propose a variant to the Teaching Learning Based Optimization algorithm by incorporating focused learning of students. A student undergoes focused learning phase only when it is unable to obtain a better solution in the teacher phase and is expected to efficiently utilize the limited functional evaluations. The performance of this variant is evaluated on the single objective bound constrained real-parameter numerical optimization problems which have been proposed as a part of IEEE Congress on Evolutionary Computation. The proposed variant has provided competitive results in most of the problems.}, keywords = {evolutionary computation, optimisation, teaching, CEC2017 function, IEEE congress on evolutionary computation, constrained real-parameter numerical optimization, limited functional evaluations, single objective bound numerical optimization, student focused learning, teacher phase, teaching learning based optimization algorithm, Algorithm design and analysis, Benchmark testing, Education, Hybrid power systems, Optimization, Sociology, Statistics, CEC 2017, Focused Learning, Single objective optimization, Teaching Learning Based Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969595}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969595}}, } @INPROCEEDINGS{wen:2017:CEC, author={Y. W. Wen and C. K. Ting}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Parting ways and reallocating resources in evolutionary multitasking}, year={2017}, editor = {Jose A. Lozano}, pages = {2404--2411}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary multitasking aims to explore implicit synergy among multiple optimization tasks. Through the effect of hitchhiking, evolutionary multitasking is capable of improving the performance of evolutionary algorithms on exploration as well as exploitation. Multifactorial evolutionary algorithm (MFEA) presented an effectual implementation of evolutionary multitasking, which simultaneously seeks the solutions to multiple optimization problems by unifying their search spaces. The MFEA enables information sharing across tasks during evolution. This mechanism can improve the evolutionary efficiency in the early phase; however, it will impair the exploitation and consume extra resources later on, due to the essential difference among the fitness landscapes of optimization problems. This study proposes detecting the occurrence of parting ways, at which the information sharing begins to fail. In addition, we develop the resource allocation mechanism to reallocate the fitness evaluations on different types of offspring by ceasing information sharing when parting ways. Experiments are conducted to evaluate the proposed methods. The experimental results show that applying parting ways detection and resource reallocation for MFEA can achieve better solution quality in most of testing cases, especially when the tasks share low similarity of landscapes.}, keywords = {evolutionary computation, resource allocation, search problems, MFEA, evolutionary efficiency, evolutionary multitasking, exploitation, exploration, fitness evaluations, fitness landscapes, hitchhiking, implicit synergy, information sharing, multifactorial evolutionary algorithm, optimization tasks, parting ways, resources reallocation, search spaces, Biological cells, Information management, Multitasking, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969596}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969596}}, } @INPROCEEDINGS{sinha:2017:CEC, author={A. Sinha and T. Soun and K. Deb}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary bilevel optimization using KKT proximity measure}, year={2017}, editor = {Jose A. Lozano}, pages = {2412--2419}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Bilevel optimization problems are often reduced to single level using Karush-Kuhn-Tucker (KKT) conditions; however, there are some inherent difficulties when it comes to satisfying the KKT constraints strictly. In this paper, we discuss single level reduction of a bilevel problem using approximate KKT conditions which have been recently found to be more useful than the original and strict KKT conditions. We embed the recently proposed KKT proximity measure idea within an evolutionary algorithm to solve bilevel optimization problems. The idea is tested on a number of test problems and comparison results have been provided against a recently proposed evolutionary algorithm for bilevel optimization. The proposed idea leads to significant savings in lower level function evaluations and shows promise in further use of KKT proximity measures in bilevel optimization algorithm development.}, keywords = {evolutionary computation, KKT proximity measure, Karush-Kuhn-Tucker conditions, evolutionary algorithm, evolutionary bilevel optimization, level function evaluations, Linear programming, Measurement, Optimization, Radiation detectors, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969597}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969597}}, } @INPROCEEDINGS{ain:2017:CEC, author={Q. Ul Ain and Bing Xue and H. Al-Sahaf and M. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic programming for skin cancer detection in dermoscopic images}, year={2017}, editor = {Jose A. Lozano}, pages = {2420--2427}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Development of an effective skin cancer detection system can greatly assist the dermatologist while significantly increasing the survival rate of the patient. To deal with melanoma detection, knowledge of dermatology can be combined with computer vision techniques to evolve better solutions. Image classification can significantly help in diagnosing the disease by accurately identifying the morphological structures of skin lesions responsible for developing cancer. Genetic Programming (GP), an emerging Evolutionary Computation technique, has the potential to evolve better solutions for image classification problems compared to many existing methods. In this paper, GP has been utilized to automatically evolve a classifier for skin cancer detection and also analysed GP as a feature selection method. For combining knowledge of dermatology and computer vision techniques, GP has been given domain specific features provided by the dermatologists as well as Local Binary Pattern features extracted from the dermoscopic images. The results have shown that GP has significantly outperformed or achieved comparable performance compared to the existing methods for skin cancer detection.}, keywords = {genetic algorithms, genetic programming, cancer, computer vision, feature selection, image classification, medical image processing, patient diagnosis, GP, computer vision techniques, dermoscopic images, disease diagnosis, domain specific features, evolutionary computation technique, feature selection method, local binary pattern features, melanoma detection, patient survival rate, skin cancer detection, Feature extraction, Image color analysis, Malignant tumors, Mutual information, Sensitivity, Skin, Skin cancer}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969598}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969598}}, } @INPROCEEDINGS{poudel:2017:CEC, author={B. Poudel and S. J. Louis and A. Munir}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving side-channel resistant reconfigurable hardware for elliptic curve cryptography}, year={2017}, editor = {Jose A. Lozano}, pages = {2428--2436}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We propose to use a genetic algorithm to evolve novel reconfigurable hardware to implement elliptic curve cryptographic combinational logic circuits. Elliptic curve cryptography offers high security-level with a short key length making it one of the most popular public-key cryptosystems. Furthermore, there are no known sub-exponential algorithms for solving the elliptic curve discrete logarithm problem. These advantages render elliptic curve cryptography attractive for incorporating in many future cryptographic applications and protocols. However, elliptic curve cryptography has proven to be vulnerable to non-invasive side-channel analysis attacks such as timing, power, visible light, electromagnetic, and acoustic analysis attacks. In this paper, we use a genetic algorithm to address this vulnerability by evolving combinational logic circuits that correctly implement elliptic curve cryptographic hardware that is also resistant to simple timing and power analysis attacks. Using a fitness function composed of multiple objectives - maximizing correctness, minimizing propagation delays and minimizing circuit size, we can generate correct combinational logic circuits resistant to non-invasive, side channel attacks. To the best of our knowledge, this is the first work to evolve a cryptography circuit using a genetic algorithm. We implement evolved circuits in hardware on a Xilinx Kintex-7 FPGA. Results reveal that the evolutionary algorithm can successfully generate correct, and side-channel resistant combinational circuits with negligible propagation delay.}, keywords = {combinational circuits, field programmable gate arrays, genetic algorithms, minimisation, public key cryptography, Xilinx Kintex-7 FPGA, circuit size minimization, cryptographic applications, cryptographic protocols, elliptic curve cryptographic combinational logic circuits, elliptic curve cryptographic hardware, elliptic curve discrete logarithm, evolutionary algorithm, fitness function, genetic algorithm, noninvasive side channel attacks, power analysis attacks, propagation delay minimization, public-key cryptosystems, security-level, side-channel resistant reconfigurable hardware, timing attacks, Algorithm design and analysis, Elliptic curve cryptography, Elliptic curves, Hardware, reconfigurable hardware design, side-channel attacks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969599}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969599}}, } @INPROCEEDINGS{chen:2017:CECe, author={A. H. L. Chen and Y. C. Liang and J. D. Padilla}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Using discrete Differential Evolution and entropy to solve the MRCPSP}, year={2017}, editor = {Jose A. Lozano}, pages = {2437--2442}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Up to date, there is no standard guideline to generate schedules capable of withstanding the inherent uncertainty of projects. Moreover, even though the research in scheduling and resource allocation in project management is vast, the practice differs significantly. This study aims to deliver a stable, efficient and practical methodology capable of generating robust baseline schedules. To achieve this, the authors use a discrete version of Differential Evolution within a previously proposed and tested framework and the results improve significantly when compared to it.}, keywords = {entropy, evolutionary computation, project management, resource allocation, scheduling, MRCPSP, discrete differential evolution, robust baseline schedules, Optimization, Robustness, Schedules, Uncertainty, differential evolution, multi-mode resource constrained project scheduling problem}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969600}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969600}}, } @INPROCEEDINGS{zamud:2017:CEC, author={A. Zamuda}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Adaptive constraint handling and Success History Differential Evolution for CEC 2017 Constrained Real-Parameter Optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2443--2450}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), enhanced with adaptive constraint violation handling, applied to the benchmark for CEC 2017 Competition on Constrained Real-Parameter Optimization. The constraint handling method prioritizes the feasible solutions before infeasible, while disregarding the constraint violation values below an adaptive threshold, i.e. adaptive ε-constraint handling. The 28 constrained test functions on 10, 30, 50, and 100 dimensions are assessed on the benchmark and the required resulting final fitnesses, constraints violations, and success rates are reported for 25 independent runs of the proposed algorithm under the budget of fixed maximum number of fitness evaluations for 10, 30, 50, and 100 dimensional test functions.}, keywords = {constraint handling, evolutionary computation, mathematics computing, optimisation, CEC 2017 constrained real-parameter optimization, L-SHADE, adaptive ε-constraint handling, adaptive constraint violation handling, adaptive threshold, constraint violation values, constraint violations, fitness evaluations, success history differential evolution, success rates, Benchmark testing, Electrical engineering, History, Iron, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969601}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969601}}, } @INPROCEEDINGS{pérez-castro:2017:CEC, author={N. Pérez-Castro and A. Márquez-Grajales and H. G. Acosta-Mesa and E. Mezura-Montes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Full Model Selection issue in temporal data through evolutionary algorithms: A brief review}, year={2017}, editor = {Jose A. Lozano}, pages = {2451--2457}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this article, a brief literature review of Full Model Selection (FMS) for temporal data is presented. An analysis of FMS approaches which use evolutionary algorithms to exploit and explore the vast search space found in this kind of problem is presented. The primary motivation of this review is to highlight the scarce published works of FMS in temporal databases. Moreover, a taxonomy for the tasks derived of FMS is proposed and chosen to discuss the different revised approaches. Also, the most representative assessment measures for model selection are described. From the literature review, a set of opportunities and challenges research is presented in the temporal FMS area.}, keywords = {evolutionary computation, feature selection, search problems, temporal databases, evolutionary algorithms, full model selection issue, representative assessment measures, search space, temporal FMS area, temporal data, Computational efficiency, Data mining, Data models, Databases, Frequency modulation, Machine learning algorithms, Time series analysis}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969602}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969602}}, } @INPROCEEDINGS{vishwanathan:2017:CEC, author={A. Vishwanathan and P. Cheema and G. Vio}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-particle swarm optimization used to study material degradation in aeroelastic composites including probabalistic uncertainties}, year={2017}, editor = {Jose A. Lozano}, pages = {2458--2464}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Extensive research has been done in optimizing simple composite wings to maximize their flutter/divergence speeds by varying ply orientation angles. This paper extends upon previous work by simulating material degradation in multiple layers, thereby adding a dynamic component to the optimization problem. A multiple-swarm variation (MPSO*) of the canonical particle swarm algorithm is used in this paper, along with a Non-Intrusive Polynomial Chaos (NIPC) model to estimate a mean flutter speed and obtain a robust optimal ply orientation. Results show that employing the MPSO* algorithm is well suited to track the time-varying optimum ply orientation. As expected, good correlation is also obtained between the NIPC analysis and Monte-Carlo simulation, and this was observed even with second and third order polynomials with modest oversampling. Results also indicate Young's modulus variations in the outer layer impact flutter velocity much more than the middle layer, however only a small change is noted in the optimal robust topology compared to the deterministic orientation.}, keywords = {Monte Carlo methods, Young's modulus, aerospace components, elasticity, particle swarm optimisation, polynomials, probability, MPSO algorithm, Monte-Carlo simulation, NIPC analysis, aeroelastic composites, composite wings, flutter-divergence speeds, impact flutter velocity, material degradation, multiparticle swarm optimization, multiple-swarm variation, nonintrusive polynomial chaos model, optimal robust topology, optimization problem, probabalistic uncertainties, second order polynomials, third order polynomials, Aerodynamics, Damping, Degradation, Mathematical model, Optimization, Robustness, Uncertainty}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969603}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969603}}, } @INPROCEEDINGS{liu:2017:CECe, author={S. Liu and S. J. Louis and Tianyi Jiang and Rui Wu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Increasing physics realism when evolving micro behaviors for 3D RTS games}, year={2017}, editor = {Jose A. Lozano}, pages = {2465--2472}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We attack the problem of evolving high performance micro behaviors in 3D RTS-game-like simulations. Prior work had shown the potential for the Meta-Search approach to evolve high performance micro for RTS games like StarCraft. We extend this work by moving to 3D and by moving to more realistic physics for simulating the movement of entities in our RTS-game-like simulation. We compare the evolved micro performance of our entities with different physics models of motion on the same scenarios against identical opponent units in a 3D RTS simulation. Results show that our genetic algorithm approach works to reliably evolve high quality 3D micro behaviors for entities independent of the physics model used. Furthermore, experiments show that the entity's acceleration has more of an effect on performance than rotation speed. Our work provides evidence for the generalizability of an evolutionary approach to generating complex behavior for 3D RTS games, training simulations, and real-world unmanned vehicles.}, keywords = {computer games, computer graphics, genetic algorithms, physics computing, 3D RTS-game-like simulations, evolutionary approach, evolving high performance microbehaviors, genetic algorithm, physics, real-time strategy games, Artificial intelligence, Games, Mathematical model, Metasearch, Solid modeling, Three-dimensional displays}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969604}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969604}}, } @INPROCEEDINGS{starke:2017:CEC, author={S. Starke and N. Hendrich and J. Zhang}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A memetic evolutionary algorithm for real-time articulated kinematic motion}, year={2017}, editor = {Jose A. Lozano}, pages = {2473--2479}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Solving kinematic motion is a challenging field of research which is relevant for various applications in character animation and robotics. This paper presents a novel and fast hybrid evolutionary algorithm for inverse kinematics which can handle fully constrained and highly articulated geometries with multiple end effectors and individual objectives. Several experiments on the 42 DoF human body mannequin and other kinematic models demonstrate a robust multimodal and multi-objective optimisation, and the ability to evolve accurate solutions in real-time while offering maximum flexibility for the design of custom cost functions.}, keywords = {end effectors, evolutionary computation, manipulator kinematics, 42 DoF human body mannequin, fully constrained geometries, highly articulated geometries, hybrid evolutionary algorithm, inverse kinematics, memetic evolutionary algorithm, multimodal optimisation, multiobjective optimisation, real-time articulated kinematic motion, Geometry, Kinematics, Optimization, Sociology, Statistics, Character Animation, Games, Hybrid Evolutionary Algorithms, Multi-Objective Optimisation, Robotics, Swarm Intelligence}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969605}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969605}}, } @INPROCEEDINGS{necula:2017:CEC, author={R. Necula and M. Breaban and M. Raschip}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Tackling Dynamic Vehicle Routing Problem with Time Windows by means of ant colony system}, year={2017}, editor = {Jose A. Lozano}, pages = {2480--2487}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an ongoing manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristic, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.}, keywords = {ant colony optimisation, search problems, vehicle routing, DVRPTW, ant colony optimization based algorithm, ant colony system, dynamic vehicle routing problem-with-time windows, insertion heuristic, joint solution construction mechanism, local search procedure, vehicle capacity constraint, Algorithm design and analysis, Heuristic algorithms, Time factors, Vehicle dynamics, Windows}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969606}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969606}}, } @INPROCEEDINGS{shi:2017:CECa, author={J. C. Shi and C. Qian and Y. Yu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary multi-objective optimization made faster by sequential decomposition}, year={2017}, editor = {Jose A. Lozano}, pages = {2488--2493}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Multi-objective evolutionary algorithms (MOEAs) can be mainly divided into set approximation methods and decomposition methods. The former approximates the Pareto front by the whole population directly, while the latter solves decomposed subproblems. The theoretical understanding of these methods is, however, quite insufficient. In this paper, we try to gain more understanding by investigating a combination of set approximation MOEAs with a sequential decomposition mechanism. Our theoretical analysis shows that, the combination achieves a better running time than the corresponding set approximation MOEAs by a factor n (the problem size) on synthetic problems as well as the minimum spanning tree problem, which hints that the two types of MOEAs might be mutually complemental.}, keywords = {approximation theory, evolutionary computation, trees (mathematics), MOEA, evolutionary multiobjective optimization, minimum spanning tree problem, multiobjective evolutionary algorithms, sequential decomposition mechanism, set approximation methods, Algorithm design and analysis, Linear programming, Pareto optimization, Search problems, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969607}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969607}}, } @INPROCEEDINGS{mısı:2017:CEC, author={M. Mısır}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Data sampling through collaborative filtering for algorithm selection}, year={2017}, editor = {Jose A. Lozano}, pages = {2494--2501}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Algorithm selection has been studied to specify the best possible algorithm(s) for a given problem instance. One of the major drawbacks of the algorithm selection methods is their need for the performance data. The performance data involves the performance of a set of algorithms on a group of problem instances. Depending on the problem domain, algorithms and the experimental settings, generating such data can be computationally expensive. ALORS [1] as a collaborative filtering based algorithm selection strategy addresses this issue by performing matrix completion. Matrix completion allows to generate algorithm selection models when the performance data is incomplete. Although ALORS is able to deal with varying data incompleteness levels, it ignores the quality and cost of the performance data. The present study offers a collaborative filtering based sampling strategy to designate which algorithm(s) to run on which instance(s). The goal is to provide either computationally cheap or highly informative incomplete performance data for algorithm selection.}, keywords = {collaborative filtering, data handling, matrix algebra, sampling methods, ALORS, algorithm selection methods, collaborative filtering based algorithm selection strategy, collaborative filtering based sampling strategy, data sampling, matrix completion, performance data, varying data incompleteness levels, Algorithm design and analysis, Classification algorithms, Clustering algorithms, Collaboration, Filtering, Machine learning algorithms, Prediction algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969608}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969608}}, } @INPROCEEDINGS{aksoy:2017:CEC, author={A. Aksoy and S. Louis and M. H. Gunes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Operating system fingerprinting via automated network traffic analysis}, year={2017}, editor = {Jose A. Lozano}, pages = {2502--2509}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Operating System (OS) detection significantly impacts network management and security. Current OS classification systems used by administrators use human-expert generated network signatures for classification. In this study, we investigate an automated approach for classifying host OS by analyzing the network packets generated by them without relying on human experts. While earlier approaches look for certain packets such as SYN packets, our approach is able to use any TCP/IP packet to determine the host systems' OS. We use genetic algorithms for feature subset selection in three machine learning algorithms (i.e., OneR, Random Forest and Decision Trees) to classify host OS by analyzing network packets. With the help of feature subset selection and machine learning, we can automatically detect the difference in network behaviors of OSs and also adapt to new OSs. Results show that the genetic algorithm significantly reduces the number of packet features to be analyzed while increasing the classification performance.}, keywords = {feature selection, genetic algorithms, learning (artificial intelligence), operating systems (computers), pattern classification, telecommunication traffic, OS classification, TCP/IP packet, automated network traffic analysis, classification performance, feature subset selection, machine learning algorithms, operating system fingerprinting, Databases, Feature extraction, Linux, Protocols, Tools, Genetic algorithm, Machine learning}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969609}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969609}}, } @INPROCEEDINGS{chica:2017:CEC, author={M. Chica and R. Chiong and M. T. P. Adam and S. Damas and T. Teubner}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary trust game for the sharing economy}, year={2017}, editor = {Jose A. Lozano}, pages = {2510--2517}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Through systematic simulation experiments, five different scenarios with varying proportions and types of providers and consumers were considered. Our results show that each type of players influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Our findings may have important implications for understanding the emergence of trust in the context of sharing economy transactions.}, keywords = {commerce, economics, evolutionary computation, game theory, evolutionary trust game, population perspective, sharing economy transactions, sharing economy trust model, systematic simulation experiments, untrustworthy consumer, untrustworthy provider, Computational modeling, Context, Games, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969610}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969610}}, } @INPROCEEDINGS{burnett:2017:CEC, author={A. W. Burnett and A. J. Parkes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Exploring the landscape of the space of heuristics for local search in SAT}, year={2017}, editor = {Jose A. Lozano}, pages = {2518--2525}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called `WalkSAT'. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space.}, keywords = {genetic algorithms, genetic programming, computability, search problems, WalkSAT, clustering, combinatorial optimisation problems, evolutionary methods, local search, propositional satisfiability, search space landscapes, Computer science, Measurement, Reactive power, Systematics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969611}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969611}}, } @INPROCEEDINGS{alinodehi:2017:CEC, author={S. P. H. Alinodehi and S. J. Louis and S. Moshfe and M. Nicolescu}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A modified steady state genetic algorithm suitable for fast pipelined hardware}, year={2017}, editor = {Jose A. Lozano}, pages = {2526--2533}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, a modification of steady state genetic algorithm, called dual-population scheme, is proposed to improve its execution speed on electronic hardware. It utilizes two memories to store two interactive populations on them. The system, inherently interchanges chromosomes between these populations. In this manner, it can fully benefit from the pipeline processing on hardware. It is shown that the proposed method performs much faster than the standard steady state and canonical genetic algorithms on pipelined genetic hardware. Moreover, the searching performance, repeatability and convergence properties of the proposed technique were tested. They show dual-population scheme performs similarly to the regular genetic algorithms while achieves better results than the present hardware-oriented genetic algorithm models.}, keywords = {genetic algorithms, pipeline processing, canonical genetic algorithms, chromosomes, dual-population scheme, electronic hardware, fast pipelined hardware, hardware-oriented genetic algorithm models, interactive populations, pipelined genetic hardware, steady state genetic algorithm, Biological cells, Hardware, Pipelines, Sociology, Statistics, Steady-state, fast GA, hardware genetic algorithm, pipeline}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969612}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969612}}, } @INPROCEEDINGS{allard:2017:CEC, author={U. C. Allard and G. Dubé and R. Khoury and L. Lamontagne and B. Gosselin and F. Laviolette}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Time Adaptive Dual Particle Swarm Optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2534--2543}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper presents a novel particle swarm optimization (PSO) algorithm that combines the strengths of several PSO variants into a single competitive algorithm. This novel algorithm, named Time Adaptive Dual Particle Swarm Optimization (TAD-PSO), is comprised of two specialized populations, with one focusing on exploration of the search space and the other on exploitation. The main population, specialized in exploration, uses orthogonal learning to create information-rich exemplars which intelligently guide particle movement throughout the search space. The auxiliary population uses a PSO variant known for its very fast convergence speed, and thus very high performance on unimodal problems. This population is specialized in exploitation of the interesting local minima. The main population size decays linearly, to foster exploration early and convergence in the later stages of the optimization procedure. Additionally, TAD-PSO does not have the topological structure of the swarm as an algorithm hyper-parameter, making it a fast and simple algorithm to apply to new problems. TAD-PSO was tested extensively and compared to 6 widely used PSO variants on 19 benchmark problems, for 10, 30 and 100 dimensions. TAD-PSO consistently ranked first in each dimensional space, making it a competitive optimization algorithm on both unimodal and multimodal problems.}, keywords = {particle swarm optimisation, TAD-PSO algorithm, competitive optimization algorithm, information-rich exemplar, orthogonal learning, search space exploration, time adaptive dual particle swarm optimization, Animals, Convergence, Optimization, Particle swarm optimization, Sociology, Statistics, Topology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969613}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969613}}, } @INPROCEEDINGS{santos:2017:CEC, author={A. G. Santos and P. G. L. Cândido and A. F. Balardino and W. Herbawi}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Vehicle relocation problem in free floating carsharing using multiple shuttles}, year={2017}, editor = {Jose A. Lozano}, pages = {2544--2551}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={We present an Integer Linear Programming (ILP) formulation and an Evolutionary Algorithm (EA) to solve the vehicle relocation problem in free floating carsharing. In this system, users rent cars and may leave them anywhere on a designated area. After a while, a set of vehicles must be relocated to a discrete set of weighted spots, where other users may rent them again. In order to do it, some shuttles drive a set of operators to vehicles to be relocated and then collect the operators back. The objective is to maximize the weighted sum of served spots on a given time. The ILP model could solve the small instances and gave an upper (UB) and a lower bound (LB) for the others. The UB and LB values were used to evaluate the solution found by EA and showed that EA indeed found good solutions, even optimal ones.}, keywords = {evolutionary computation, integer programming, linear programming, transportation, EA, ILP, LB, UB, evolutionary algorithm, free floating carsharing, integer linear programming formulation, lower bound, multiple shuttles, upper bound, vehicle relocation problem, Automobiles, Electronic mail, Informatics, Integer linear programming}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969614}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969614}}, } @INPROCEEDINGS{sodjahin:2017:CEC, author={B. Sodjahin and V. S. Kumar and S. Lewenza and S. Reckseidler-Zenteno}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Probabilistic graphs to model Pseudomonas aeruginosa survival mechanism and infer low nutrient water response genes}, year={2017}, editor = {Jose A. Lozano}, pages = {2552--2558}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Pseudomonas aeruginosa is an organism notable for its ubiquity in the ecosystem and its antibiotics resistance. This agent presents a particular medical concern because it can live on hospital surfaces and cause various nosocomial infections. Among mechanically ventilated patients with P. aeruginosa pneumonia, ~50% succumb to their condition. Understanding how it survives is important for the design of preventive and curative measures. Furthermore, identifying the survival mechanism in the absence of nutrients is beneficial because P. aeruginosa and related organisms are capable of bioremediation. We hypothesize that P. aeruginosa is capable of long-term survival due to the presence of particular genes which encode for persistence proteins. In this paper, our primary goal is to identify genes responsible for the bacterium's survival. To achieve this, we devised a Bayesian Machine Learning based methodology to analyze the gene expression response to low nutrient water. This approach permitted to learn and construct from gene expression data, an optimal probabilistic graphical model of the survival mechanism. We then used node force techniques to infer a dozen of genes as top orchestrators of the organism's survival mechanism in low nutrient water.}, keywords = {Bayes methods, diseases, genetics, graph theory, learning (artificial intelligence), medical computing, microorganisms, optimisation, proteins, Bayesian machine learning, Pseudomonas aeruginosa survival mechanism, antibiotics resistance, bacterium survival, bioremediation, curative measures, ecosystem, gene expression, hospital surfaces, low nutrient water response genes, nosocomial infections, optimal probabilistic graphical model, organism, pneumonia, preventive measures, Bioinformatics, Genomics, Organisms, Probabilistic logic, Bayesian Networks, Machine Learning, Probabilistic networks, Pseudomonas aeruginosa}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969615}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969615}}, } @INPROCEEDINGS{banda:2017:CEC, author={J. Banda and J. Velasco and A. Berrones}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A hybrid heuristic algorithm based on Mean-Field Theory with a Simple Local Search for the Quadratic Knapsack Problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2559--2565}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In statistical physics, Mean-Field Theory is a probabilistic model which has three different approaches, one of them is Mean-Field variational that replaces a difficult distribution by an easier one. An optimization problem can be associated with a probability distribution that involves the structure of the problem, and is generally difficult to treat. This paper presents a novel method based on the Mean-Field Theory and a Local Search procedure to build good feasible solutions for the Quadratic Knapsack Problem. Basically, Mean-Field is used as a constructive heuristic that offers initial solutions and the quality of the solution is improved by a Local Search. To compare the performance of the proposed algorithm a Greedy constructive heuristic is implemented, and the same Local Search procedure was used. In order to test the efficiency of both algorithms, computational experiments were done on a set of benchmark instances in literature and another set is created. The experimental results show that Mean-Field like a constructive method provides similar solutions as Greedy in less time. And, incorporate them in a Local Search the response time for Mean-FIeld is less than Greedy but the quality of solutions is slightly smaller than Greedy.}, keywords = {greedy algorithms, heuristic programming, knapsack problems, optimisation, search problems, statistical distributions, greedy constructive heuristic, hybrid heuristic algorithm, local search procedure, mean-field theory, mean-field variational, optimization, probabilistic model, probability distribution, quadratic knapsack problem, statistical physics, Electronic mail, Heuristic algorithms, Linear programming, Mathematical model}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969616}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969616}}, } @INPROCEEDINGS{fuad:2017:CEC, author={M. M. M. Fuad and S. Besenbacher}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Modeling non-equilibrium population using variable-chromosome-length genetic algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {2566--2573}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Codon usage bias is the preferential use of synonymous codons. First models that studied this phenomenon assumed that the population is at mutation-selection-drift equilibrium, but more advanced models were proposed later to incorporate demographic changes. One of these models proposed by Zeng and Charlesworth represents the evolutionary process by a Markov model, allowing for changes in the population size. Their model is, however, too simple to reflect many realistic demographic changes. In this paper, we extend their model by allowing complex demographies with many changes in population size. Such extension requires a more powerful optimization algorithm compared with the simple one used in the model proposed by Zeng and Charlesworth. The optimization algorithm we use is a version of the genetic algorithm that we develop particularly for this purpose. We validate our method using simulated data.}, keywords = {demography, genetic algorithms, Markov model, codon usage bias, demographic change, evolutionary process, mutation-selection-drift equilibrium, nonequilibrium population modeling, optimization algorithm, variable-chromosome-length genetic algorithm, Biological cells, Genetics, Optimization, Sociology, Statistics, modeling, population genetics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969617}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969617}}, } @INPROCEEDINGS{tan:2017:CEC, author={Boxiong Tan and Hui Ma and Yi Mei}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A NSGA-II-based approach for service resource allocation in Cloud}, year={2017}, editor = {Jose A. Lozano}, pages = {2574--2581}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Web service and Cloud computing have significantly reformed the software industry. The need for web service allocation in the cloud environment is increasing dramatically. In order to reduce the cost for service providers as well as improve the utilization of cloud resource for cloud providers, this paper formulates the web service resource allocation in cloud environment problem as a two-level multi-objective bin packing problem. It proposes a NSGA-II-based algorithm with specifically designed genetic operators. We are compared with two varieties of the algorithm. The results show that the proposed algorithm can provide reasonably good results with low violation rate.}, keywords = {Web services, bin packing, cloud computing, genetic algorithms, resource allocation, NSGA-II-based approach, Web service resource allocation, genetic operators, multiobjective bin packing problem, software industry, Algorithm design and analysis, Optimization, Resource management, Software as a service, Virtual machining}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969618}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969618}}, } @INPROCEEDINGS{gonzález:2017:CECa, author={O. M. González and C. Segura and S. I. V. Peña and C. León}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A memetic algorithm for the Capacitated Vehicle Routing Problem with Time Windows}, year={2017}, editor = {Jose A. Lozano}, pages = {2582--2589}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Vehicle Routing Problem (VRP) is a widely known NP-Hard combinatorial optimization problem. This paper presents a proposal of a memetic algorithm (MA) with simulated annealing (SA) as trajectory-based method for solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A novel crossover operator, the Single Breaking-point Sequence Based Crossover (SBSBX), is introduced and compared with a widely used operator, the Sequence-based Crossover (SBX). One of the principles behind the design of SBSBX is to reduce the disruptive behavior of SBX, with the aim of providing additional intensification. Initial studies show that the different crossover operators heavily impact the preservation of diversity in the population. Thus, two different parent-selection operators that induce different selection pressure are applied: random selection and binary tournament. The proposal is validated using the well-known Solomon's benchmark. The experimental validation shows that in some of the tested methods premature convergence is an important issue, whereas in other cases convergence is not attained. Overall, the combination of SBSBX and random selection attains the most promising results. In fact, a new best-known solution could be generated for one commonly used instance.}, keywords = {combinatorial mathematics, computational complexity, simulated annealing, vehicle routing, CVRPTW, MA, NP-Hard combinatorial optimization problem, SA, SBSBX, binary tournament, capacitated vehicle routing problem-with-time windows, crossover operator, disruptive behavior reduction, diversity preservation, memetic algorithm, parent-selection operators, random selection, selection pressure, single breaking-point sequence based crossover, trajectory-based method, Benchmark testing, Convergence, Measurement, Memetics, Proposals}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969619}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969619}}, } @INPROCEEDINGS{ghosh:2017:CEC, author={A. Ghosh and S. Das and B. K. Panigrahi and A. K. Das}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A noise resilient Differential Evolution with improved parameter and strategy control}, year={2017}, editor = {Jose A. Lozano}, pages = {2590--2597}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={A switched-parameter Differential Evolution (DE) enforced with equiprobable switching between two alternative mutation strategies, an optional blending crossover, and a threshold-based selection mechanism is proposed for optimization of complex functions corrupted with additive noise. In order to handle the noisy optimization problems, the DE framework is coupled with three new algorithmic components. Each individual is subjected to one of the two well known mutation strategies namely DE/best/1 and DE/rand/1 with equal chances. In the recombination stage, binomial and blending crossovers are opted in the same switchable strategy as done for mutation. A novel threshold-based selection mechanism is used to allow less fit offspring to survive occasionally, thus countering the noisy function behavior. Additive Gaussian noise is used to simulate the noisy behavior of functions defined over continuous search spaces. A benchmark suite comprising of 21 well-known numerical functions is considered to compare and contrast the proposed method with other state-of-the-art evolutionary algorithms specifically tailored for noisy optimization scenario. The proposed method shows very competitive performance indicating highly robust behavior against the noisy functional landscapes.}, keywords = {Gaussian noise, evolutionary computation, search problems, DE, additive Gaussian noise, algorithmic components, alternative mutation strategies, binomial crossovers, blending crossovers, complex functions, continuous search spaces, equiprobable switching, evolutionary algorithms, improved parameter, noise resilient differential evolution, noisy function behavior, noisy functional landscapes, noisy optimization problems, noisy optimization scenario, recombination stage, strategy control, switched-parameter differential evolution, threshold-based selection mechanism, Linear programming, Noise measurement, Optimization, Sociology, Standards, Statistics, Switches, Differential Evolution, Evolutionary Optimization, Noisy Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969620}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969620}}, } @INPROCEEDINGS{ashraf:2017:CEC, author={Z. Ashraf and D. Malhotra and P. K. Muhuri and Q. M. D. Lohani}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Hybrid biogeography-based optimization for solving vendor managed inventory system}, year={2017}, editor = {Jose A. Lozano}, pages = {2598--2605}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In the modern era of industrialization and globalization, distribution and control of goods are essential aspects for multinational corporations and strategic partners. Vendor managed inventory (VMI) is one of the well-known strategies of merchandizing between supplier and retailer. In this paper, we consider different number of suppliers and retailers to perform business under VMI system and formulate three: single-supplier and single-retailer, single-supplier and multi-retailer, and multi-supplier and multi-retailer VMI systems. The objective is to minimize the total cost of VMI system. Since it is a non-linear integer programming problem, this paper proposes a novel hybrid biogeography-based optimization algorithm to solve it. We enhance the proposed algorithm by embedding stochastic fractal search (SFS) in biogeography-based optimization (BBO). SFS algorithm is a newly developed powerful evolutionary algorithm to find global optimum much faster and efficiently. The diffusion process of SFS improved the exploitation ability of search in BBO. Our proposed algorithm is applied on all three versions of VMI systems under different constraints. We have considered suitable input data for all the different problems and obtained the results. By comparison, we show that the results outperformed for all VMI systems.}, keywords = {evolutionary computation, globalisation, goods distribution, inventory management, search problems, supply chains, SFS algorithm, evolutionary algorithm, globalization, hybrid biogeography, industrialization, merchandizing strategies, multi-supplier and multi-retailer VMI systems, multinational corporations, nonlinear integer programming problem, single-supplier and multi-retailer VMI systems, single-supplier and single-retailer VMI systems, stochastic fractal search, strategic partners, vendor managed inventory system optimization, Algorithm design and analysis, Biological system modeling, Diffusion processes, Fractals, Genetic algorithms, Mathematical model, Optimization, Biogeography-Based Optimization (BBO), Multi-supplier multi-retailer, Stochastic fractal search (SFS), Vendor Managed Inventory (VMI)}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969621}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969621}}, } @INPROCEEDINGS{santana:2017:CEC, author={R. Santana and G. Sirbiladze and B. Ghvaberidze and B. Matsaberidze}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A comparison of probabilistic-based optimization approaches for vehicle routing problems}, year={2017}, editor = {Jose A. Lozano}, pages = {2606--2613}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Estimation of distribution algorithms (EDAs) are evolutionary algorithms that use probabilistic modeling to lead a more efficient search for optimal solutions. While EDAs have been applied to several types of optimization problems, they exhibit some limitations to deal with constrained optimization problems. More study and understanding of how can EDAs deal with these problems is required. In this paper we investigate the application of EDAs to a version of the vehicle routing problem in which solutions should satisfy a number of constraints involving the customers, the fleet vehicle, and the items to be delivered. For this problem, we compare two different representations of the solutions, and apply EDAs that use three probabilistic models with different characteristics. Our results show that the combination of an integer representation with tree-based probabilistic model produces the best results and is able to solve vehicle routing problems that contain over thousands of promising paths.}, keywords = {evolutionary computation, optimisation, search problems, statistical distributions, trees (mathematics), vehicle routing, EDAs, constrained optimization problems, estimation of distribution algorithms, evolutionary algorithms, fleet vehicle, optimal solution search, probabilistic modeling, probabilistic-based optimization approaches, tree-based probabilistic, vehicle routing problems, Indexes, Maintenance engineering, Optimization, Probabilistic logic}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969622}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969622}}, } @INPROCEEDINGS{amaya:2017:CEC, author={I. Amaya and J. C. Ortiz-Bayliss and A. E. Gutiérrez-Rodríguez and H. Terashima-Marín and C. A. C. Coello}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving hyper-heuristic performance through feature transformation}, year={2017}, editor = {Jose A. Lozano}, pages = {2614--2621}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Hyper-heuristics are powerful search methodologies that can adapt to different kinds of problems. One element of paramount importance, however, is the selection module that they incorporate. Traditional approaches define a set of features for characterizing a problem and, thus, define how to best solve it. However, some features may vary nonlinearly as the solver progresses, requiring higher resolution in specific areas of the feature domain. This work focuses on assessing the advantage of using feature transformations to improve the given resolution and, as a consequence, to improve the overall performance of a hyper-heuristic. We provide evidence that using feature transformations may result in a better discrimination of the problem instance and, as consequence, a better performance of the hyper-heuristics. The feature transformation strategy was applied to an evolutionary-based hyper-heuristic model taken from the literature and tested on constraint satisfaction problems The proposed strategy increased the median success rate of hyper-heuristics by more than 13% and reduced its standard deviation in about 7%, while reducing the median number of adjusted consistency checks by almost 30%.}, keywords = {constraint satisfaction problems, optimisation, search problems, evolutionary-based hyper-heuristic model, feature transformation, hyper-heuristic performance improvement, median success rate, resolution improvement, search methodologies, standard deviation reduction, Electronic mail, Evolutionary computation, Optimization, Resource management, Testing, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969623}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969623}}, } @INPROCEEDINGS{rosales-pérez:2017:CEC, author={A. Rosales-Pérez and A. E. Gutiérrez-Rodríguez and J. C. Ortiz-Bayliss and H. Terashima-Marín and C. A. C. Coello}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary multilabel hyper-heuristic design}, year={2017}, editor = {Jose A. Lozano}, pages = {2622--2629}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Nowadays, heuristics represent a commonly used alternative to solve complex optimization problems. This, however, has given rise to the problem of choosing the most effective heuristic for a given problem. In recent years, one of the most used strategies for this task has been the hyper-heuristics, which aim at selecting/generating heuristics to solve a wide range of optimization problems. Most of the existing selection hyper-heuristics attempt to recommend only one heuristic for a given instance. However, for some classes of problems, more than one heuristic can be suitable. With this premise, in this paper, we address this issue through an evolutionary multilabel learning approach for building hyper-heuristics. Unlike traditional approaches, in the multilabel formulation, the result could not be a single recommendation, but a set of potential heuristics. Due to the fact that cooperative coevolutionary algorithms allow us to divide the problem into several subproblems, it results in a natural approach for dealing with multilabel classification. The proposed cooperative coevolutionarymultilabel approach aims at choosing the most relevant patterns for each heuristic. For the experimental study included in this paper, we have used a set of constraint satisfaction problems as our study case. Our experimental results suggest that the proposed method is able to generate accurate hyper-heuristics that outperform reference methods.}, keywords = {constraint satisfaction problems, evolutionary computation, learning (artificial intelligence), pattern classification, complex optimization problems, cooperative coevolutionary algorithms, cooperative coevolutionary multilabel approach, evolutionary multilabel hyperheuristic design, evolutionary multilabel learning approach, multilabel classification, multilabel formulation, Algorithm design and analysis, Electronic mail, Optimization, Sociology, Statistics, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969624}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969624}}, } @INPROCEEDINGS{tatsumi:2017:CEC, author={T. Tatsumi and H. Sato and T. Kovacs and K. Takadama}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution}, year={2017}, editor = {Jose A. Lozano}, pages = {2630--2637}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper focuses on a generalization of classifiers in noisy problems and aims at exploring learning classifier systems (LCSs) that can evolve accurately generalized classifiers as an optimal solution in several environments which include different type of noise. For this purpose, this paper employs XCS-CRE (XCS without Convergence of Reward Estimation) which can correctly identify classifiers as either accurate or inaccurate ones even in a noisy problem, and investigates its effectiveness in several noisy problems. Through intensive experiments of three LCSs (i.e., XCS as the conventional LCS, XCS-SAC (XCS with Self-adaptive Accuracy Criterion) as our previous LCS, and XCS-CRE) on the noisy 11-multiplexer problem where reward value changes according to (a) Gaussian distribution, (b) Cauchy distribution, or (c) Lognormal distribution, the following implications have been revealed: (1) the correct rate of the classifier of XCS-CRE and XCS-SAC converge to 100% in all three types of the reward distribution while that of XCS cannot reach 100%; (2) the population size of XCS-CRE is smallest followed by that of XCS-SAC and XCS; and (3) the percentage of the acquired optimal classifiers of XCS-CRE is highest followed by that of XCS-SAC and XCS.}, keywords = {Gaussian distribution, learning (artificial intelligence), pattern classification, 11-multiplexer problem, Cauchy distribution, LCS, XCS with self-adaptive accuracy criterion, XCS without convergence of reward estimation, XCS-CRE, XCS-SAC, generalized classifiers, lognormal distribution, reward distribution, variance-based learning classifier system, Convergence, Electronic mail, Learning systems, Noise measurement, Sociology, Standards, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969625}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969625}}, } @INPROCEEDINGS{gutierrez-rodríguez:2017:CEC, author={A. E. Gutierrez-Rodríguez and J. C. Ortiz-Bayliss and A. Rosales-Pérez and I. M. Amaya-Contreras and S. E. Conant-Pablos and H. Terashima-Marín and C. A. C. Coello}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Applying automatic heuristic-filtering to improve hyper-heuristic performance}, year={2017}, editor = {Jose A. Lozano}, pages = {2638--2644}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Hyper-heuristics have emerged as an important strategy for combining the strengths of different heuristics into a single method. Although hyper-heuristics have been found to be successful in many scenarios, little attention has been paid to the subsets of heuristics that these methods manage and apply. In several cases, heuristics can interfere with each other and can be harmful for the search. Thus, obtaining information about the differences among heuristics, and how they contribute to the search process is very important. The main contribution of this paper is an automatic heuristic-filtering process that allows hyper-heuristics to exclude heuristics that do not contribute to improving the solution. Based on some previous works in feature selection, two methods are proposed that rank heuristics and sequentially select only suitable heuristics in a hyper-heuristic framework. Our experiments over a set of Constraint Satisfaction Problem instances show that a hyper-heuristic with only selected heuristics obtains significantly better results than a hyper-heuristic containing all heuristics, in terms of running times. In addition, the success rate of solving such instances is better for the hyper-heuristic with the suitable heuristics than for the hyper-heuristic without our proposed filtering process.}, keywords = {constraint satisfaction problems, feature selection, search problems, automatic heuristic-filtering process, combinatorial optimization problems, constraint satisfaction problem, heuristic search, hyper-heuristic performance, rank heuristics, search process, Correlation, Electronic mail, Genetic algorithms, Measurement, Optimization, Proposals, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969626}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969626}}, } @INPROCEEDINGS{liu:2017:CECf, author={C. H. Liu and C. K. Ting}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Fusing Flamenco and Argentine Tango by evolutionary composition}, year={2017}, editor = {Jose A. Lozano}, pages = {2645--2652}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Evolutionary composition has received considerable attention in these years. An automatic composition system based on evolutionary computation is attractive but very challenging because the evaluation function is difficult to design: First, human's evaluation on a composition is subject to personal feeling and preference. Second, different regions form their unique music genres from local customs, lifestyle, or ethnic history. These genres enrich music diversity and human sensation. This study presents an evolutionary composition system to address the above two issues. More specifically, the proposed system adopts general music theory rules as the fundamentals of fitness evaluation, thereby ensuring the basic harmony and consonance. In addition, we explore the specific musical expressions of Flamenco and Argentine Tango, and include their features in the evolutionary composition system to create a fusion. Experimental results show that the generated music is satisfactory and can express the emphases of both Flamenco and Argentine Tango.}, keywords = {evolutionary computation, music, Argentine Tango, Flamenco Tango, automatic composition system, consonance, ethnic history, evolutionary composition system, fitness evaluation, general music theory rules, harmony, human sensation, lifestyle, local customs, music diversity, music genre, musical expression, personal feeling, personal preference, Biological cells, Genetic algorithms, Rhythm, Sociology, Statistics, Flamenco, genetic algorithm, music composition, music theory}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969627}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969627}}, } @INPROCEEDINGS{grebennikov:2017:CEC, author={D. Grebennikov and G. Bocharov}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Modelling the structural organization of lymph nodes}, year={2017}, editor = {Jose A. Lozano}, pages = {2653--2655}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The lymph node structural organization is essential for providing effective immune responses. We present an algorithm to generate 3D voxel-based approximation of the reticular network - the scaffold of T cell zone, consisting of fibroblastic reticular cells enwrapping the conduit system, - from the given conduit system topology graph. The algorithm is based on extended formulation of Cellular Potts Model, in which the intrinsic motility of cells is specified for each voxel according to distributions localized around graph nodes. This approach allows one to maintain the connectivity of network by freezing the cells junctions, to control the target volume fraction of network and the typical sizes of FRC bodies.}, keywords = {biology computing, cellular biophysics, 3D voxel-based approximation, Cellular Potts Model, T cell zone, cells motility, conduit system topology graph, fibroblastic reticular cells, immune responses, localized around graph nodes, lymph node structural organization, modelling, reticular network, volume fraction, Computational modeling, Human immunodeficiency virus, Immune system, Lymph nodes, Numerical models, Solid modeling, Three-dimensional displays}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969628}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969628}}, } @INPROCEEDINGS{ibarguren:2017:CEC, author={I. Ibarguren and J. M. Pérez and J. Mugerza and D. Rodriguez and R. Harrison}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={The Consolidated Tree Construction algorithm in imbalanced defect prediction datasets}, year={2017}, editor = {Jose A. Lozano}, pages = {2656--2660}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this short paper, we compare well-known rule/tree classifiers in software defect prediction with the CTC decision tree classifier designed to deal with class imbalanced. It is well-known that most software defect prediction datasets are highly imbalance (non-defective instances outnumber defective ones). In this work, we focused only on tree/rule classifiers as these are capable of explaining the decision, i.e., describing the metrics and thresholds that make a module error prone. Furthermore, rules/decision trees provide the advantage that they are easily understood and applied by project managers and quality assurance personnel. The CTC algorithm was designed to cope with class imbalance and noisy datasets instead of using preprocessing techniques (oversampling or undersampling), ensembles or cost weights of misclassification. The experimental work was carried out using the NASA datasets and results showed that induced CTC decision trees performed better or similar to the rest of the rule/tree classifiers.}, keywords = {aerospace computing, decision trees, pattern classification, software reliability, CTC decision tree classifier, NASA datasets, class imbalance problem, consolidated tree construction algorithm, imbalanced defect prediction datasets, noisy datasets, rule classifiers, software defect prediction, tree classifiers, Algorithm design and analysis, Measurement, NASA, Prediction algorithms, Software, Software algorithms}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969629}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969629}}, } @INPROCEEDINGS{nath:2017:CEC, author={R. Nath and Z. Ashraf and P. K. Muhuri and Q. M. D. Lohani}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={BLEAQ based solution for bilevel reliability-allocation problem}, year={2017}, editor = {Jose A. Lozano}, pages = {2661--2668}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Reliability redundancy allocation problem (RRAP) is an optimization problem with objective to maximize the system reliability considering component reliability and redundancies as decision variables. RRAP was mostly solved as a single level optimization problem. However, the nature of the problem fits quite well in the framework of bilevel optimization. In this paper, we have proposed two novel bilevel formulations for the RRAP and solve them using a latest bilevel optimization algorithm called BLEAQ (bilevel evolutionary algorithm based on quadratic approximations). So far we knew no other research has been reported till date, where RRAP was addressed with bilevel optimization algorithm. Here, optimization is needed at two separate levels, where one problem is encircled within another problem. The inner problem is known as lower-level problem and the external problem is called upper-level problem. Here, we have presented two mixed-integer non-linear bilevel formulations for the RRAP of series-parallel system in a competitive environment. The purpose of the upper-level problem is to determine the component reliability that maximizes the total system reliability; whereas, lower-level problem minimizes the total cost (or weight) needed. We demonstrate the applicability of our approach with a suitable numerical example and show that our proposed approach works quite well than existing single level optimization tools.}, keywords = {approximation theory, evolutionary computation, integer programming, nonlinear programming, redundancy, reliability theory, BLEAQ based, RRAP, bilevel evolutionary algorithm-based-on-quadratic approximations, bilevel optimization algorithm, bilevel reliability-allocation problem, component reliability, decision variables, lower-level problem, mixed-integer nonlinear bilevel formulations, reliability redundancy allocation problem, series-parallel system, single level optimization problem, total cost minimization, total system reliability maximization, upper-level problem, Linear programming, Mathematical model, Optimization, Power system reliability, Resource management, BLEAQ, Bilevel optimization, Reliability, Reliability-Redundancy Allocation Problem}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969630}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969630}}, } @INPROCEEDINGS{santis:2017:CEC, author={E. De Santis and A. Rizzi and A. Sadeghian}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A learning intelligent System for classification and characterization of localized faults in Smart Grids}, year={2017}, editor = {Jose A. Lozano}, pages = {2669--2676}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The worldwide power grid can be thought as a System of Systems deeply embedded in a time-varying, non-deterministic and stochastic environment. The availability of ubiquitous and pervasive technology about heterogeneous data gathering and information processing in the Smart Grids allows new methodologies to face the challenging task of fault detection and modeling. In this study, a fault recognition system for Medium Voltage feeders operational in the power grid in Rome, Italy, is presented. The recognition task is performed synthesizing a data-driven model of fault phenomenons based on a hybridization of Evolutionary learning and Clustering techniques. The model is synthesized starting from a set of clusters obtained by partitioning the fault patterns, tuning at the same time the core dissimilarity measure. In this paper we show as clusters can be successively analyzed for mining useful information about the fault phenomenon and to build up an ad-hoc decision system to support business strategies such as Condition Based Maintenance tasks.}, keywords = {data mining, decision support systems, fault location, learning (artificial intelligence), pattern classification, pattern clustering, power engineering computing, power system faults, smart power grids, ad-hoc decision system support, business strategies, clustering techniques, core dissimilarity measure tuning, evolutionary learning, fault patterns partitioning, fault recognition system, information mining, learning intelligent system, localized faults characterization, localized faults classification, medium voltage feeders, power grid, smart grids, system of systems, Data visualization, Feature extraction, Transmission line measurements, Voltage measurement}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969631}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969631}}, } @INPROCEEDINGS{abdelhafez:2017:CEC, author={A. Abdelhafez and E. Alba}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Speed-up of synchronous and asynchronous distributed Genetic Algorithms: A first common approach on multiprocessors}, year={2017}, editor = {Jose A. Lozano}, pages = {2677--2682}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Genetic Algorithms (GAs) are being used to solve a wide range of problems in real world problems, and it is important to study their implementations to improve the solution quality and reduce the execution time. Designing parallel (e.g., distributed) GAs is one research line to do so. In distributed GAs, every individual represents a tentative solution. Individuals are split (and sparsely communicated) over many islands, with genetic operators being applied locally in each island. In addition, in order to maintain diversity and reduce the number of the evaluations, a migration operator is used to enhance their behavior. This article presents a basic study on the speed-up of parallel GAs where a common approach is followed to better understand synchronous and asynchronous versions together. We analyze the behavior of GAs over a homogeneous multiprocessor system. We will report results showing linear and even superlinear speed-up in both cases of study. The parallel performance of the synchronous and asynchronous versions is very good in a multiprocessor computer, both in terms of time and solution quality. Besides, a statistical analysis of the algorithms clearly proves that both cases have a similar numerical behavior over a homogeneous parallel system.}, keywords = {genetic algorithms, multiprocessing systems, parallel processing, asynchronous distributed genetic algorithms, execution time, genetic operators, homogeneous multiprocessor system, homogeneous parallel system, multiprocessor computer, multiprocessors, parallel GA, solution quality, superlinear speed-up, synchronous distributed genetic algorithms, Program processors, Search problems, Sociology, Statistics, Topology, Asynchronous and synchronous parallel GAs, High Dimensions, MPI, Speed-up}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969632}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969632}}, } @INPROCEEDINGS{lopez:2017:CEC, author={E. M. Lopez and C. A. C. Coello}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving the integration of the IGD^+ indicator into the selection mechanism of a Multi-objective Evolutionary Algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {2683--2690}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In recent years, the design of new selection mechanisms based on quality indicators has become a popular trend in the development of Multi-Objective Evolutionary Algorithms (MOEAs). This trend has been motivated by the well-known limitations of Pareto-based MOEAs when dealing with many-objective optimization problems (i.e., problems having more than 3 objectives). In this paper, we propose a selection mechanism (called IGD^+ -H) which is based on the combination of the Inverted Generational Distance^+ (IGD^+ ) indicator and Kuhn-Munkres' (Hungarian) algorithm to solve Linear Assignment Problems (LAPs). The proposed selection scheme is compared with respect to other selection mechanisms based on the IGD indicator and with respect to the use of the Δ_p indicator. Our proposed technique is incorporated into a MOEA and is validated using standard test functions. Our comparative study indicates that both Δ_p and IGD present some limitations when selecting solutions in degenerate multi-objective problems. Our results show that the transformation of the selection mechanism into a linear assignment problem speeds up the convergence of the MOEA and it is able to solve many-objective problems in an effective and efficient manner. We show that our proposed IGD^+ -H-based selection mechanism is able to achieve a significant speed up (of up to 200×) with respect to the exclusive use of any of the indicators adopted in our study.}, keywords = {Pareto optimisation, convergence, evolutionary computation, IGD+ indicator, Kuhn-Munkres algorithm, LAPs, MOEA convergence, Pareto-based MOEAs, inverted generational distance+ indicator, linear assignment problems, many-objective optimization problems, multi-objective evolutionary algorithm, quality indicators, selection mechanism, Cost function, Euclidean distance, Pareto optimization, Sociology}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969633}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969633}}, } @INPROCEEDINGS{kukkonen:2017:CEC, author={S. Kukkonen and E. Mezura-Montes}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={An experimental comparison of two constraint handling approaches used with differential evolution}, year={2017}, editor = {Jose A. Lozano}, pages = {2691--2697}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, two existing constraint handling approaches are compared. The constraint handling approaches are based on the same principle of preferring feasible solution candidates over infeasible but they differ in the case of two infeasible solution candidates. One approach calculates the sum of constraint violations, whereas the other approach uses Pareto-dominance of constraint violations. Comparison of the constraint handling approaches is done experimentally using Differential Evolution (DE) algorithm. DE, as many other evolutionary algorithms, contains control parameters to be set by the user. Besides using fixed control parameter values, also an Exponential Weighting Moving Average (EWMA) control parameter adaptation technique is used. Experimental results reveal that neither of the constraint handling approaches can be judged to be better than the other. What is surprising, also EWMA cannot be judged to improve performance when applied to constraints. It is rather causing more uncertainty according to the results.}, keywords = {constraint handling, evolutionary computation, moving average processes, DE algorithm, EWMA control parameter adaptation technique, Pareto-dominance, constraint violation, differential evolution, exponential weighting moving average control parameter, feasible solution candidates, fixed control parameter values, Electronic mail, Linear programming, Optimization, Sociology, Statistics, Upper bound}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969634}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969634}}, } @INPROCEEDINGS{jin:2017:CEC, author={Chen Jin and A. K. Qin}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A GPU-based implementation of brain storm optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2698--2705}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Brain storm optimization (BSO) is a newly emerging family of swarm intelligence techniques inspired by the human's creative problem-solving process, which has achieved successes in many applications. BSO is characterized by its unique process of grouping a population of ideas and carrying out brainstorming based on the grouped ideas to search for optima generation by generation. Although the original BSO is a sequential algorithm based on the central processing unit (CPU), its major algorithmic modules are highly suitable for parallelization. Nowadays, modern graphic processing units (GPUs) have become widely affordable, which empower personal computers to undertake massively parallel computing tasks. Therefore, this work investigates a GPU-based implementation of BSO using NVIDIA's CUDA technology, aiming to accelerate BSO's computation speed while maintaining its optimization accuracy. Experimental results on 30 CEC2014 single-objective real-parameter optimization benchmark problems demonstrate the remarkable speedups of the proposed GPU-based parallel BSO compared to the original CPU-based sequential BSO across varying problems and population sizes.}, keywords = {graphics processing units, mathematics computing, optimisation, parallel architectures, search problems, swarm intelligence, CEC2014 single-objective real-parameter optimization benchmark problem, GPU-based parallel BSO, NVIDIA CUDA technology, brain storm optimization, graphic processing units, human creative problem-solving process, parallel computing tasks, sequential algorithm, swarm intelligence techniques, Clustering algorithms, Instruction sets, Kernel, Optimization, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969635}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969635}}, } @INPROCEEDINGS{pillay:2017:CEC, author={N. Pillay and D. Beckedahl}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={EvoHyp - a Java toolkit for evolutionary algorithm hyper-heuristics}, year={2017}, editor = {Jose A. Lozano}, pages = {2706--2713}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Hyper-heuristics is an emergent technology that has proven to be effective at solving real-world problems. The two main categories of hyper-heuristics are selection and generation. Selection hyper-heuristics select existing low-level heuristics while generation hyper-heuristics create new heuristics. At the inception of the field single point searches were essentially employed by selection hyper-heuristics, however as the field progressed evolutionary algorithms are becoming more prominent. Evolutionary algorithms, namely, genetic programming, have chiefly been used for generation hyper-heuristics. Implementing evolutionary algorithm hyper-heuristics can be quite a time-consuming task which is daunting for first time researchers and practitioners who want to rather focus on the application domain the hyper-heuristic will be applied to which can be quite complex. This paper presents a Java toolkit for the implementation of evolutionary algorithm hyper-heuristics, namely, EvoHyp. EvoHyp includes libraries for a genetic algorithm selection hyper-heuristic (GenAlg), a genetic programming generation hyper-heuristic (GenProg), a distributed version of GenAlg (DistrGenAlg) and a distributed version of GenProg (DistrGenProg). The paper describes the libraries and illustrates how they can be used. The ultimate aim is to provide a toolkit which a non-expert in evolutionary algorithm hyper-heuristics can use. The paper concludes with an overview of future extensions of the toolkit.}, keywords = {genetic algorithms, genetic programming, Java, evolutionary computation, DistrGenAlg, DistrGenProg, EvoHyp, Java toolkit, distributed GenAlg, distributed GenProg, evolutionary algorithm hyper-heuristics, generation hyper-heuristics, genetic algorithm selection hyper-heuristic, genetic programming generation hyper-heuristic, low-level heuristics, Biological cells, Libraries, Sociology, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969636}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969636}}, } @INPROCEEDINGS{reynolds:2017:CEC, author={R. G. Reynolds and L. Kinnaird-Heether}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Problem solving using social networks in Cultural Algorithms with auctions}, year={2017}, editor = {Jose A. Lozano}, pages = {2714--2721}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Cultural Algorithms provide a meta-heuristic framework for the application of various solution mechanism represented by Knowledge Sources in the Belief Space to the evolution of complex social networks in the population space. One of the key components in the configuration of a Cultural Algorithm is the knowledge distribution mechanism, how the influence of a solution approach is spread out among individuals in individual subnetworks. In this paper a specific set of Knowledge distribution mechanisms based upon various Auction models are introduced and their performance compared in complex real-valued functional landscapes. While capable of generating solutions to the entire range of problem, from fixed to chaotic, they are best utilized for fixed problems containing of low to high variability but begin to lose their edge with increasing periodicity. It is proposed that auction mechanisms will be most likely found in subcultures where problems associated with that culture have reasonably strong signals that can afford the opportunity to make quick and precise decisions.}, keywords = {evolutionary computation, learning (artificial intelligence), problem solving, social networking (online), auction mechanisms, belief space, complex real-valued functional landscapes, complex social network evolution, cultural algorithms, fixed problems, knowledge distribution mechanism, knowledge sources, metaheuristic framework, population space, solution mechanism application, Cultural differences, Network topology, Problem-solving, Sociology, Statistics, Topology, Wheels, Metaheuristics, optimization problem solving, problem complexity, social networks}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969637}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969637}}, } @INPROCEEDINGS{sano:2017:CEC, author={R. Sano and H. Aguirre and K. Tanaka}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A closer look to elitism in #x03B5;-dominance many-objective optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2722--2729}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Elitism is a common feature of many-objective optimizers and has a strong impact on the performance of the algorithms. The way elitism is implemented vary among the various approaches to many-objective optimization and there are no detailed studies about their effects. In this work we focus on a multi- and many-objective optimization approach based on ε-dominance. We track the number of generations a solution remains in the population to bias survival selection or the creation of neighborhoods for parent selection. We investigate how elitist strategies affect performance of the algorithm and show that convergence and diversity can be enhanced by using different strategies for elitism on many-objective uni-modal and multi-modal problems with 4, 5, and 6 objectives.}, keywords = {optimisation, ε-dominance many-objective optimization, bias survival selection, elitism, many-objective multimodal problem, many-objective unimodal problem, multiobjective optimization approach, neighborhood creation, parent selection, Convergence, Next generation networking, Optimization, Schedules, Sociology, Sorting, Statistics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969638}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969638}}, } @INPROCEEDINGS{ahrari:2017:CEC, author={A. Ahrari and K. Deb and S. Mohanty and J. H. Hattel}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective optimization of cellular scanning strategy in selective laser melting}, year={2017}, editor = {Jose A. Lozano}, pages = {2730--2737}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to develop a multi-objective approach to optimize the cellular scanning strategy such that the two aforementioned defects are minimized. The decision variable in the chosen problem is a combination of the sequence in which cells are processed and one of six scanning strategies applied to each cell. Thus, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot be obtained by performing merely a local search. Possible similarities in Pareto-optimal solutions are explored.}, keywords = {Pareto optimisation, combinatorial mathematics, evolutionary computation, laser materials processing, melting, rapid prototyping (industrial), Pareto-optimal solutions, additive manufacturing process, cellular scanning strategy multiobjective optimization, choice optimization, combinatorial optimization, decision variable, multiobjective evolutionary method, process simulation domain, process-induced defect, residual stress, selective laser melting, temperature fields, Distortion, Heating systems, Laser beams, Laser noise, Optimization, Powders, Residual stresses, additive manufacturing, distortions, scan strategies}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969639}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969639}}, } @INPROCEEDINGS{sabar:2017:CEC, author={N. R. Sabar and A. Turky and A. Song and A. Sattar}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimising Deep Belief Networks by hyper-heuristic approach}, year={2017}, editor = {Jose A. Lozano}, pages = {2738--2745}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Deep Belief Networks (DBN) have been successful in classification especially image recognition tasks. However, the performance of a DBN is often highly dependent on settings in particular the combination of runtime parameter values. In this work, we propose a hyper-heuristic based framework which can optimise DBNs independent from the problem domain. It is the first time hyper-heuristic entering this domain. The framework iteratively selects suitable heuristics based on a heuristic set, apply the heuristic to tune the DBN to better fit with the current search space. Under this framework the setting of DBN learning is adaptive. Three well-known image reconstruction benchmark sets were used for evaluating the performance of this new approach. Our experimental results show this hyper-heuristic approach can achieve high accuracy under different scenarios on diverse image sets. In addition state-of-the-art meta-heuristic methods for tuning DBN were introduced for comparison. The results illustrate that our hyper-heuristic approach can obtain better performance on almost all test cases.}, keywords = {belief networks, image classification, image reconstruction, learning (artificial intelligence), DBN learning, deep belief networks, heuristic set, hyper-heuristic approach, image recognition, meta-heuristic methods, Australia, Machine learning, Mathematical model, Optimization, Stacking, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969640}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969640}}, } @INPROCEEDINGS{fang:2017:CEC, author={W. Fang and L. Zhang and J. Zhou and X. Wu and J. Sun}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={A novel quantum-behaved particle swarm optimization with random selection for large scale optimization}, year={2017}, editor = {Jose A. Lozano}, pages = {2746--2751}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={Large scale optimization has become a well-recognised field in many science and engineering applications and a variety of metaheuristic algorithms adopting cooperative coevolution (CC) framework with problem decomposition have been applied to solve them. In this paper, a novel decomposition strategy termed as random selection is proposed. In random selection strategy, only a small part of decision variables are randomly selected to form a group for evolving at every iteration and the maximum number of randomly selected decision variables are limited by the parameter RSSCALE. By random selection, the randomly selected searching subspace is explored sufficiently in each iteration and the whole search space can be fully covered after several iterations. We evaluate the random selection strategy by combining quantum-behaved particle swarm optimization (RSQPSO) and a comparative study is carried out on a set of benchmark functions between RSQPSO and four state-of-the-art algorithms, which were specially designed for large scale optimization. The comparative results show that the proposed approach performs well for solving large scale optimization problems.}, keywords = {particle swarm optimisation, CC framework, RSQPSO, RSSCALE, benchmark functions, cooperative coevolution framework, decision variables, large scale optimization, metaheuristic algorithms, problem decomposition, quantum-behaved particle swarm optimization, random selection, searching subspace, Algorithm design and analysis, Benchmark testing, Geometry, Hypercubes, Optimization, Particle swarm optimization, Quantum mechanics}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969641}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969641}}, } @INPROCEEDINGS{tanweer:2017:CEC, author={M. R. Tanweer and S. Suresh and N. Sundararajan}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Development of a Higher Order Cognitive Optimization algorithm}, year={2017}, editor = {Jose A. Lozano}, pages = {2752--2758}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={In this paper, we develop a human social intelligence inspired population-based optimization algorithm called Higher Order Cognitive Optimization (HOCO) algorithm. Each of the individuals in this HOCO possess human-like characteristics such as decision making ability, self/social-awareness, self/social belief, shared information processing, and self-regulation. These characteristics are modeled as a hierarchical inter-related structure with each layer realizing different levels of granularity. In this paper, HOCO is implemented as a three layered inter-related architecture for single-objective optimization. The main aspects of the proposed optimization technique are: (1) development of a socially intelligent optimization algorithm; (2) each individual employs their meta-cognitive as well as social meta-cognitive abilities, in addition to the cognitive abilities to attain the global optimal solution; and (3) the meta-cognitive and social meta-cognitive components self-regulate the cognitive component by adapting its strategies, such that a globally optimal solution formulation is achieved. Performance has been analyzed on six standard benchmark problems and compared with other meta-heuristic algorithms. Further, the performance on computationally expensive CEC2015 benchmark problems has also been studied. The comparison with other population based meta-heuristic approaches indicates the significance of the HOCO algorithm.}, keywords = {cognitive systems, decision making, granular computing, optimisation, CEC2015 benchmark problems, HOCO algorithm, decision making ability, global optimal solution, granularity levels, hierarchical inter-related structure, higher order cognitive optimization algorithm, human social intelligence inspired population-based optimization algorithm, human-like characteristics, self-regulation, self/social belief, self/social-awareness, shared information processing, single-objective optimization, social metacognitive abilities, three layered interrelated architecture, Benchmark testing, Computational modeling, Heuristic algorithms, Monitoring, Optimization, Particle swarm optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969642}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969642}}, } @INPROCEEDINGS{lipinsk:2017:CECa, author={P. Lipinski}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimization of representation for extracting knowledge from ultra-high frequency time series}, year={2017}, editor = {Jose A. Lozano}, pages = {2759--2766}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={This paper concerns extracting knowledge from limit order books and using it to predict significant changes in the price of the financial asset under study. Limit order books are encoded in a feature-based data representation. A binary Support Vector Machine classifier is trained to predict whether a particular limit order book leads to a significant change in the price in a few successive time instants, or not. An Evolutionary Algorithm is used to find the optimal parameter setting of the feature-based data representation, so that the performance of the classifier was better. Computational experiments performed on financial ultra-high frequency time series coming from the London Stock Exchange Rebuild Order Book database confirmed that the optimization of the feature-based data representation is very effective in improving the performance of the classification.}, keywords = {data structures, evolutionary computation, feature extraction, financial data processing, learning (artificial intelligence), optimisation, pattern classification, stock markets, support vector machines, time series, London stock exchange rebuild order book database, binary support vector machine classifier training, classification performance, evolutionary algorithm, feature-based data representation, financial asset, financial ultra-high frequency time series, knowledge extraction, limit order books, optimal parameter setting, price changes, representation optimization, Filter banks, Optimization, Time series analysis, Time-frequency analysis}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969643}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969643}}, } @INPROCEEDINGS{garro:2017:CEC, author={B. A. Garro and K. Rodríguez and R. A. Vazquez}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={Designing artificial neural networks using differential evolution for classifying DNA microarrays}, year={2017}, editor = {Jose A. Lozano}, pages = {2767--2774}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The information obtained from the analysis of DNA microarrays is relevant to identify and predict illness, improve treatments, and to determine which genes are responsible to provoke a specific disease. However, the enormous quantity of genes and the few samples to be analyzed affect the performance of any classifier. For this reason, it is necessary to develop a methodology that combines a robust feature selection technique with a classification algorithm for classifying DNA microarrays. In this paper, we combine a feature selection technique based on the Artificial Bee Colony algorithm with an Artificial Neural Network (ANN). Furthermore, this ANN is automatically designed by a Differential Evolution (DE) algorithm that optimizes the synaptic weights, the architecture, and the transfer functions at the same time. To test the accuracy of the proposed methodology, we use the Leukemia AML-ALL dataset.}, keywords = {DNA, bioinformatics, cancer, evolutionary computation, feature selection, neural net architecture, pattern classification, ANN architecture, DE algorithm, DNA microarray classification, Leukemia AML-ALL dataset, artificial bee colony algorithm, artificial neural network, artificial neural network design, differential evolution algorithm, illness identification, illness prediction, robust feature selection technique, synaptic weight optimization, transfer functions, treatment improvement, Algorithm design and analysis, Classification algorithms, Diseases, Neurons, Optimization}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969644}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969644}}, } @INPROCEEDINGS{ramirez-atencia:2017:CEC, author={C. Ramirez-Atencia and V. Rodríguez-Fernández and A. Gonzalez-Pardo and D. Camacho}, booktitle={2017 IEEE Congress on Evolutionary Computation (CEC)}, title={New Artificial Intelligence approaches for future UAV Ground Control Stations}, year={2017}, editor = {Jose A. Lozano}, pages = {2775--2782}, address = {Donostia, San Sebastian, Spain}, publisher = {IEEE}, ISBN13 = {978-1-5090-4601-0}, abstract={The increasing interest in the use of Unmanned Aerial Vehicles (UAV) in the last years has opened up a new complex area of research applications. Many works have been focused on the applicability of new Artificial Intelligence techniques to facilitate the successfully execution of UAV operations from the Ground Control Stations (GCSs). Some of the most demanded applications in this field are the reduction of the workload of operators and the automation of training processes. This paper presents new algorithms focused on this field: a Multi-Objective Genetic Algorithm for solving Mission Planning and Replanning problems and a Procedure Following Evaluation methodology based on Petri Nets. This paper is based on a framework that simulates a GCS with support for multiple UAVs. The functionality of this framework has been extended in two different directions: on the one hand, to deal with Mission Designing, Automated Mission Planning and Replanning, and Alert Generation; and, on the other hand, to perform different analysis tasks of the UAV operators. Using this framework, a test mission has been executed and debriefed, focusing on the main AI-based issues described in this work.}, keywords = {Petri nets, artificial intelligence, autonomous aerial vehicles, control engineering computing, genetic algorithms, GCS, alert generation, artificial intelligence approaches, automated mission replanning, future UAV ground control stations, ground control stations, mission designing, mission replanning problems, multiobjective genetic algorithm, procedure following evaluation methodology, unmanned aerial vehicles, Approximation algorithms, Materials requirements planning, Planning, Sensors, Training}, ISBN13 = {978-1-5090-4601-0}, doi={10.1109/CEC.2017.7969645}, month = {5-8 June}, notes = {IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969645}}, }