%processed by gecco2015_toc.awk $Revision: 1.52 $ ARGC=3 Sun Aug 9 16:03:12 BST 2015 %1 gecco2015_toc.txt %2 gecco2015.bib %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Cao:2015:GECCO, author = {Cen Cao and Qingjian Ni and Yuqing Zhai}, title = {An Improved Collaborative Filtering Recommendation Algorithm Based on Community Detection in Social Networks}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1--8}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754670}, doi = {doi:10.1145/2739480.2754670}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recommendation algorithms in social networks have attracted much attention in recent years. Collaborative filtering recommendation algorithm is one of the most commonly used recommendation algorithms. Traditional user-based collaborative filtering recommendation algorithm recommends based on the user-item rating matrix, but the large amounts of data may cause low efficiency. In this paper, we propose an improved collaborative filtering recommendation algorithm based on community detection. Firstly, the user-item rating matrix is mapped into the user similarity network. Furthermore, a novel discrete particle swarm optimization algorithm is applied to find communities in the user similarity network, and finally Top-N items are recommend to the recommended user according to the communities. The experiments on a real dataset validate the effectiveness of the proposed algorithm for improving the precision, coverage and efficiency of recommendation.}, notes = {Also known as \cite{2754670} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Fortier:2015:GECCO, author = {Nathan Fortier and John Sheppard and Shane Strasser}, title = {Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {9--16}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754793}, doi = {doi:10.1145/2739480.2754793}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Bayesian networks are probabilistic graphical models that have proven to be able to handle uncertainty in many real-world applications. One key issue in learning Bayesian networks is parameter estimation, i.e., learning the local conditional distributions of each variable in the model. While parameter estimation can be performed efficiently when complete training data is available (i.e., when all variables have been observed), learning the local distributions becomes difficult when latent (hidden) variables are introduced. While Expectation Maximization (EM) is commonly used to perform parameter estimation in the context of latent variables, EM is a local optimization method that often converges to sub-optimal estimates. Although several authors have improved upon traditional EM, few have applied population based search techniques to parameter estimation, and most existing population-based approaches fail to exploit the conditional independence properties of the networks. We introduce two new methods for parameter estimation in Bayesian networks based on particle swarm optimization (PSO). The first is a single swarm PSO, while the second is a multi-swarm PSO algorithm. In the multi-swarm version, a swarm is assigned to the Markov blanket of each variable to be estimated, and competition is held between overlapping swarms. Results of comparing these new methods to several existing approaches indicate that the multi-swarm algorithm outperforms the competing approaches when compared using data generated from a variety of Bayesian networks.}, notes = {Also known as \cite{2754793} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Friedrich:2015:GECCO, author = {Tobias Friedrich and Timo Koetzing and Martin S. Krejca and Andrew M. Sutton}, title = {Robustness of Ant Colony Optimization to Noise}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {17--24}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754723}, doi = {doi:10.1145/2739480.2754723}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently Ant Colony Optimization (ACO) algorithms have been proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses focus on combinatorial problems, such as path finding. We analyse an ACO algorithm in a setting where we try to optimize the simple OneMax test function, but with additive posterior noise sampled from a Gaussian distribution. Without noise the classical (mu+1)-EA outperforms any ACO algorithm, with smaller mu being better; however, with large noise, the (mu+1)-EA fails, even for high values of mu (which are known to help against small noise). In this paper we show that ACO is able to deal with arbitrarily large noise in a graceful manner, that is, as long as the evaporation factor p is small enough dependent on the parameter delta2 of the noise and the dimension $n$ of the search space (p= o(1/(n(n + deltalog n)2 log n))), optimization will be successful.}, notes = {Also known as \cite{2754723} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Helbig:2015:GECCO, author = {Marde Helbig and Andries P. Engelbrecht}, title = {The Effect of Quantum and Charged Particles on the Performance of the Dynamic Vector-evaluated Particle Swarm Optimisation Algorithm}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {25--32}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754810}, doi = {doi:10.1145/2739480.2754810}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.}, notes = {Also known as \cite{2754810} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Kumar:2015:GECCO, author = {Udit Kumar and Jayadeva, and Sumit Soman}, title = {Enhancing IACO_R Local Search by Mtsls1-BFGS for Continuous Global Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {33--40}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754689}, doi = {doi:10.1145/2739480.2754689}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A widely known approach for continuous global optimization has been the Incremental Ant Colony Framework (IACOR). In this paper, we propose a strategy to introduce hybridization within the exploitation phase of the IACOR framework by using the Multi-Trajectory Local Search (Mtsls1) algorithm and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms. Our approach entails making a probabilistic choice between these algorithms. In case of stagnation, we switch the algorithm being used based on the last iteration. We evaluate our approach on the Soft Computing (SOCO) benchmark functions and present results by computing the mean and median errors on the global optima achieved, as well as the iterations required. We compare our approach with competing methods on a number of benchmark functions, and show that the proposed approach achieves improved results. In particular, we obtain the global optima in terms of average value for 14 out of 19 benchmark functions, and in terms of the median value for all SOCO benchmarks. At the same time, the proposed approach uses fewer function evaluations on several benchmarks when compared with competing methods, which have been found to use 54percent more function evaluations.}, notes = {Also known as \cite{2754689} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Liu:2015:GECCO, author = {Yu Liu and Wei-Neng Chen and Xiao-min Hu and Jun Zhang}, title = {An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {41--48}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754671}, doi = {doi:10.1145/2739480.2754671}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogeneous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.}, notes = {Also known as \cite{2754671} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Mavrovouniotis:2015:GECCO, author = {Michalis Mavrovouniotis and Felipe Martins Mueller and Shengxiang Yang}, title = {An Ant Colony Optimization Based Memetic Algorithm for the Dynamic Travelling Salesman Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {49--56}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754651}, doi = {doi:10.1145/2739480.2754651}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ant colony optimization (ACO) algorithms have proved to be able to adapt for solving dynamic optimization problems (DOPs). The integration of local search algorithms has also proved to significantly improve the output of ACO algorithms. However, almost all previous works consider stationary environments. In this paper, the MAX -MIN Ant System, one of the best ACO variations, is integrated with the unstringing and stringing (US) local search operator for the dynamic travelling salesman problem (DTSP). The best solution constructed by ACO is passed to the US operator for local search improvements. The proposed memetic algorithm aims to combine the adaptation capabilities of ACO for DOPs and the superior performance of the US operator on the static travelling salesman problem in order to tackle the DTSP. The experiments show that the MAX -MIN Ant System is able to provide good initial solutions to US and the proposed algorithm outperforms other peer ACO-based memetic algorithms on different DTSPs.}, notes = {Also known as \cite{2754651} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{MiguelAntonio:2015:GECCO, author = {Luis {Miguel Antonio} and Carlos Artemio {Coello Coello}}, title = {Particle Swarm Optimization Based on Linear Assignment Problem Transformations}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {57--64}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754789}, doi = {doi:10.1145/2739480.2754789}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle swarm optimization (PSO) algorithms have been widely used to solve a variety of optimization problems. Their success has motivated researchers to extend the use of these techniques to the multi-objective optimization field. However, most of these extensions have been used to solve multi-objective optimization problems (MOPs) with no more than three objective functions. Here, we propose a novel multi-objective PSO (MOPSO) algorithm characterized by the use of a recent approach that transforms a MOP into a linear assignment problem (LAP), with the aim of being able to solve many-objective optimization problems. Our proposed approach, called LAP based PSO (LAPSO), adopts the Munkres assignment algorithm to solve the generated LAPs and has no need of an external archive. LAPSO is compared with respect to three MOPSOs which are representative of the state-of-the-art in the area: the Optimized Multi-Objective Particle Swarm Optimizer (OMOPSO) the Speed-constrained Multiobjective Particle Swarm Optimizer (SMPSO) and a variant of the latter that uses the hypervolume indicator for its leader selection scheme (SMPSOhv). Our results indicate that LAPSO is able to outperform the MOPSOs with respect to which it was compared in most of the test problems adopted, specially when solving instances with more than three objectives.}, notes = {Also known as \cite{2754789} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Moritz:2015:GECCO, author = {Ruby L.V. Moritz and Martin Middendorf}, title = {Evolutionary Inheritance Mechanisms for Multi-criteriaDecision Making in Multi-agent Systems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {65--72}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754773}, doi = {doi:10.1145/2739480.2754773}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we study the use of different evolutionary inheritance mechanisms for the adaptation of parameters in a multi-agent system where the agents have to solve tasks that are distributed within a dynamic environment. In the studied system the agents have to form teams to execute the tasks. Deciding which task to execute next is a multi-criteria decision problem for which the agents use different ranking schemes. Agents that have successfully executed several tasks can reproduce and pass the type of ranking scheme they have used and some corresponding parameter values to their successors. Three types of evolutionary mechanisms are compared: haploid, diploid, and haplo-diploid. The latter one is new for multi-agent systems. The focus of our simulation experiments is to study the influence of the different evolutionary mechanisms on the diversity of the agents and on the resulting efficiency of the multi-agent system for different dynamic environments.}, notes = {Also known as \cite{2754773} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Noble:2015:GECCO, author = {Diego Vrague Noble and Felipe Grando and Ricardo Matsumura Araujo and Luis da Cunha Lamb}, title = {The Impact of Centrality on Individual and Collective Performance in Social Problem-Solving Systems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {73--80}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754679}, doi = {doi:10.1145/2739480.2754679}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we analyse the dependency between centrality and individual performance in socially-inspired problem-solving systems. By means of extensive numerical simulations, we investigate how individual performance in four different models correlate with four different classical centrality measures. Our main result shows that there is a high linear correlation between centrality and individual performance when individuals systematically exploit central positions. In this case, central individuals tend to deviate from the expected majority contribution behaviour. Although there is ample evidence about the relevance of centrality in social problem-solving, our work contributes to understand that some measures correlate better with individual performance than others due to individual traits, a position that is gaining strength in recent studies.}, notes = {Also known as \cite{2754679} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Yi:2015:GECCO, author = {Daqing Yi and Kevin D. Seppi and Michael A. Goodrich}, title = {Input-to-State Stability Analysis on Particle Swarm Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {81--88}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754782}, doi = {doi:10.1145/2739480.2754782}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper examines the dynamics of particle swarm optimization (PSO) by modelling PSO as a feedback cascade system and then applying input-to-state stability analysis. Using a feedback cascade system model we can include the effects of the global-best and personal-best values more directly in the model of the dynamics. Thus in contrast to previous study of PSO dynamics, the input-to-state stability property used here allows for the analysis of PSO both before and at stagnation. In addition, the use of input-to-state stability allows this analysis to preserve random terms which were heretofore simplified to constants. This analysis is important because it can inform the setting of PSO parameters and better characterize the nature of PSO as a dynamic system. This work also illuminates the way in which the personal-best and the global-best updates influence the bound on the particle's position and hence, how the algorithm exploits and explores the fitness landscape as a function of the personal best and global best.}, notes = {Also known as \cite{2754782} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Yu:2015:GECCO, author = {Xue Yu and Wei-Neng Chen and Xiao-min Hu and Jun Zhang}, title = {A Set-based Comprehensive Learning Particle Swarm Optimization with Decomposition for Multiobjective Traveling Salesman Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {89--96}, keywords = {Ant Colony Optimization and Swarm Intelligence}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754672}, doi = {doi:10.1145/2739480.2754672}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper takes the multiobjective travelling salesman problem (MOTSP) as the representative for multiobjective combinatorial problems and develop a set-based comprehensive learning particle swarm optimization (S-CLPSO) with decomposition for solving MOTSP. The main idea is to take advantages of both the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework and our previously proposed S-CLPSO method for discrete optimization. Consistent to MOEA/D, a multiobjective problem is decomposed into a set of subproblems, each of which is represented as a weight vector and solved by a particle. Thus the objective vector of a solution or the cost vector between two cities will be transformed into real fitness to be used in S-CLPSO for the exemplar construction, the heuristic information generation and the update of pBest. To validate the proposed method, experiments based on TSPLIB benchmark are conducted and the results indicate that the proposed algorithm can improve the solution quality to some degree.}, notes = {Also known as \cite{2754672} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Borgohain:2015:GECCO, author = {Samir Kr Borgohain and Shivashankar B. Nair}, title = {An Immuno-inspired Approach Towards Sentence Generation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {97--104}, keywords = {Artificial Immune Systems and Artificial Chemistries}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754805}, doi = {doi:10.1145/2739480.2754805}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Though human beings comprehend, imbibe and subsequently generate syntactically and semantically correct languages, the manner in which they do so has hardly been understood or unearthed. Most of the current work to achieve the same is heavily dependent on statistical and probabilistic data retrieved from a large corpus coupled with a formal grammar catering to the concerned natural language. This paper attempts to portray a technique based on an analogy described by Jerne on how his theory of the Idiotypic Network could possibly explain the human language generation capability. Starting with a repertoire of unigrams (antibodies) weaned from a corpus available a priori, we show how these can be sequenced to generate higher order n-grams that depict full or portions of correct sentences in that language. These sentences or their correct portions form a network similar to the Idiotypic Network that in turn aid in the generation of sentences or portions thereof which are new to the corpus signifying the learning of new and correct sequences. The network is built based on a modified version of the dynamics suggested by Farmer et. al. The paper describes the related dynamics of the network along with the results obtained from a corpus.}, notes = {Also known as \cite{2754805} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Fricke:2015:GECCO, author = {George Matthew Fricke and Sarah R. Black and Joshua P. Hecker and Judy L. Cannon and Melanie E. Moses}, title = {Distinguishing Adaptive Search from Random Search in Robots and T cells}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {105--112}, keywords = {Artificial Immune Systems and Artificial Chemistries}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754794}, doi = {doi:10.1145/2739480.2754794}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the (LN) environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently than a random model of search would suggest. We then test whether T cells that visit such locations have different movement patterns than other T cells. Our analysis suggests that T cells do adapt their movement in response to cues that may indicate the locations of DC targets. We test the ability of our method to identify frequently visited sites in T cells and in a swarm of simulated iAnt robots evolved to search using a suite of biologically-inspired behaviours. We compare the movement of T cells and robots that repeatedly sample the same locations in space with the movement of agents that do not resample space in order to understand whether repeated sampling alters movement. Our analysis suggests that specific environmental cues can be inferred from the movement of T cells. While the precise identity of these cues remains unknown, comparing adaptive search strategies of robots to the movement patterns of T cells lends insights into search efficiency in both systems.}, notes = {Also known as \cite{2754794} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Greensmith:2015:GECCO, author = {Julie Greensmith}, title = {Securing the Internet of Things with Responsive Artificial Immune Systems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {113--120}, keywords = {Artificial Immune Systems and Artificial Chemistries}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754816}, doi = {doi:10.1145/2739480.2754816}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Internet of Things is a network of smart objects, transforming everyday objects into entities which can measure, sense and understand their environment. The devices are uniquely identifiable, rely on near field connectivity, often in embedded devices. The Internet of Things is designed to be deployed without human intervention or interaction. One application is the smart house, with components including household appliances, networked with the user able to control devices remotely. However, the security inherent in these systems is added as somewhat of an afterthought. One hypothetical scenario is where a malicious party could exploit this technology with potentially disastrous consequences, turning on a cooker remotely leading to digital arson. Reliance on standard methods is insufficient to provide the user with adequate levels of security, an area where AIS may be extremely useful. There are currently limitations with AIS applied in security, focussing on detection without providing automatic responses. This problem provides an opportunity to advance AIS in providing both an ideal scenario for testing their real-world application and to develop novel responsive AIS. A responsive version of the deterministic Dendritic Cell Algorithm will be proposed to demonstrate how responsive AIS will need to be developed to meet these future challenges through proposing the incorporation of a model of T-cell responses.}, notes = {Also known as \cite{2754816} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Montero:2015:GECCO, author = {Elizabeth Montero and Maria-Cristina Riff}, title = {RAIS_TTP Revisited to Solve Relaxed Travel Tournament Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {121--128}, keywords = {Artificial Immune Systems and Artificial Chemistries}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754753}, doi = {doi:10.1145/2739480.2754753}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We are interested in methods and strategies that allow us to simplify the code of bio-inspired algorithms without altering their performance. In this paper, we study an artificial immune algorithm specially designed to solve Relaxed Travelling Tournament Problems which has been able to obtain new bounds for some instances of this problem. We use the EvoCa tuner to analyse the components of the algorithm in order to discard some parts of the code. The results show that the filtered algorithm is able to solve the instances as well as does the original algorithm, and with this code we have obtained new bounds for some instances of the problem.}, notes = {Also known as \cite{2754753} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bongard:2015:GECCO, author = {Josh C. Bongard and Anton Bernatskiy and Ken Livingston and Nicholas Livingston and John Long and Marc Smith}, title = {Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {129--136}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754750}, doi = {doi:10.1145/2739480.2754750}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Although recent work has demonstrated that modularity can increase evolvability in non-embodied systems, it remains to be seen how the morphologies of embodied agents influences the ability of an evolutionary algorithm to find useful and modular controllers for them. We hypothesize that a modular control system may enable different parts of a robot's body to sense and react to stimuli independently, enabling it to correctly recognize a seemingly novel environment as, in fact, a composition of familiar percepts and thus respond appropriately without need of further evolution. Here we provide evidence that supports this hypothesis: We found that such robots can indeed be evolved if (1) the robot's morphology is evolved along with its controller, (2) the fitness function selects for the desired behaviour and (3) also selects for conservative and robust behavior. In addition, we show that if constraints (1) and (3) are relaxed, or structural modularity is selected for directly, the robots have too little or too much modularity and lower evolvability. Thus, we demonstrate a previously unknown relationship between modularity and embodied cognition: evolving morphology and control such that robots exhibit conservative behavior indirectly selects for appropriate modularity and, thus, increased evolvability.}, notes = {Also known as \cite{2754750} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Clark:2015:GECCO, author = {Anthony J. Clark and Philip K. McKinley and Xiaobo Tan}, title = {Enhancing a Model-Free Adaptive Controller through Evolutionary Computation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {137--144}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754762}, doi = {doi:10.1145/2739480.2754762}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many robotic systems experience fluctuating dynamics during their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. One approach to mitigating these problems is to use an adaptive controller. For example, in model-free adaptive control (MFAC) a controller learns how to drive a system by continually updating link weights of an artificial neural network (ANN). However, determining the optimal control parameters for MFAC, including the structure of the underlying ANN, is a challenging process. In this paper we investigate how to enhance the online adaptability of MFAC-based systems through computational evolution. We apply the proposed methods to a simulated robotic fish propelled by a flexible caudal fin. Results demonstrate that the robot is able to effectively respond to changing fin characteristics and varying control signals when using an evolved MFAC controller. Notably, the system is able to adapt to characteristics not encountered during evolution. The proposed technique is general and can be applied to improve the adaptability of other cyber-physical systems.}, notes = {Also known as \cite{2754762} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Corucci:2015:GECCO, author = {Francesco Corucci and Marcello Calisti and Helmut Hauser and Cecilia Laschi}, title = {Novelty-Based Evolutionary Design of Morphing Underwater Robots}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {145--152}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754686}, doi = {doi:10.1145/2739480.2754686}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent developments in robotics demonstrated that bioinspiration and embodiment are powerful tools to achieve robust behaviour in presence of little control. In this context morphological design is usually performed by humans, following a set of heuristic principles: in general this can be limiting, both from an engineering and an artificial life perspectives. In this work we thus suggest a different approach, leveraging evolutionary techniques. The case study is the one of improving the locomotion capabilities of an existing bioinspired robot. First, we explore the behaviour space of the robot to discover a number of qualitatively different morphology-enabled behaviours, from whose analysis design indications are gained. The suitability of novelty search -- a recent open-ended evolutionary algorithm -- for this intended purpose is demonstrated. Second, we show how it is possible to condense such behaviours into a reconfigurable robot capable of online morphological adaptation (morphosis, morphing). Examples of successful morphing are demonstrated, in which changing just one morphological parameter entails a dramatic change in the behavior: this is promising for a future robot design. The approach here adopted represents a novel computed-aided, bioinspired, design paradigm, merging human and artificial creativity. This may result in interesting implications also for artificial life, having the potential to contribute in exploring underwater locomotion as-it-could-be.}, notes = {Also known as \cite{2754686} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{DivbandSoorati:2015:GECCO, author = {Mohammad {Divband Soorati} and Heiko Hamann}, title = {The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {153--160}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754676}, doi = {doi:10.1145/2739480.2754676}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.}, notes = {Also known as \cite{2754676} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{FernandezPerez:2015:GECCO, author = {Inaki {Fernandez Perez} and Amine Boumaza and Francois Charpillet}, title = {Decentralized Innovation Marking for Neural Controllers in Embodied Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {161--168}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754759}, doi = {doi:10.1145/2739480.2754759}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel innovation marking method for Neuro-Evolution of Augmenting Topologies in Embodied Evolutionary Robotics. This method does not rely on a centralized clock, which makes it well suited for the decentralized nature of EE where no central evolutionary process governs the adaptation of a team of robots exchanging messages locally. This method is inspired from event dating algorithms, based on logical clocks, that are used in distributed systems, where clock synchronization is not possible. We compare our method to odNEAT, an algorithm in which agents use local time clocks as innovation numbers, on two multi-robot learning tasks: navigation and item collection. Our experiments showed that the proposed method performs as well as odNEAT, with the added benefit that it does not rely on synchronization of clocks and is not affected by time drifts.}, notes = {Also known as \cite{2754759} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Hart:2015:GECCO, author = {Emma Hart and Andreas Steyven and Ben Paechter}, title = {Improving Survivability in Environment-driven Distributed Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {169--176}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754688}, doi = {doi:10.1145/2739480.2754688}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ensuring the integrity of a robot swarm in terms of maintaining a stable population of functioning robots over long periods of time is a mandatory prerequisite for building more complex systems that achieve user-defined tasks. mEDEA is an environment-driven evolutionary algorithm that provides promising results using an implicit fitness function combined with a random genome selection operator. Motivated by the need to sustain a large population with sufficient spare energy to carry out user-defined tasks in the future, we develop an explicit fitness metric providing a measure of fitness that is relative to surrounding robots and examine two methods by which it can influence spread of genomes. Experimental results in simulation find that use of the fitness-function provides significant improvements over the original algorithm; in particular, a method that influences the frequency and range of broadcasting when combined with random selection has the potential to conserve energy whilst maintaining performance, a critical factor for physical robots.}, notes = {Also known as \cite{2754688} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Heinerman:2015:GECCO, author = {Jacqueline Heinerman and Dexter Drupsteen and A.E. Eiben}, title = {Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {177--183}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754743}, doi = {doi:10.1145/2739480.2754743}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Adapting the control systems of robots on the fly is important in robotic systems of the future. In this paper we present and investigate a three-fold adaptive system based on evolution, individual and social learning in a group of robots and report on a proof-of-concept study based on e-pucks. We distinguish inheritable and learnable components in the robots' makeup, specify and implement operators for evolution, learning and social learning, and test the system in an arena where the task is to learn to avoid obstacles. In particular, we make the sensory layout evolvable, the locomotion control system learnable and investigate the effects of including social learning in the adaptation engine. Our simulation experiments demonstrate that the full mix of three adaptive mechanisms is practicable and that adding social learning leads to better controllers faster.}, notes = {Also known as \cite{2754743} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Marriott:2015:GECCO, author = {Chris Marriott and Jobran Chebib}, title = {Finding a Mate With No Social Skills}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {185--192}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754804}, doi = {doi:10.1145/2739480.2754804}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Sexual reproductive behaviour has a necessary social coordination component as willing and capable partners must both be in the right place at the right time. While there are many known social behavioural adaptations to support solutions to this problem, we explore the possibility and likelihood of solutions that rely only on non-social mechanisms. We find three kinds of social organization that help solve this social coordination problem (herding, assortative mating, and natal philopatry) emerge in populations of simulated agents with no social mechanisms available to support these organizations. We conclude that the non-social origins of these social organizations around sexual reproduction may provide the environment for the development of social solutions to the same and different problems.}, notes = {Also known as \cite{2754804} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Methenitis:2015:GECCO, author = {Georgios Methenitis and Daniel Hennes and Dario Izzo and Arnoud Visser}, title = {Novelty Search for Soft Robotic Space Exploration}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {193--200}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754731}, doi = {doi:10.1145/2739480.2754731}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of soft robots in future space exploration is still a far-fetched idea, but an attractive one. Soft robots are inherently compliant mechanisms that are well suited for locomotion on rough terrain as often faced in extra-planetary environments. Depending on the particular application and requirements, the best shape (or body morphology) and locomotion strategy for such robots will vary substantially. Recent developments in soft robotics and evolutionary optimization showed the possibility to simultaneously evolve the morphology and locomotion strategy in simulated trials. The use of techniques such as generative encoding and neural evolution were key to these findings. In this paper, we improve further on this methodology by introducing the use of a novelty measure during the evolution process. We compare fitness search and novelty search in different gravity levels and we consistently find novelty-based search to perform as good as or better than a fitness--based search, while also delivering a greater variety of designs. We propose a combination of the two techniques using fitness-elitism in novelty search to obtain a further improvement. We then use our methodology to evolve the gait and morphology of soft robots at different gravity levels, finding a taxonomy of possible locomotion strategies that are analysed in the context of space-exploration.}, notes = {Also known as \cite{2754731} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Zieba:2015:GECCO, author = {Karol Zieba and Josh Bongard}, title = {An Embodied Approach for Evolving Robust Visual Classifiers}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {201--208}, keywords = {Artificial Life/Robotics/Evolvable Hardware}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754788}, doi = {doi:10.1145/2739480.2754788}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Despite recent demonstrations that deep learning methods can successfully recognize and categorize objects using high dimensional visual input, other recent work has shown that these methods can fail when presented with novel input. However, a robot that is free to interact with objects should be able to reduce spurious differences between objects belonging to the same class through motion and thus reduce the likelihood of overfitting. Here we demonstrate a robot that achieves more robust categorization when it evolves to use proprioceptive sensors and is then trained to rely increasingly on vision, compared to a similar robot that is trained to categorize only with visual sensors. This work thus suggests that embodied methods may help scaffold the eventual achievement of robust visual classification.}, notes = {Also known as \cite{2754788} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bhardwaj:2015:GECCO, author = {Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna}, title = {An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {209--216}, keywords = {genetic algorithms, genetic programming, Biological and Biomedical Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754710}, doi = {doi:10.1145/2739480.2754710}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analysed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.}, notes = {Also known as \cite{2754710} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Clausen:2015:GECCO, author = {Rudy Clausen and Emmanuel Sapin and Kenneth A. {De Jong} and Amarda Shehu}, title = {Evolution Strategies for Exploring Protein Energy Landscapes}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {217--224}, keywords = {Biological and Biomedical Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754692}, doi = {doi:10.1145/2739480.2754692}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The focus on important diseases of our time has prompted many experimental labs to resolve and deposit functional structures of disease-causing or disease-participating proteins. At this point, many functional structures of wild type and disease-involved variants of a protein exist in structural databases. The objective for computational approaches is to employ such information to discover features of the underlying energy landscape on which functional structures reside. Important questions about which subset of structures are most thermodynamically-stable remain unanswered. The challenge is how to transform an essentially discrete problem into one where continuous optimization is suitable and effective. In this paper, we present such a transformation, which allows adapting and applying evolution strategies to explore an underlying continuous variable space and locate the global optimum of a multimodal fitness landscape. The paper presents results on wildtype and mutant sequences of proteins implicated in human disorders, such as cancer and Amyotrophic lateral sclerosis. More generally, the paper offers a methodology for transforming a discrete problem into a continuous optimization one as a way to possibly address outstanding discrete problems in the evolutionary computation community.}, notes = {Also known as \cite{2754692} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Lincoln:2015:GECCO, author = {Stephen Lincoln and Ian Rogers and Ranjan Srivastava}, title = {Metabolic Design And Engineering Through Ant Colony Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {225--232}, keywords = {Biological and Biomedical Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754817}, doi = {doi:10.1145/2739480.2754817}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Due to the vast search space of all possible combinations of reaction knockouts in Escherichia coli, determining the best combination of knockouts for over-production of a metabolite of interest is a computationally expensive task. Ant colony optimization (ACO) applied to genome-scale metabolic models via flux balance analysis (FBA) provides a means by which such a solution space may feasibly be explored. In previous work, the Minimization of Metabolic Adjustment (MoMA) objective function for FBA was used in conjunction with ACO to identify the best gene knockouts for succinic acid production. In this work, algorithmic and biological constraints are introduced to further reduce the solution space. We introduce Stochastic Exploration Edge Reduction Ant Colony Optimization, or STEER-ACO. Algorithmically, ACO is modified to refine its search space over time allowing for greater initial coverage of the solution space while ultimately honing on a high quality solution. Biologically, a heuristic is introduced allowing the maximum number of knockouts to be no greater than five. Beyond this number, cellular viability becomes suspect. Results using this approach versus previous methods are reported.}, notes = {Also known as \cite{2754817} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Monteagudo:2015:GECCO, author = {Angel Monteagudo and Jose Santos}, title = {Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {233--240}, keywords = {Biological and Biomedical Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754640}, doi = {doi:10.1145/2739480.2754640}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We used evolutionary computing for optimizing cancer treatments taking into account the presence and effects of cancer stem cells. We used a cellular automaton to model tumour growth at cellular level, based on the presence of the main cancer hallmarks in the cells. The cellular automaton allows the study of the emergent behaviour of the multicellular system evolution in different scenarios defined by the predominance of the different hallmarks. When cancer stem cells (CSCs) are modelled, the multicellular system evolution is additionally dependent on the CSC tumor regrowth capability because their differentiation to non-stem cancer cells. When a standard treatment is applied against non-stem (differentiated) cancer cells, different effects are present depending on the strategy used to eliminate these non-stem cancer cells. We used Differential Evolution to optimize the treatment application strategy in terms of intensity, duration and periodicity to minimize the final outcome of tumor growth and regrowth.}, notes = {Also known as \cite{2754640} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ameca-Alducin:2015:GECCO, author = {Maria-Yaneli Ameca-Alducin and Efren Mezura-Montes and Nicandro Cruz-Ramirez}, title = {A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {241--248}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754786}, doi = {doi:10.1145/2739480.2754786}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Repair methods, which usually require feasible solutions as reference, have been employed by Evolutionary Algorithms to solve constrained optimization problems. In this work, a novel repair method, which does not require feasible solutions as reference and inspired by the differential mutation, is added to an algorithm which uses two variants of differential evolution to solve dynamic constrained optimization problems. The proposed repair method replaces a local search operator with the aim to improve the overall performance of the algorithm in different frequencies of change in the constrained space. The proposed approach is compared against other recently proposed algorithms in an also recently proposed benchmark. The results show that the proposed improved algorithm outperforms its original version and provides a very competitive overall performance with different change frequencies.}, notes = {Also known as \cite{2754786} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Arnold:2015:GECCO, author = {Dirk V. Arnold and Jeremy Porter}, title = {Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {249--256}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754813}, doi = {doi:10.1145/2739480.2754813}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We consider the problem of devising an approach for handling inequality constraints in evolution strategies that allows converging linearly to optimal solutions on sphere functions with a single linear constraint. An analysis of the single-step behaviour of the (1+1)-ES shows that the task of balancing improvements in the objective with those in the constraint function is quite delicate, and that adaptive approaches need to be carefully designed in order to avoid failure. Based on the understanding gained, we propose a simple augmented Lagrangian approach and experimentally demonstrate good performance on a broad range of sphere functions as well as on moderately ill-conditioned ellipsoids with a single linear constraint.}, notes = {Also known as \cite{2754813} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Baudivs:2015:GECCO, author = {Petr Baudis and Petr Posik}, title = {Global Line Search Algorithm Hybridized with Quadratic Interpolation and Its Extension to Separable Functions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {257--264}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754717}, doi = {doi:10.1145/2739480.2754717}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel hybrid algorithm Brent-STEP for univariate global function minimization, based on the global line search method STEP and accelerated by Brent's method, a local optimizer that combines quadratic interpolation and golden section steps. We analyse the performance of the hybrid algorithm on various one-dimensional functions and experimentally demonstrate a significant improvement relative to its constituent algorithms in most cases. We then generalize the algorithm to multivariate functions, proposing a scheme to interleave evaluations across dimensions to achieve smoother and more efficient convergence. We experimentally demonstrate the highly competitive performance of the proposed multivariate algorithm on separable functions of the BBOB benchmark. The combination of good performance and smooth convergence on separable functions makes the algorithm an interesting candidate for inclusion in algorithmic portfolios or hybrid algorithms that aim to provide good performance on a wide range of problems.}, notes = {Also known as \cite{2754717} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Kerschke:2015:GECCO, author = {Pascal Kerschke and Mike Preuss and Simon Wessing and Heike Trautmann}, title = {Detecting Funnel Structures by Means of Exploratory Landscape Analysis}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {265--272}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754642}, doi = {doi:10.1145/2739480.2754642}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.}, notes = {Also known as \cite{2754642} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Koch:2015:GECCO, author = {Patrick Koch and Samineh Bagheri and Wolfgang Konen and Christophe Foussette and Peter Krause and Thomas Baeck}, title = {A New Repair Method For Constrained Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {273--280}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754658}, doi = {doi:10.1145/2739480.2754658}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nowadays, constraints play an important role in industry, because most industrial optimization tasks underlay several restrictions. Finding good solutions for a particular problem with respect to all constraint functions can be expensive, especially when the dimensionality of the search space is large and many constraint functions are involved. Unfortunately function evaluations in industrial optimization are heavily limited, because often expensive simulations must be conducted. For such high-dimensional optimization tasks, the constraint optimization algorithm COBRA was proposed, making use of surrogate modelling for both the objective and the constraint functions. In this paper we present a new mechanism for COBRA to repair infill solutions with slightly violated constraints. The repair mechanism is based on gradient descent on surrogates of the constraint functions and aims at finding nearby feasible solutions. We test the repair mechanism on a real-world problem from the automotive industry and on other synthetic test cases. It is shown in this paper that with the integration of the repair method, the percentage of infeasible solutions is significantly reduced, leading to faster convergence and better final results.}, notes = {Also known as \cite{2754658} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Krause:2015:GECCO, author = {Oswin Krause and Tobias Glasmachers}, title = {A CMA-ES with Multiplicative Covariance Matrix Updates}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {281--288}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754781}, doi = {doi:10.1145/2739480.2754781}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Covariance matrix adaptation (CMA) mechanisms are core building blocks of modern evolution strategies. Despite sharing a common principle, the exact implementation of CMA varies considerably between different algorithms. In this paper, we investigate the benefits of an exponential parametrization of the covariance matrix in the CMA-ES. This technique was first proposed for the xNES algorithm. It results in a multiplicative update formula for the covariance matrix. We show that the exponential parametrisation and the multiplicative update are compatible with all mechanisms of CMA-ES. The resulting algorithm, xCMA-ES, performs at least on par with plain CMA-ES. Its advantages show in particular with updates that actively decrease the sampling variance in specific directions, i.e., for active constraint handling.}, notes = {Also known as \cite{2754781} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Liu:2015:GECCOa, author = {Xiao-Fang Liu and Zhi-Hui Zhan and Jun Zhang}, title = {Dichotomy Guided Based Parameter Adaptation for Differential Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {289--296}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754646}, doi = {doi:10.1145/2739480.2754646}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Differential evolution (DE) is an efficient and powerful population-based stochastic evolutionary algorithm, which evolves according to the differential between individuals. The success of DE in obtaining the optima of a specific problem depends greatly on the choice of mutation strategies and control parameter values. Good parameters lead the individuals towards optima successfully. The increasing of the success rate (the ratio of entering the next generation successfully) of population can speed up the searching. Adaptive DE incorporates success-history or population-state based parameter adaptation. However, sometimes poor parameters may improve individual with small probability and are regarded as successful parameters. The poor parameters may mislead the parameter control. So, in this paper, we propose a novel approach to distinguish between good and poor parameters in successful parameters. In order to speed up the convergence of algorithm and find more good parameters, we propose a dichotomy adaptive DE (DADE), in which the successful parameters are divided into two parts and only the part with higher success rate is used for parameter adaptation control. Simulation results show that DADE is competitive to other classic or adaptive DE algorithms on a set of benchmark problem and IEEE CEC 2014 test suite.}, notes = {Also known as \cite{2754646} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Melkozerov:2015:GECCO, author = {Alexander Melkozerov and Hans-Georg Beyer}, title = {Towards an Analysis of Self-Adaptive Evolution Strategies on the Noisy Ellipsoid Model}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {297--304}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754800}, doi = {doi:10.1145/2739480.2754800}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper analyses the multi-recombinant self-adaptive evolution strategy (ES), denoted as(mu/muI, lambda)-σSA-ES on the convex-quadratic function class under the influence of noise, which is referred to as noisy ellipsoid model. Asymptotically exact progress rate and self-adaptation response measures are derived (i.e., for N to N - search space dimensionality) for the considered objective function model and verified using experimental ES runs.}, notes = {Also known as \cite{2754800} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Shirakawa:2015:GECCO, author = {Shinichi Shirakawa and Youhei Akimoto and Kazuki Ouchi and Kouzou Ohara}, title = {Sample Reuse in the Covariance Matrix Adaptation Evolution Strategy Based on Importance Sampling}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {305--312}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754704}, doi = {doi:10.1145/2739480.2754704}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent studies reveal that the covariance matrix adaptation evolution strategy (CMA-ES) updates the parameters based on the natural gradient. The rank-based weight is considered the result of the quantile-based transformation of the objective value and the parameters are adjusted in the direction of the natural gradient estimated by Monte-Carlo with the samples drawn from the current distribution. In this paper, we propose a sample reuse mechanism for the CMA-ES. On the basis of the importance sampling, the past samples are reused to reduce the estimation variance of the quantile and the natural gradient. We derive the formula for the rank-mu update of the covariance matrix and the mean vector update using the past samples, then incorporate it into the CMA-ES without the step-size adaptation. From the numerical experiments, we observe that the proposed approach helps to reduce the number of function evaluations on many benchmark functions, especially when the number of samples at each iteration is relatively small.}, notes = {Also known as \cite{2754704} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Sun:2015:GECCO, author = {Yuan Sun and Michael Kirley and Saman Kumara Halgamuge}, title = {Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {313--320}, keywords = {Continuous Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754666}, doi = {doi:10.1145/2739480.2754666}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. Ideally, the interacting decision variables should be assigned to the same sub-component and the interdependency between sub-components should be kept to a minimum. Differential grouping, a recently proposed method, has high decomposition accuracy across a suite of benchmark functions. However, we show that differential grouping can only identify decision variables that interact directly. Subsequently, we propose an extension of differential grouping that is able to correctly identify decision variables that also interact indirectly. Empirical studies show that our extended differential grouping method achieves perfect decomposition on all of the benchmark functions investigated. Significantly, when our decomposition method is embedded in the cooperative co-evolution framework, it achieves comparable or better solution quality than the differential grouping method.}, notes = {Also known as \cite{2754666} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Jonsson:2015:GECCO, author = {Bjoern \THor Jonsson and Amy K. Hoover and Sebastian Risi}, title = {Interactively Evolving Compositional Sound Synthesis Networks}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {321--328}, keywords = {Digital Entertainment Technologies and Arts}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754796}, doi = {doi:10.1145/2739480.2754796}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {While the success of electronic music often relies on the uniqueness and quality of selected timbres, many musicians struggle with complicated and expensive equipment and techniques to create their desired sounds. Instead, this paper presents a technique for producing novel timbres that are evolved by the musician through interactive evolutionary computation. Each timbre is produced by an oscillator, which is represented by a special type of artificial neural network (ANN) called a compositional pattern producing network (CPPN). While traditional ANNs compute only sigmoid functions at their hidden nodes, CPPNs can theoretically compute any function and can build on those present in traditional synthesizers (e.g. square, sawtooth, triangle, and sine waves functions) to produce completely novel timbres. Evolved with NeuroEvolution of Augmenting Topologies (NEAT), the aim of this paper is to explore the space of potential sounds that can be generated through such compositional sound synthesis networks (CSSNs). To study the effect of evolution on subjective appreciation, participants in a listener study ranked evolved timbres by personal preference, resulting in preferences skewed toward the first and last generations. In the long run, the CSSN's ability to generate a variety of different and rich timbre opens up the intriguing possibility of evolving a complete CSSN-encoded synthesizer.}, notes = {Also known as \cite{2754796} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Lessin:2015:GECCO, author = {Dan Lessin and Sebastian Risi}, title = {Darwin's Avatars: A Novel Combination of Gameplay and Procedural Content Generation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {329--336}, keywords = {Digital Entertainment Technologies and Arts}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754749}, doi = {doi:10.1145/2739480.2754749}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The co-evolution of morphology and control for virtual creatures enables the creation of a novel form of gameplay and procedural content generation. Starting with a creature evolved to perform a simple task such as locomotion and removing its brain, the remaining body can be employed in a compelling interactive control problem. Just as we enjoy the challenge and reward of mastering helicopter flight or learning to play a musical instrument, learning to control such a creature through manual activation of its actuators presents an engaging and rewarding puzzle. Importantly, the novelty of this challenge is inexhaustible, since the evolution of virtual creatures provides a way to procedurally generate content for such a game. An endless series of creatures can be evolved for a task, then have their brains removed to become the game's next human-control challenge. To demonstrate this new form of gameplay and content generation, a proof-of-concept game--tentatively titled Darwin's Avatars--was implemented using evolved creature content, and user tested. This implementation also provided a unique opportunity to compare human and evolved control of evolved virtual creatures, both qualitatively and quantitatively, with interesting implications for improvements and future work.}, notes = {Also known as \cite{2754749} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{PerezLiebana:2015:GECCO, author = {Diego {Perez Liebana} and Jens Dieskau and Martin Hunermund and Sanaz Mostaghim and Simon Lucas}, title = {Open Loop Search for General Video Game Playing}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {337--344}, keywords = {Digital Entertainment Technologies and Arts}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754811}, doi = {doi:10.1145/2739480.2754811}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {General Video Game Playing is a sub-field of Game Artificial Intelligence, where the goal is to find algorithms capable of playing many different real-time games, some of them unknown a priori. In this scenario, the presence of domain knowledge must be severely limited, or the algorithm will overfit to the training games and perform poorly on the unknown games of the test set. Research in this area has been of special interest in the last years, with emerging contests like the General Video Game AI (GVG-AI) Competition. This paper introduces three different open loop techniques for dealing with this problem. First, a simple directed depth first search algorithm is employed as a baseline. Then, a tree search algorithm with a multi-armed bandit based tree policy is presented, followed by a Rolling Horizon Evolutionary Algorithm (RHEA) approach. In order to test these techniques, the games from the GVG-AI Competition framework are used as a benchmark, evaluation on a training set of 29 games, and submitting to the 10 unknown games at the competition website. Results show how the general game-independent heuristic proposed works well across all algorithms and games, and how the RHEA becomes the best evolutionary technique in the rankings of the test set.}, notes = {Also known as \cite{2754811} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Schrum:2015:GECCO, author = {Jacob Schrum and Risto Miikkulainen}, title = {Solving Interleaved and Blended Sequential Decision-Making Problems through Modular Neuroevolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {345--352}, keywords = {Digital Entertainment Technologies and Arts}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754744}, doi = {doi:10.1145/2739480.2754744}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many challenging sequential decision-making problems require agents to master multiple tasks, such as defence and offense in many games. Learning algorithms thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well the methods work depends on the nature of the tasks: Interleaved tasks are disjoint and have different semantics, whereas blended tasks have regions where semantics from different tasks overlap. While many methods work well in interleaved tasks, blended tasks are difficult for methods with strict, human-specified task divisions, such as Multitask Learning. In such problems, task divisions should be discovered automatically. To demonstrate the power of this approach, the MM-NEAT neuroevolution framework is applied in this paper to two variants of the challenging video game of Ms. Pac-Man. In the simplified interleaved version of the game, the results demonstrate when and why such machine-discovered task divisions are useful. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. Modular neuroevolution is thus a promising technique for discovering multimodal behaviour for challenging real-world tasks.}, notes = {Also known as \cite{2754744} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Wang:2015:GECCO, author = {Sunrise Wang and James Edward Gain and Geoff Stuart Nistchke}, title = {Controlling Crowd Simulations using Neuro-Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {353--360}, keywords = {Digital Entertainment Technologies and Arts}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754715}, doi = {doi:10.1145/2739480.2754715}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Crowd simulations have become increasingly popular in films over the last decade, appearing in large crowd shots of many big name block-buster films. An important requirement for crowd simulations in films is that they should be directable both at a high and low level. As agent-based techniques allow for low-level directability and more believable crowds, they are typically used in this field. However, due to the bottom-up nature of these techniques, to achieve high level directability, agent-level parameters must be adjusted until the desired crowd behaviour emerges. As manually adjusting parameters is a time consuming and tedious process, this paper investigates a method for automating this, using Neuro-Evolution. To this end, the Conventional Neuro-Evolution (CNE), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Neuro-Evolution of Augmenting Topologies (NEAT), and Enforced Sub Populations (ESP) algorithms are compared across a variety of representative crowd simulation scenarios. Overall, it was found that CMA-ES generally performs the best across the selected simulations.}, notes = {Also known as \cite{2754715} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Cutillas-Lozano:2015:GECCO, author = {Jose-Matias Cutillas-Lozano and Domingo Gimenez and Francisco Almeida}, title = {Hyperheuristics Based on Parametrized Metaheuristic Schemes}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {361--368}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754641}, doi = {doi:10.1145/2739480.2754641}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of a unified parametrized scheme for metaheuristics facilitates the development of metaheuristics and their application. The unified scheme can also be used to implement hyperheuristics on top of parametrized metaheuristics, selecting appropriate values for the metaheuristic parameters, and consequently the metaheuristic itself. The applicability of hyperheuristics to efficiently solve computational search problems is tested with the application of local and global search methods (GRASP, Tabu Search, Genetic algorithms and Scatter Search) and their combinations to three problems: a problem of optimization of power consumption in operation of wells,the determination of the kinetic constants of a chemical reaction and the maximum diversity problem. The hyperheuristic approach provides satisfactory values for the metaheuristic parameters and consequently satisfactory metaheuristics.}, notes = {Also known as \cite{2754641} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Daolio:2015:GECCO, author = {Fabio Daolio and Arnaud Liefooghe and Sebastien Verel and Hernan Aguirre and Kiyoshi Tanaka}, title = {Global vs Local Search on Multi-objective NK-Landscapes: Contrasting the Impact of Problem Features}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {369--376}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754745}, doi = {doi:10.1145/2739480.2754745}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computationally hard multi-objective combinatorial optimization problems are common in practice, and numerous evolutionary multi-objective optimization (EMO) algorithms have been proposed to tackle them. Our aim is to understand which (and how) problem features impact the search performance of such approaches. In this paper, we consider two prototypical dominance-based algorithms: a global EMO strategy using an ergodic variation operator (GSEMO) and a neighbourhood-based local search heuristic (PLS). Their respective runtime is estimated on a benchmark of combinatorial problems with tunable ruggedness, objective space dimension, and objective correlation ($\rho$MNK-landscapes). In other words, benchmark parameters define classes of instances with increasing empirical problem hardness; we enumerate and characterize the search space of small instances. Our study departs from simple performance comparison to systematically analyse the correlations between runtime and problem features, contrasting their association with search performance within and across instance classes, for both chosen algorithms. A mixed-model approach then allows us to further generalize from the experimental design, supporting a sound assessment of the joint impact of instance features on EMO search performance.}, notes = {Also known as \cite{2754745} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Dubois-Lacoste:2015:GECCO, author = {Jeremie Dubois-Lacoste and Holger H. Hoos and Thomas Stuetzle}, title = {On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {377--384}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754747}, doi = {doi:10.1145/2739480.2754747}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a thorough empirical investigation of the scaling behaviour of state-of-the-art local search algorithms for the TSP; in particular, we study the scaling of running time required for finding optimal solutions to Euclidean TSP instances. We use a recently introduced bootstrapping approach to assess the statistical significance of the scaling models thus obtained and contrast these models with those recently reported for the Concorde algorithm. In particular, we answer the question whether the scaling behaviour of state-of-the-art local search algorithms for the TSP differs by more than a constant from that required by Concorde to find the first optimal solution to a given TSP instance.}, notes = {Also known as \cite{2754747} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Faulkner:2015:GECCO, author = {Hayden Faulkner and Sergey Polyakovskiy and Tom Schultz and Markus Wagner}, title = {Approximate Approaches to the Traveling Thief Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {385--392}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754716}, doi = {doi:10.1145/2739480.2754716}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This study addresses the recently introduced Travelling Thief Problem (TTP) which combines the classical Traveling Salesman Problem (TSP) with the 0-1 Knapsack Problem (KP). The problem consists of a set of cities, each containing a set of available items with weights and profits. It involves searching for a permutation of the cities to visit and a decision on items to pick. A selected item contributes its profit to the overall profit at the price of higher transportation cost incurred by its weight. The objective is to maximize the resulting profit. We propose a number of problem-specific packing strategies run on top of TSP solutions derived by the Chained Lin-Kernighan heuristic. The investigations provided on the set of benchmark instances prove their rapidity and efficiency when compared with an approximate mixed integer programming based approach and state-of-the-art heuristic solutions from the literature.}, notes = {Also known as \cite{2754716} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Garcia-Alvarez:2015:GECCO, author = {Jorge Garcia-Alvarez and Miguel A. Gonzalez and Camino R. Vela}, title = {A Genetic Algorithm for Scheduling Electric Vehicle Charging}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {393--400}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754695}, doi = {doi:10.1145/2739480.2754695}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper addresses a problem motivated by a real life environment, in which we have to schedule the charge of electric vehicles in a parking, subject to a set of constraints, with the objective of minimizing the total tardiness. We consider both the static version of the problem, where we know in advance the arrival time, charging time and due date of every vehicle, and also the dynamic version of it. We design a genetic algorithm with some components specifically tailored to deal with the problem. In the experimental study we evaluate the proposed algorithm in a benchmark set taken from the literature, and we also compare it against the state-of-the-art showing that our proposal is significantly better.}, notes = {Also known as \cite{2754695} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Herrmann:2015:GECCO, author = {Sebastian Herrmann and Franz Rothlauf}, title = {Predicting Heuristic Search Performance with PageRank Centrality in Local Optima Networks}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {401--408}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754691}, doi = {doi:10.1145/2739480.2754691}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Previous studies have used statistical analysis of fitness landscapes such as ruggedness and deceptiveness in order to predict the expected quality of heuristic search methods. Novel approaches for predicting the performance of heuristic search are based on the analysis of local optima networks (LONs). A LON is a compressed stochastic model of a fitness landscape's basin transitions. Recent literature has suggested using various LON network measurements as predictors for local search performance. In this study, we suggest PageRank centrality as a new measure for predicting the performance of heuristic search methods using local search. PageRank centrality is a variant of Eigenvector centrality and reflects the probability that a node in a network is visited by a random walk. Since the centrality of high-quality solutions in LONs determines the search difficulty of the underlying fitness landscape and since the big valley property suggests that local optima are not randomly distributed in the search space but rather clustered and close to one another, PageRank centrality can serve as a good predictor for local search performance. In our experiments for NK-models and the travelling salesman problem, we found that the PageRank centrality is a very good predictor for the performance of first-improvement local search as well as simulated annealing, since it explains more than 90percent of the variance of search performance. Furthermore, we found that PageRank centrality is a better predictor of search performance than traditional approaches such as ruggedness, deceptiveness, and the length of the shortest path to the optimum.}, notes = {Also known as \cite{2754691} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Kaufmann:2015:GECCO, author = {Paul Kaufmann and Cong Shen}, title = {Generator Start-up Sequences Optimization for Network Restoration Using Genetic Algorithm and Simulated Annealing}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {409--416}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754647}, doi = {doi:10.1145/2739480.2754647}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the domain of power grid systems, scheduling tasks are widespread. Typically, linear programming (LP) techniques are used to solve these tasks. For cases with high complexity, linear system modelling is often cumbersome. There, other modeling approaches allow for a more compact representation being typically also more accurate as non-linear dependencies can be captured natively. In this work, we focus on the optimization of a power plant start-up sequence, which is part of the network restoration process of a power system after a blackout. Most large power plants cannot start on their own without cranking energy from the outside grid. These are the non-black start (NBS) units. As after a blackout we assume all power plants being shut down, self-contained power plants (black start (BS) units), such as the hydroelectric power plants, start first and boot the NBS units one after each other. Once a NBS unit is restored, it supports the restoration process and because an average NBS unit is much larger than a BS unit, NBS unit's impact on the restoration process is typically dominant. The overall restoration process can take, depending on the size of the blackout region and the damaged components, some hours to weeks. And as the blackout time corresponds directly to economic and life losses, its reduction, even by some minutes, is worthwhile. In this work we compare two popular metaheuristics, the genetic (GA) and simulated annealing (SA) algorithms on start-up sequence optimization and conclude that an efficient restoration plan can be evolved reliably and, depending on the implementation, in a very short period of time allowing for an integration into a real-time transmission system operation tool.}, notes = {Also known as \cite{2754647} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Kheiri:2015:GECCO, author = {Ahmed Kheiri and Ed Keedwell}, title = {A Sequence-based Selection Hyper-heuristic Utilising a Hidden Markov Model}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {417--424}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754766}, doi = {doi:10.1145/2739480.2754766}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Selection hyper-heuristics are optimisation methods that operate at the level above traditional (meta-)heuristics. Their task is to evaluate low level heuristics and determine which of these to apply at a given point in the optimisation process. Traditionally this has been accomplished through the evaluation of individual or paired heuristics. In this work, we propose a hidden Markov model based method to analyse the performance of, and construct, longer sequences of low level heuristics to solve difficult problems. The proposed method is tested on the well known hyper-heuristic benchmark problems within the CHeSC 2011 competition and compared with a large number of algorithms in this domain. The empirical results show that the proposed hyper-heuristic is able to outperform the current best-in-class hyper-heuristic on these problems with minimal parameter tuning and so points the way to a new field of sequence-based selection hyper-heuristics.}, notes = {Also known as \cite{2754766} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Meisel:2015:GECCO, author = {Stephan Meisel and Christian Grimme and Jakob Bossek and Martin Woelck and Guenter Rudolph and Heike Trautmann}, title = {Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {425--432}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754705}, doi = {doi:10.1145/2739480.2754705}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}, notes = {Also known as \cite{2754705} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Nguyen:2015:GECCO, author = {Su Nguyen and Mengjie Zhang and Kay Chen Tan}, title = {A Dispatching rule based Genetic Algorithm for Order Acceptance and Scheduling}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {433--440}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754821}, doi = {doi:10.1145/2739480.2754821}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Order acceptance and scheduling is an interesting and challenging scheduling problem in which two decisions need to be handled simultaneously. While the exact methods are not efficient and sometimes impractical, existing meta-heuristics proposed in the literature still have troubles dealing with large problem instances. In this paper, a dispatching rule based genetic algorithm is proposed to combine the advantages of existing dispatching rules/heuristics, genetic algorithm and local search. The results indicates that the proposed methods are effective and efficient when compared to a number of existing heuristics with a wide range of problem instances.}, notes = {Also known as \cite{2754821} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Nouri:2015:GECCO, author = {Nouha Nouri and Talel Ladhari}, title = {Minimizing Regular Objectives for Blocking Permutation Flow Shop Scheduling: Heuristic Approaches}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {441--448}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754638}, doi = {doi:10.1145/2739480.2754638}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The objective of this work is to present and evaluate meta-heuristics for the blocking permutation flow shop scheduling problem subject to regular objectives. The blocking problem is known to be NP-hard with more than two machines. We assess the difficulty level of this problem by developing two population-based meta-heuristics: Genetic Algorithm and Artificial Bee Colony algorithm. The final goal is to measure the performance of these proposed techniques and potentially contribute in possible improvements in the blocking benchmark instances. Furthermore, computational tests carried out on randomly generated test problems show that the approaches consistently yields good solutions in a moderate amount of time. Finally, an updated list of best-known solutions for the Taillard's and Ronconi and Henriques's benchmark is exposed: new best-known solutions for the blocking flow shop scheduling problem with makespan, total flow time, and total tardiness criteria are found.}, notes = {Also known as \cite{2754638} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ochoa:2015:GECCO, author = {Gabriela Ochoa and Francisco Chicano and Renato Tinos and Darrell Whitley}, title = {Tunnelling Crossover Networks}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {449--456}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754657}, doi = {doi:10.1145/2739480.2754657}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Local optima networks are a recent model of fitness landscapes. They compress the landscape by representing local optima as nodes, and search transitions among them as edges. Previous local optima networks considered transitions based on mutation; this study looks instead at transitions based on deterministic recombination. We define and analyse networks based on the recently proposed partition crossover for k-bounded pseudo-Boolean functions, using NKq landscapes as a case study. Partition crossover was initially proposed for the travelling salesman problem, where it was found to tunnel between local optima, i.e., jump from local optimum to local optimum. Our network analysis shows that this also happens for NK landscapes: local optima are densely connected via partition crossover. We found marked differences between the adjacent and random interaction NK models. Surprisingly, with the random model, instances have a lower number of local optima on average, but their networks are more sparse and decompose into several clusters. There is also large variability in the size and pattern of connectivity of instances coming from the same landscape parameter values. These network features offer new insight informing why some instances are harder to solve than others.}, notes = {Also known as \cite{2754657} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Picek:2015:GECCO, author = {Stjepan Picek and Robert I. McKay and Roberto Santana and Tom D. Gedeon}, title = {Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {457--464}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754739}, doi = {doi:10.1145/2739480.2754739}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We explore the problem space of maximum nonlinearity problems for balanced Boolean functions, examining the symmetry structure and fitness landscapes in the most common (bit string) representation. We present theoretical analyses of well understood aspects, together with detailed enumeration of the 4-bit problem, sampling of the 6-bit problem based on known optima, and sampling of the 8-bit problem based on its fittest known solutions. We show that these problems have many more symmetries than is generally noted, with implications for crossover and for distributional methods. We explore the large-scale plateau structure of the problem, with similar implications for local search. We show that symmetries yield additional information that may yield more effective search methods.}, notes = {Also known as \cite{2754739} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pourhassan:2015:GECCO, author = {Mojgan Pourhassan and Frank Neumann}, title = {On the Impact of Local Search Operators and Variable Neighbourhood Search for the Generalized Travelling Salesperson Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {465--472}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754656}, doi = {doi:10.1145/2739480.2754656}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem where local search approaches have been very successful. We investigate the two hierarchical approaches of Hu and Raidl (2008) for solving this problem from a theoretical perspective. We examine the complementary abilities of the two approaches caused by their neighbourhood structures and the advantage of combining them into variable neighbourhood search. We first point out complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time.}, notes = {Also known as \cite{2754656} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ramos:2015:GECCO, author = {Gabriel de Oliveira Ramos and Ana Lucia Cetertich Bazzan}, title = {Towards the User Equilibrium in Traffic Assignment Using GRASP with Path Relinking}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {473--480}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754755}, doi = {doi:10.1145/2739480.2754755}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Solving the traffic assignment problem (TAP) is an important step towards an efficient usage of the traffic infrastructure. A fundamental assignment model is the so-called User Equilibrium (UE), which may turn into a complex optimisation problem. In this paper, we present the use of the GRASP metaheuristic to approximate the UE of the TAP. A path relinking mechanism is also employed to promote a higher coverage of the search space. Moreover, we propose a novel performance evaluation function, which measures the number of vehicles that have an incentive to deviate from the routes to which they were assigned. Through experiments, we show that our approach outperforms classical algorithms, providing solutions that are, on average, significantly closer to the UE. Furthermore, when compared to classical methods, the fairness achieved by our assignments is considerably better. These results indicate that our approach is efficient and robust, producing reasonably stable assignments.}, notes = {Also known as \cite{2754755} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Raschip:2015:GECCO, author = {Madalina Raschip and Cornelius Croitoru and Kilian Stoffel}, title = {Guiding Evolutionary Search with Association Rules for Solving Weighted CSPs}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {481--488}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754757}, doi = {doi:10.1145/2739480.2754757}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Weighted constraint satisfaction problems are difficult optimization problems that could model applications from various domains. Evolutionary algorithms are not the first option for solving such type of problems. In this work, the evolutionary algorithm uses the information extracted from the previous best solutions to guide the search in the next iterations. After the archive of previous best solutions has been sufficiently (re)filled, a data mining module is called to find association rules between variables and values. The generated rules are used to improve further the search process. Different methods of applying the association rules are investigated. Computational experiments are done on academic and real-world problem instances. The obtained results validate the approach and show that it is competitive with existing approaches in literature.}, notes = {Also known as \cite{2754757} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Segura:2015:GECCO, author = {Carlos Segura and Salvador {Botello Rionda} and Arturo {Hernandez Aguirre} and S. Ivvan {Valdez Pena}}, title = {A Novel Diversity-based Evolutionary Algorithm for the Traveling Salesman Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {489--496}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754802}, doi = {doi:10.1145/2739480.2754802}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Travelling Salesman Problem (TSP) is one of the most well-known NP-hard combinatorial optimization problems. In order to deal with large TSP instances, several heuristics and metaheuristics have been devised. In this paper, a novel memetic scheme that incorporates a new diversity-based replacement strategy is proposed and applied to the largest instances of the TSPLIB benchmark. The novelty of our method is that it combines the idea of transforming a single-objective problem into a multi-objective one, by considering diversity as an explicit objective, with the idea of adapting the balance induced between exploration and exploitation to the various optimization stages. In addition, the intensification capabilities of the individual learning method incorporated in the memetic scheme are also adapted by taking into account the stopping criterion. Computational results show the clear superiority of our scheme when compared against state-of-the-art schemes. To our knowledge, our proposal is the first evolutionary scheme that readily solves an instance with more than 30,000 cities to optimality.}, notes = {Also known as \cite{2754802} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Volke:2015:GECCO, author = {Sebastian Volke and Dirk Zeckzer and Gerik Scheuermann and Martin Middendorf}, title = {A Visual Method for Analysis and Comparison of Search Landscapes}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {497--504}, keywords = {Evolutionary Combinatorial Optimization and Metaheuristics}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754733}, doi = {doi:10.1145/2739480.2754733}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Combinatorial optimization problems and corresponding (meta-)heuristics have received much attention in the literature. Especially, the structural or topological analysis of search landscapes is important for evaluating the applicability and the performance of search operators for a given problem. However, this analysis is often tedious and usually the focus is on one specific problem and only a few operators. We present a visual analysis method that can be applied to a wide variety of problems and search operators. The method is based on steepest descent walks and shortest distances in the search landscape. The visualization shows the search landscape as seen by the search algorithm. It supports the topological analysis as well as the comparison of search landscapes. We showcase the method by applying it to two different search operators on the TSP, the QAP, and the SMTTP. Our results show how differences between search operators manifest in the search landscapes and how conclusions about the suitability of the search operator for different optimizations can be drawn.}, notes = {Also known as \cite{2754733} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ceberio:2015:GECCO, author = {Josu Ceberio and Alexander Mendiburu and Jose A. Lozano}, title = {Kernels of Mallows Models for Solving Permutation-based Problems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {505--512}, keywords = {Estimation of Distribution Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754741}, doi = {doi:10.1145/2739480.2754741}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently, distance-based exponential probability models, such as Mallows and Generalized Mallows, have demonstrated their validity in the context of estimation of distribution algorithms (EDAs) for solving permutation problems. However, despite their successful performance, these models are unimodal, and therefore, they are not flexible enough to accurately model populations with solutions that are very sparse with regard to the distance metric considered under the model. In this paper, we propose using kernels of Mallows models under the Kendall's-tau and Cayley distances within EDAs. In order to demonstrate the validity of this new algorithm, Mallows Kernel EDA, we compare its performance with the classical Mallows and Generalized Mallows EDAs, on a benchmark of 90 instances of two different types of permutation problems: the quadratic assignment problem and the permutation flowshop scheduling problem. Experimental results reveal that, in most cases, Mallows Kernel EDA outperforms the Mallows and Generalized Mallows EDAs under the same distance. Moreover, the new algorithm under the Cayley distance obtains the best results for the two problems in terms of average fitness and computational time.}, notes = {Also known as \cite{2754741} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Dang:2015:GECCO, author = {Duc-Cuong Dang and Per Kristian Lehre}, title = {Simplified Runtime Analysis of Estimation of Distribution Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {513--518}, keywords = {Estimation of Distribution Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754814}, doi = {doi:10.1145/2739480.2754814}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of distribution algorithms (EDA) are stochastic search methods that look for optimal solutions by learning and sampling from probabilistic models. Despite their popularity, there are only few rigorous theoretical analyses of their performance. Even for the simplest EDAs, such as the Univariate Marginal Distribution Algorithm (UMDA) which assumes independence between decision variables, there are only a handful of results about its runtime, and results for simple functions such as Onemax are still missing. In this paper, we show that the recently developed level-based theorem for non-elitist populations is directly applicable to runtime analysis of EDAs. To demonstrate this approach, we derive easily upper bounds on the expected runtime of the UMDA.}, notes = {Also known as \cite{2754814} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Hsu:2015:GECCO, author = {Shih-Huan Hsu and Tian-Li Yu}, title = {Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {519--526}, keywords = {Estimation of Distribution Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754737}, doi = {doi:10.1145/2739480.2754737}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve optimization problems via exploiting problem substructures. The proposed algorithm adopts pairwise linkage detection and stores the information in the form of dependency structure matrix (DSM). A new linkage model, called the incremental linkage set, is then constructed by using the DSM. Inspired by the idea of optimal mixing, the restricted mixing and the back mixing are proposed. The former aims at efficient exploration under certain constrains. The latter aims at exploitation by refining the DSM so as to reduce unnecessary evaluations. Experimental results show that DSMGA-II outperforms LT-GOMEA and hBOA in terms of number of function evaluations on the concatenated/folded/cyclic trap problems, NK-landscape problems with various degrees of overlapping, 2D Ising spin-glass problems, and MAX-SAT. The investigation of performance comparison with P3 is also included.}, notes = {Also known as \cite{2754737} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Segovia-Dominguez:2015:GECCO, author = {Ignacio Segovia-Dominguez and Arturo Hernandez-Aguirre}, title = {An Estimation of Distribution Algorithm based on the Natural Gradient and the Boltzmann Distribution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {527--534}, keywords = {Estimation of Distribution Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754803}, doi = {doi:10.1145/2739480.2754803}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces an Estimation of Distribution Algorithm (EDA), in which the parameters of the search distribution are updated by the natural gradient technique. The parameter updating is guided via the Kullback-Leibler divergence between the multivariate Normal and the Boltzmann densities. This approach makes sense because it is well-known that the Boltzmann function yields a reliable model to simulate particles near to optimum locations. Three main contributions are presented here in order to build an effective EDA. The first one is a natural gradient formula which allows for an update of the parameters of a density function. These equations are related to an exponential parametrization of the search distribution. The second contribution involves the approximation of the developed gradient formula and its connection to the importance sampling method. The third contribution is a parameter update rule which is designed to control the exploration and exploitation phases of the algorithm. The proposed EDA is tested on a benchmark of 16 problems and compared versus the XNES and iAMaLGaM algorithms. The statistical results show that the performance of the proposed method is competitive and it is the winner in several problems.}, notes = {Also known as \cite{2754803} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Tung:2015:GECCO, author = {Yu-Fan Tung and Tian-Li Yu}, title = {Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {535--542}, keywords = {Estimation of Distribution Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754685}, doi = {doi:10.1145/2739480.2754685}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much attention for their robustness, small size of required population, and efficiency in terms of number of function evaluations (NFE). In this paper, the performances and behaviours of convergence in OMEAs are studied by investigating the mechanism of optimal mixing (OM), the variation operator in OMEAs, under two scenarios---one-layer and two-layer masks. For the case of one-layer masks, the required population size is derived from the viewpoint of initial supply, while the convergence time is derived by analysing the progress of sub-solution growth. NFE is then asymptotically bounded with rational probability by estimating the probability of performing evaluations. For the case of two-layer masks, empirical results indicate that the required population size is proportional to both the degree of cross competition and the results from the one-layer-mask case. The derived models also indicate that population sizing is decided by initial supply when disjoint masks are adopted, that the high selection pressure imposed by OM makes the composition of sub-problems impact little on NFE, and that the population size requirement for two-layer masks increases with the reverse-growth probability.}, notes = {Also known as \cite{2754685} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Alonso-Barba:2015:GECCO, author = {Juan I. Alonso-Barba and Luis {de la Ossa} and Olivier Regnier-Coudert and John McCall and Jose A. Gamez and Jose M. Puerta}, title = {Ant Colony and Surrogate Tree-Structured Models for Orderings-Based Bayesian Network Learning}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {543--550}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754806}, doi = {doi:10.1145/2739480.2754806}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Structural learning of Bayesian networks is a very expensive task even when sacrificing the optimality of the result. Because of that, there are some proposals aimed at obtaining relative-quality solutions in short times. One of them, namely Chain-ACO, searches an ordering among all variables with Ant Colony Optimization and a chain-structured surrogate model, and then uses this ordering to build a Bayesian network by means of the well-known K2 algorithm. This work is based on Chain-ACO. We evaluate the impact of using a tree-structured surrogate model instead of a chain to evaluate orderings during the search. Moreover, we propose a variation of the way K2 builds the network, which consists of allowing some changes in the positions of the variables whenever they such changes not produce a cycle. This modification of the process may improve the score of the final network, without (almost) any additional cost.}, notes = {Also known as \cite{2754806} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Cussat-Blanc:2015:GECCO, author = {Sylvain Cussat-Blanc and Kyle Harrington}, title = {Genetically-regulated Neuromodulation Facilitates Multi-Task Reinforcement Learning}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {551--558}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754730}, doi = {doi:10.1145/2739480.2754730}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we use a gene regulatory network (GRN) to regulate a reinforcement learning controller, the State-Action-Reward-State-Action (SARSA) algorithm. The GRN serves as a neuromodulator of SARSA's learning parameters: learning rate, discount factor, and memory depth. We have optimized GRNs with an evolutionary algorithm to regulate these parameters on specific problems but with no knowledge of problem structure. We show that genetically-regulated neuromodulation (GRNM) performs comparably or better than SARSA with fixed parameters. We then extend the GRNM SARSA algorithm to multi-task problem generalization, and show that GRNs optimized on multiple problem domains can generalize to previously unknown problems with no further optimization.}, notes = {Also known as \cite{2754730} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Gonccalves:2015:GECCO, author = {Eduardo C. Goncalves and Alexandre Plastino and Alex A. Freitas}, title = {Simpler is Better: a Novel Genetic Algorithm to Induce Compact Multi-label Chain Classifiers}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {559--566}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754650}, doi = {doi:10.1145/2739480.2754650}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC). This approach consists in training q binary classifiers linked in a chain, y1 rightarrow y2 rightarrow ... rightarrow yq, with each responsible for classifying a specific label in {l1, l2, ..., lq}. The chaining mechanism allows each individual classifier to incorporate the predictions of the previous ones as additional information at classification time. Thus, possible correlations among labels can be automatically exploited. Nevertheless, CC suffers from two important drawbacks: (i) the label ordering is decided at random, although it usually has a strong effect on predictive accuracy; (ii) all labels are inserted into the chain, although some of them might carry irrelevant information to discriminate the others. In this paper we tackle both problems at once, by proposing a novel genetic algorithm capable of searching for a single optimized label ordering, while at the same time taking into consideration the use of partial chains. Experiments on benchmark datasets demonstrate that our approach is able to produce models that are both simpler and more accurate.}, notes = {Also known as \cite{2754650} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Jaskowski:2015:GECCO, author = {Wojciech Jaskowski and Marcin Szubert and Pawe\l Liskowski and Krzysztof Krawiec}, title = {High-Dimensional Function Approximation for Knowledge-Free Reinforcement Learning: a Case Study in SZ-Tetris}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {567--573}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754783}, doi = {doi:10.1145/2739480.2754783}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {SZ-Tetris, a restricted version of Tetris, is a difficult reinforcement learning task. Previous research showed that, similarly to the original Tetris, value function-based methods such as temporal difference learning, do not work well for SZ-Tetris. The best performance in this game was achieved by employing direct policy search techniques, in particular the cross-entropy method in combination with handcrafted features. Nonetheless, a simple heuristic hand-coded player scores even higher. Here we show that it is possible to equal its performance with CMA-ES (Covariance Matrix Adaptation Evolution Strategy). We demonstrate that further improvement is possible by employing systematic n-tuple network, a knowledge-free function approximator, and VD-CMA-ES, a linear variant of CMA-ES for high dimension optimization. Last but not least, we show that a large systematic n-tuple network (involving more than 4 million parameters) allows the classical temporal difference learning algorithm to obtain similar average performance to VD-CMA-ES, but at 20 times lower computational expense, leading to the best policy for SZ-Tetris known to date. These results enrich the current understanding of difficulty of SZ-Tetris, and shed new light on the capabilities of particular search paradigms when applied to representations of various characteristics and dimensionality.}, notes = {Also known as \cite{2754783} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Peignier:2015:GECCO, author = {Sergio Peignier and Christophe Rigotti and Guillaume Beslon}, title = {Subspace Clustering Using Evolvable Genome Structure}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {575--582}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754709}, doi = {doi:10.1145/2739480.2754709}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present an evolutionary algorithm to tackle the subspace clustering problem. Subspace clustering is recognized as more difficult than standard clustering since it requires to identify not only the clusters but also the various subspaces where the clusters hold. We propose to tackle this problem with a bio-inspired algorithm that includes many bio-like features like variable genome length and organization, functional and non-functional elements, and variation operators including chromosomal rearrangements. These features give the algorithm a large degree of freedom to achieve subspace clustering with satisfying results on a reference benchmark with respect to state of the art methods. One of the main advantages of the approach is that it needs only one subspace clustering ad-hoc parameter: the maximal number of clusters. This is a single and intuitive parameter that sets the maximal level of details of the clustering, while other algorithms require more complicated parameter space exploration. The other parameters of the algorithm are related to the evolution strategy (population size, mutation rate, ...) and for them we use a single setting that turns out to be effective on all the datasets of the benchmark.}, notes = {Also known as \cite{2754709} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Tran:2015:GECCO, author = {Cao Truong Tran and Mengjie Zhang and Peter Andreae}, title = {Multiple Imputation for Missing Data Using Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {583--590}, keywords = {genetic algorithms, genetic programming, Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754665}, doi = {doi:10.1145/2739480.2754665}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Missing values are a common problem in many real world databases. Inadequate handing of missing data can lead to serious problems in data analysis. A common way to cope with this problem is to use imputation methods to fill missing values with plausible values. This paper proposes GPMI, a multiple imputation method that uses genetic programming as a regression method to estimate missing values. Experiments on eight datasets with six levels of missing values compare GPMI with seven other popular and advanced imputation methods on two measures: the prediction accuracy and the classification accuracy. The results show that, in most cases, GPMI not only achieves better prediction accuracy, but also better classification accuracy than the other imputation methods.}, notes = {Also known as \cite{2754665} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Urbanowicz:2015:GECCO, author = {Ryan Urbanowicz and Jason Moore}, title = {Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {591--598}, keywords = {Evolutionary Machine Learning}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754756}, doi = {doi:10.1145/2739480.2754756}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An accuracy-based rule fitness is a hallmark of most modern Michigan-style learning classifier systems (LCS), a powerful, flexible, and largely interpretable class of machine learners. However, rule-fitness based solely on accuracy is not ideal for identifying optimal rules in supervised learning. This is particularly true for noisy problem domains where perfect rule accuracy essentially guarantees over-fitting. Rule fitness based on accuracy alone is unreliable for reflecting the global value of a given rule since rule accuracy is based on a subset of the training instances. While moderate over-fitting may not dramatically hinder LCS classification or prediction performance, the interpretability of the solution is likely to suffer. Additionally, over-fitting can impede algorithm learning efficiency and leads to a larger number of rules being required to capture relationships. The present study seeks to develop an intuitive multi-objective fitness function that will encourage the discovery, preservation, and identification of optimal rules through accuracy, correct coverage of training data, and the prior probability of the specified attribute states and class expressed by a given rule. We demonstrate the advantages of our proposed fitness by implementing it into the ExSTraCS algorithm and performing evaluations over a large spectrum of complex, noisy, simulated datasets.}, notes = {Also known as \cite{2754756} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Alves:2015:GECCO, author = {Maria Joao Alves and Carlos Henggeler Antunes and Pedro Carrasqueira}, title = {A PSO Approach to Semivectorial Bilevel Programming: Pessimistic, Optimistic and Deceiving Solutions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {599--606}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754644}, doi = {doi:10.1145/2739480.2754644}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In a bilevel programming problem, the upper level decision maker (leader) decides first, but he must incorporate the reaction of the lower level decision maker (follower) in his decision. The existence of multiple objectives at the lower level gives rise to a set of lower level efficient solutions for each leader's decision, which poses additional difficulties for the leader to anticipate the follower's reaction. The optimistic approach assumes that the follower accepts any efficient solution, while the pessimistic approach considers that the leader prepares for the worst case. In this work, we first discuss the assumptions and implications of optimistic vs. pessimistic approaches in bilevel problems with multiple objective functions at the lower level (semivectorial bilevel problems) or at both levels. Three types of solutions are analyzed: the optimistic, pessimistic and deceiving solutions, the latter being a new solution concept introduced herein, which represents the worst outcome of a failed optimistic approach (i.e., when the leader believes that the follower will pursue his own interests but the follower does not react accordingly). Then we propose a particle swarm optimization algorithm to semivectorial bilevel problems, which aims to approximate these three types of solutions in a single run. Some experimental results of the algorithm are presented.}, notes = {Also known as \cite{2754644} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Asafuddoula:2015:GECCO, author = {Md Asafuddoula and Tapabrata Ray and Hemant Kumar Singh}, title = {Characterizing Pareto Front Approximations in Many-objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {607--614}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754701}, doi = {doi:10.1145/2739480.2754701}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A Pareto Optimal Front (POF) provides the set of optimal trade-off solutions formulti-objective optimization problems. The characteristics of the POF, e.g. continuity, convexity, spread and uniformity are important in the context of decision making and performance assessment. Most of the existing metrics (hypervolume, inverted generational distance, coverage etc.) were originally designed for two or three objective optimization problems with an aim of assessing the quality of non-dominated solutions delivered by various algorithms. The metrics provide little information about the nature of the front and some of them (e.g. hypervolume) are computationally expensive for problems involving large number of objectives. For problems with more than three objectives, existing tools for visualization such as parallel plots, spider/radar plots and scatter plots also offer limited useful information in terms of the nature of the front. In this paper, we introduce an alternative scalar measure of diversity that is suitable for characterizing POF approximations of optimization problems with high number of objectives. The diversity is measured against a Reference Pareto Front, a set of points uniformly spread on the hyperplane with unit intercepts. We also illustrate that the computation of such a metric is a natural extension of decomposition based evolutionary algorithms which attempt to align solutions with the reference directions constructed using the Ideal point and uniformly distributed points on the hyperplane. In particular, the perpendicular distances between the uniformly distributed reference directions and their closest solutions in the nondominated front provides information about the diversity, while the shortest distance of every solution from the hyperplane provides information about the convexity of the POF. The proposed metrics are illustrated using a number of test problems involving up to fifteen objectives.}, notes = {Also known as \cite{2754701} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Azzouz:2015:GECCO, author = {Radhia Azzouz and Slim Bechikh and Lamjed {Ben Said}}, title = {Multi-objective Optimization with Dynamic Constraints and Objectives: New Challenges for Evolutionary Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {615--622}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754708}, doi = {doi:10.1145/2739480.2754708}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dynamic Multi-objective Optimization (DMO) is a challenging research topic since the objective functions, constraints, and problem parameters may change over time. Several evolutionary algorithms have been proposed to deal with DMO problems. Nevertheless, they were restricted to unconstrained or domain constrained problems. In this work, we focus on the dynamicty of problem constraints along with time-varying objective functions. As this is a very recent research area, we have observed a lack of benchmarks that simultaneously take into account these characteristics. To fill this gap, we propose a set of test problems that extend a suite of static constrained multi-objective problems. Moreover, we propose a new version of the Dynamic Non dominated Sorting Genetic Algorithm II to deal with dynamic constraints by replacing the used constraint-handling mechanism by a more elaborated and self-adaptive penalty function. Empirical results show that our proposal is able to: (1) handle dynamic environments and track the changing Pareto front and (2) handle infeasible solutions in an effective and efficient manner which allows avoiding premature convergence. Moreover, the statistical analysis of the obtained results emphasize the advantages of our proposal over the original algorithm on both aspects of convergence and diversity on most test problems.}, notes = {Also known as \cite{2754708} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Branke:2015:GECCO, author = {Juergen Branke and Ke Lu}, title = {Finding the Trade-off between Robustness and Worst-case Quality}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {623--630}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754711}, doi = {doi:10.1145/2739480.2754711}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many real-world problems are subject to uncertainty, and often solutions should not only be good, but also robust against environmental disturbances or deviations from the decision variables. While most papers dealing with robustness aim at finding solutions with a high expected performance given a distribution of the uncertainty, we examine the trade-off between the allowed deviations from the decision variables (tolerance level), and the worst case performance given the allowed deviations. A possible application are manufacturing tolerances, where an engineer can specify an allowed tolerance for manufacturing, but a low tolerance requirement incurs substantially higher manufacturing cost, whereas a high tolerance requirement usually means having to accept a lower worst-case quality of the solution. More specifically, in this paper, we suggest two multi-objective evolutionary algorithms to compute the available trade offs between allowed tolerance level and worst-case quality of the solutions. Both algorithms are 2-level nested algorithms. While the first algorithm is point-based in the sense that the lower level computes a point of worst case for each upper level solution, the second algorithm is envelope-based, in the sense that the lower level computes a whole trade-off curve between worst-case oftness and tolerance level for each upper level solution.}, notes = {Also known as \cite{2754711} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Braun:2015:GECCO, author = {Marlon Alexander Braun and Pradyumn Kumar Shukla and Hartmut Schmeck}, title = {Obtaining Optimal Pareto Front Approximations using Scalarized Preference Information}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {631--638}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754674}, doi = {doi:10.1145/2739480.2754674}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Scalarization techniques are a popular method for articulating preferences in solving multi-objective optimization problems. These techniques, however, have so far proven to be ill-suited in finding a preference-driven approximation that still captures the Pareto front in its entirety. Therefore, we propose a new concept that defines an optimal distribution of points on the front given a specific scalarization function. It is proven that such an approximation exists for every real-valued problem irrespective of the shape of the corresponding front under some very mild conditions. We also show that our approach works well in obtaining an equidistant approximation of the Pareto front if no specific preference is articulated. Our analysis is complemented by the presentation of a new algorithm that implements the aforementioned concept. We provide in-depth simulation results to demonstrate the performance of our algorithm. The analysis also reveals that our algorithm is able to outperform current state-of-the-art algorithms on many popular benchmark problems.}, notes = {Also known as \cite{2754674} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Brockhoff:2015:GECCO, author = {Dimo Brockhoff and Thanh-Do Tran and Nikolaus Hansen}, title = {Benchmarking Numerical Multiobjective Optimizers Revisited}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {639--646}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754777}, doi = {doi:10.1145/2739480.2754777}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Algorithm benchmarking plays a vital role in designing new optimization algorithms and in recommending efficient and robust algorithms for practical purposes. So far, two main approaches have been used to compare algorithms in the evolutionary multiobjective optimization (EMO) field: (i) displaying empirical attainment functions and (ii) reporting statistics on quality indicator values. Most of the time, EMO benchmarking studies compare algorithms for fixed and often arbitrary budgets of function evaluations although the algorithms are any-time optimizers. Instead, we propose to transfer and adapt standard benchmarking techniques from the single-objective optimization and classical derivative-free optimization community to the field of EMO. Reporting \emph{target-based runlengths} allows to compare algorithms with varying numbers of function evaluations quantitatively. Displaying data profiles can aggregate performance information over different test functions, problem difficulties, and quality indicators. We apply this approach to compare three common algorithms on a new test function suite derived from the well-known single-objective BBOB functions. The focus thereby lies less on gaining insights into the algorithms but more on showcasing the concepts and on what can be gained over current benchmarking approaches.}, notes = {Also known as \cite{2754777} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Buzdalov:2015:GECCO, author = {Maxim Buzdalov and Ilya Yakupov and Andrey Stankevich}, title = {Fast Implementation of the Steady-State NSGA-II Algorithm for Two Dimensions Based on Incremental Non-Dominated Sorting}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {647--654}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754728}, doi = {doi:10.1145/2739480.2754728}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic algorithms (GAs) are widely used in multi-objective optimization for solving complex problems. There are two distinct approaches for GA design: generational and steady-state algorithms. Most of the current state-of-the-art GAs are generational, although there is an increasing interest to steady-state algorithms as well. However, for algorithms based on non-dominated sorting, most of steady-state implementations have higher computation complexity than their generational counterparts, which limits their applicability. We present a fast implementation of a steady-state version of the NSGA-II algorithm for two dimensions. This implementation is based on a data structure which has O(N) complexity for single solution insertion and deletion in the worst case. The experimental results show that our implementation works noticeably faster than steady-state NSGA-II implementations which use fast non-dominated sorting.}, notes = {Also known as \cite{2754728} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Byers:2015:GECCO, author = {Chad Michael Byers and Betty H.C. Cheng}, title = {An Approach to Mitigating Unwanted Interactions between Search Operators in Multi-Objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {655--662}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754698}, doi = {doi:10.1145/2739480.2754698}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {At run time, software systems often face a myriad of adverse environmental conditions and system failures that cannot be anticipated during the system's initial design phase. These uncertainties drive the need for dynamically adaptive systems that are capable of providing self-* properties (e.g., self-monitoring, self-adaptive, self-healing, etc.). Prescriptive techniques to manually preload these systems with a limited set of configurations often result in brittle, rigid designs that are unable to cope with environmental uncertainty. An alternative approach is to embed a search technique capable of exploring and generating optimal reconfigurations at run time. Increasingly, DAS applications are defined by multiple competing objectives (e.g., cost vs. performance) in which a set of valid solutions with a range of trade-offs are to be considered rather than a single optimal solution. While leveraging a multi-objective optimization technique, NSGA-II, to manage these competing objectives, hidden interactions were observed between search operators that prevented fair competition among solutions and restricted search from regions where valid optimal configurations existed. In this follow-on work, we demonstrate the role that niching can play in mitigating these unwanted interactions by explicitly creating favorable regions within the objective space where optimal solutions can equally compete and co-exist.}, notes = {Also known as \cite{2754698} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Denysiuk:2015:GECCO, author = {Roman Denysiuk and Lino Costa and Isabel {Espirito Santo}}, title = {MOEA/VAN: Multiobjective Evolutionary Algorithm Based on Vector Angle Neighborhood}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {663--670}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754722}, doi = {doi:10.1145/2739480.2754722}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.}, notes = {Also known as \cite{2754722} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Guerreiro:2015:GECCO, author = {Andreia P. Guerreiro and Carlos M. Fonseca and Luis Paquete}, title = {Greedy Hypervolume Subset Selection in the Three-Objective Case}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {671--678}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754812}, doi = {doi:10.1145/2739480.2754812}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Given a non-dominated point set X subset of R^d of size n and a suitable reference point r elementof Rd, the Hypervolume Subset Selection Problem (HSSP) consists of finding a subset of size k > n that maximizes the hypervolume indicator. It arises in connection with multiobjective selection and archiving strategies, as well as Pareto-front approximation post-processing for visualization and/or interaction with a decision maker. Efficient algorithms to solve the HSSP are available only for the 2-dimensional case, achieving a time complexity of O(n(k+log n)). In contrast, the best upper bound available for d>2 is O(nd/2 log n + nn-k). Since the hypervolume indicator is a monotone submodular function, the HSSP can be approximated to a factor of (1-1/e) using a greedy strategy. Such a greedy algorithm for the 3-dimensional HSSP is proposed in this paper. The time complexity of the algorithm is shown to be O(n2), which considerably improves upon recent complexity results for this approximation problem.}, notes = {Also known as \cite{2754812} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{HernandezGomez:2015:GECCO, author = {Raquel {Hernandez Gomez} and Carlos A. {Coello Coello}}, title = {Improved Metaheuristic Based on the R2 Indicator for Many-Objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {679--686}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754776}, doi = {doi:10.1145/2739480.2754776}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In recent years, performance indicators were introduced as a selection mechanism in multi-objective evolutionary algorithms (MOEAs). A very attractive option is the R2 indicator due to its low computational cost and weak-Pareto compatibility. This indicator requires a set of utility functions, which map each objective to a single value. However, not all the utility functions available in the literature scale properly for more than four objectives and the diversity of the approximation sets is sensitive to the choice of the reference points during normalization. In this paper, we present an improved version of a MOEA based on the $R2$ indicator, which takes into account these two key aspects, using the achievement scalarizing function and statistical information about the population's proximity to the true Pareto optimal front. Moreover, we present a comparative study with respect to some other emerging approaches, such as NSGA-III (based on Pareto dominance), DELTAp-DDE (based on the DELTAp indicator) and some other MOEAs based on the R2 indicator, using the DTLZ and WFG test problems. Experimental results indicate that our approach outperforms the original algorithm as well as the other MOEAs in the majority of the test instances, making it a suitable alternative for solving many-objective optimization problems.}, notes = {Also known as \cite{2754776} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Hrbacek:2015:GECCO, author = {Radek Hrbacek}, title = {Parallel Multi-Objective Evolutionary Design of Approximate Circuits}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {687--694}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming, Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754785}, doi = {doi:10.1145/2739480.2754785}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multi-objective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multi-objective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.}, notes = {Also known as \cite{2754785} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ishibuchi:2015:GECCO, author = {Hisao Ishibuchi and Hiroyuki Masuda and Yusuke Nojima}, title = {A Study on Performance Evaluation Ability of a Modified Inverted Generational Distance Indicator}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {695--702}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754792}, doi = {doi:10.1145/2739480.2754792}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The inverted generational distance (IGD) has been frequently used as a performance indicator for many-objective problems where the use of the hypervolume is difficult. However, since IGD is not Pareto compliant, it is possible that misleading Pareto incompliant results are obtained. Recently, a simple modification of IGD was proposed by taking into account the Pareto dominance relation between a solution and a reference point when their distance is calculated. It was also shown that the modified indicator called IGD+ is weakly Pareto compliant. However, actual effects of the modification on performance comparison have not been examined. Moreover, IGD+ has not been compared with other distance-based weakly Pareto compliant indicators such as the additive epsilon indicator and the D1 indicator (i.e., IGD with the weighted achievement scalarizing function). In this paper, we examine the effect of the modification by comparing IGD+ with IGD for multiobjective and many-objective problems. In computational experiments, we generate a large number of ordered pairs of non-dominated solution sets where one is better than the other. Two solution sets in each pair are compared by the above-mentioned performance indicators. We examine whether each indicator can correctly say which solution set is better between them.}, notes = {Also known as \cite{2754792} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Li:2015:GECCO, author = {Miqing Li and Shengxiang Yang and Xiaohui Liu}, title = {A Performance Comparison Indicator for Pareto Front Approximations in Many-Objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {703--710}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754687}, doi = {doi:10.1145/2739480.2754687}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Increasing interest in simultaneously optimizing many objectives (typically more than three objectives) of problems leads to the emergence of various many-objective algorithms in the evolutionary multi-objective optimization field. However, in contrast to the development of algorithm design, how to assess many-objective algorithms has received scant concern. Many performance indicators are designed in principle for any number of objectives, but in practice are invalid or infeasible to be used in many-objective optimization. In this paper, we explain the difficulties that popular performance indicators face and propose a performance comparison indicator (PCI) to assess Pareto front approximations obtained by many-objective algorithms. PCI evaluates the quality of approximation sets with the aid of a reference set constructed by themselves. The points in the reference set are divided into many clusters, and the proposed indicator estimates the minimum moves of solutions in the approximation sets to weakly dominate these clusters. PCI has been verified both by an analytic comparison with several well-known indicators and by an empirical test on four groups of Pareto front approximations with different numbers of objectives and problem characteristics.}, notes = {Also known as \cite{2754687} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Mahbub:2015:GECCO, author = {Md Shahriar Mahbub and Tobias Wagner and Luigi Crema}, title = {Improving Robustness of Stopping Multi-objective Evolutionary Algorithms by Simultaneously Monitoring Objective and Decision Space}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {711--718}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754680}, doi = {doi:10.1145/2739480.2754680}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Appropriate stopping criteria for multi-objective evolutionary algorithms (MOEA) are an important research topic due to the computational cost of function evaluations, particularly on real-world problems. Most common stopping criteria are based on a fixed budget of function evaluations or the monitoring of the objective space. In this work, we propose a stopping criterion based on monitoring both the objective and decision space of a problem. Average Hausdorff distance (AHD) and genetic diversity are used, respectively. Two-sided t-tests on the slope coefficients after regression analyses are used to detect the stagnation of the AHD and the genetic diversity. The approach is implemented for two widely used MOEAs: NSGA-II and SPEA2. It is compared to a fixed budget, the online convergence detection approach, and the individual monitoring of each space on four bi-objective and two three-objective benchmark problems. Our experimental results reveal that the combined approach achieved significantly better results than the approaches considering only one of the spaces. In particular, we find that the combined consideration runs longer and hence more robustly ensures a well-approximated Pareto front. Nevertheless, on average 29percent and 17percent function evaluations are saved for NSGA-II and SPEA2, respectively, compared to standard budget recommendations.}, notes = {Also known as \cite{2754680} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{PalaciosAlonso:2015:GECCO, author = {Juan Jose {Palacios Alonso} and Bilel Derbel}, title = {On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {719--726}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754774}, doi = {doi:10.1145/2739480.2754774}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MOEA/D is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this paper, we consider applying MOEA/D to solve a bi-objective scheduling combinatorial problem in which task durations and due-dates are uncertain. Surprisingly, we find that the conventional MOEA/D implementation provides poor performance in our application setting. We show that this is because the replacement strategy underlying MOEA/D is suffering some shortcomes that lead to low population diversity, and thus to premature convergence. Consequently, we investigate existing variants of MOEA/D and we propose a novel and simple alternative replacement component at the aim of maintaining population diversity. Through extensive experiments, we then provide a comprehensive analysis on the relative performance and the behavior of the considered algorithms. Besides being able to outperform existing MOEA/D variants, as well as the standard NSGA-II algorithm, our investigations provide new insights into the search ability of MOEA/D and highlight new research opportunities for improving its design components.}, notes = {Also known as \cite{2754774} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pilat:2015:GECCO, author = {Martin Pilat and Roman Neruda}, title = {Incorporating User Preferences in MOEA/D through the Coevolution of Weights}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {727--734}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754801}, doi = {doi:10.1145/2739480.2754801}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The resulting set of solutions obtained by MOEA/D depends on the weights used in the decomposition. In this work, we use this feature to incorporate user preferences into the search. We use co-evolutionary approach to change the weights adaptively during the run of the algorithm. After the user specifies their preferences by assigning binary preference values to the individuals, the co-evolutionary step improves the distribution of weights by creating new (offspring) weights and selecting those that better match the user preferences. The algorithm is tested on a set of benchmark functions with a set of different user preferences.}, notes = {Also known as \cite{2754801} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pinheiro:2015:GECCO, author = {Rodrigo L. Pinheiro and Dario Landa-Silva and Jason Atkin}, title = {Analysis of Objectives Relationships in Multiobjective Problems Using Trade-Off Region Maps}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {735--742}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754721}, doi = {doi:10.1145/2739480.2754721}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Understanding the relationships between objectives in many-objective optimisation problems is desirable in order to develop more effective algorithms. We propose a technique for the analysis and visualisation of complex relationships between many (three or more) objectives. This technique looks at conflicting, harmonious and independent objectives relationships from different perspectives. To do that, it uses correlation, trade-off regions maps and scatter-plots in a four step approach. We apply the proposed technique to a set of instances of the well-known multiobjective multidimensional knapsack problem. The experimental results show that with the proposed technique we can identify local and complex relationships between objectives, trade-offs not derived from pairwise relationships, gaps in the fitness landscape, and regions of interest. Such information can be used to tailor the development of algorithms.}, notes = {Also known as \cite{2754721} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Qiu:2015:GECCO, author = {Xin Qiu and Weinan Xu and Jian-Xin Xu and Kay Chen Tan}, title = {A New Framework for Self-adapting Control Parameters in Multi-objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {743--750}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754714}, doi = {doi:10.1145/2739480.2754714}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Proper tuning of control parameters is critical to the performance of a multi-objective evolutionary algorithm (MOEA). However, the developments of tuning methods for multi-objective optimization are insufficient compared to single-objective optimization. To circumvent this issue, this paper proposes a novel framework that can self-adapt the parameter values from an objective-based perspective. Optimal parametric setups for each objective will be efficiently estimated by combining single-objective tuning methods with a grouping mechanism. Subsequently, the position information of individuals in objective space is used to achieve a more efficient adaptation among multiple objectives. The new framework is implemented into two classical Differential-Evolution-based MOEAs to help to adapt the scaling factor F in an objective-wise manner. Three state-of-the-art single-objective tuning methods are applied respectively to validate the robustness of the proposed mechanisms. Experimental results demonstrate that the new framework is effective and robust in solving multi-objective optimization problems.}, notes = {Also known as \cite{2754714} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Shirinzadeh:2015:GECCO, author = {Saeideh Shirinzadeh and Mathias Soeken and Rolf Drechsler}, title = {Multi-Objective BDD Optimization with Evolutionary Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {751--758}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754718}, doi = {doi:10.1145/2739480.2754718}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Binary Decision Diagrams (BDDs) are widely used in electronic design automation and formal verification. BDDs are a canonical representation of Boolean functions with respect to a variable ordering. Finding a variable ordering resulting in a small number of nodes and paths is a primary goal in BDD optimization. There are several approaches minimizing the number of nodes or paths in BDDs, but yet no method has been proposed to minimize both objectives at the same time. In this paper, BDD optimization is carried out as a bi-objective problem using two aforementioned criteria. For this purpose, we have exploited NSGA-II which has been proven to fit problems with a small number of objectives. Furthermore, the algorithm is facilitated with an objective priority scheme that allows to incorporate preference to one of the objectives. Experimental results show that our multi-objective BDD optimization algorithm has achieved a good trade-off between the number of nodes and the number of paths. Comparison of the results obtained by applying priority to the number of nodes or paths with node and path minimization techniques demonstrates that the proposed algorithm can find the minimum of the preferred objective in most cases as well as lowering the other objective simultaneously.}, notes = {Also known as \cite{2754718} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Xiao:2015:GECCO, author = {Jing Xiao and Mei-Ling Gao and Min-Mei Huang}, title = {Empirical Study of Multi-objective Ant Colony Optimization to Software Project Scheduling Problems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {759--766}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754702}, doi = {doi:10.1145/2739480.2754702}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Software Project Scheduling Problem (SPSP) focuses on the management of software engineers and tasks in a software project so as to complete the tasks with a minimal cost and duration. It's becoming more and more important and challenging with the rapid development of software industry. In this paper, we employ a Multi-objective Evolutionary Algorithm using Decomposition and Ant Colony (MOEA/D-ACO) to solve the SPSP. To the best of our knowledge, it is the first application of Multi-objective Ant Colony Optimization (MOACO) to SPSP. Two heuristics capable of guiding the algorithm to search better in the SPSP model are examined. Experiments are conducted on a set of 36 publicly available instances. The results are compared with the implementation of another multi-objective evolutionary algorithm called NSGA-II for SPSP. MOEA/D-ACO does not outperform NSGA-II for most of complex instances in terms of Pareto Front. But MOEA/D-ACO can obtain solutions with much less time for all instances in our experiments and it outperforms NSGA-II with less duration for most of test instances. The performance may be improved with tuning of the algorithm such as incorporating more heuristic information or using other MOACO algorithms, which deserve further investigation.}, notes = {Also known as \cite{2754702} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Yan:2015:GECCO, author = {Yiming Yan and Ioannis Giagkiozis and Peter J. Fleming}, title = {Improved Sampling of Decision Space for Pareto Estimation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {767--774}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754713}, doi = {doi:10.1145/2739480.2754713}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Pareto Estimation (PE) is a novel method for increasing the density of Pareto optimal solutions across the entire Pareto Front or in a specific region of interest. PE identifies the inverse mapping of Pareto optimal solutions, namely, from objective space to decision space. This identification can be performed using a number of modeling techniques, however, for the sake of simplicity in this work we use a radial basis neural network. In any modeling method, the quality of the resulting model depends heavily on the training samples used. The original version of PE uses the resulting set of Pareto optimal solutions from any multi-objective optimization algorithm and then uses this set to identify the aforementioned mapping. However, we argue that this selection may not always be the best possible and propose an alternative scheme to improve the resulting set of Pareto optimal solutions in order to produce higher quality samples for the identification scheme in PE. The proposed approach is integrated with MAEA-gD, and the resulting solutions are used with PE. The results show that the proposed method shows promise, in that there is measurable improvement in the quality of the estimated PE in terms of the coverage and density.}, notes = {Also known as \cite{2754713} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Yuan:2015:GECCO, author = {Yuan Yuan and Hua Xu and Bo Wang}, title = {An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {775--782}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754655}, doi = {doi:10.1145/2739480.2754655}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in many-objective optimization. However, most of existing research within this area focused on improving the environmental selection procedure, and little work has been done on the effect of variation operators. In this paper, we conduct an experimental investigation of variation operators in a typical reference-point based MOEA, i.e., NSGA-III. First, we provide a new NSGA-III variant, i.e., NSGA-III-DE, which introduces differential evolution (DE) operator into NSGA-III, and we further examine the effect of two main control parameters in NSGA-III-DE. Second, we have an experimental analysis of the search behavior of NSGA-III-DE and NSGA-III. We observe that NSGA-III-DE is generally better at exploration whereas NSGA-III normally has advantages in exploitation. Third, based on this observation, we present two other NSGA-III variants, where DE operator and genetic operators are simply combined to reproduce solutions. Experimental results on several benchmark problems show that very encouraging performance can be achieved by three suggested new NSGA-III variants. Our work also indicates that the performance of NSGA-III is significantly bottlenecked by its variation operators, providing opportunities for the study of the other alternative ones.}, notes = {Also known as \cite{2754655} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Zapotecas-Martinez:2015:GECCO, author = {Saul Zapotecas-Martinez and Bilel Derbel and Arnaud Liefooghe and Dimo Brockhoff and Hernan E. Aguirre and Kiyoshi Tanaka}, title = {Injecting CMA-ES into MOEA/D}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {783--790}, keywords = {Evolutionary Multiobjective Optimization}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754754}, doi = {doi:10.1145/2739480.2754754}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MOEA/D is an aggregation-based evolutionary algorithm which has been proved extremely efficient and effective for solving multi-objective optimization problems. It is based on the idea of decomposing the original multi-objective problem into several single-objective subproblems by means of well-defined scalarizing functions. Those single-objective subproblems are solved in a cooperative manner by defining a neighborhood relation between them. This makes MOEA/D particularly interesting when attempting to plug and to leverage single-objective optimizers in a multi-objective setting. In this context, we investigate the benefits that MOEA/D can achieve when coupled with CMA-ES, which is believed to be a powerful single-objective optimizer. We rely on the ability of CMA-ES to deal with injected solutions in order to update different covariance matrices with respect to each subproblem defined in MOEA/D. We show that by cooperatively evolving neighboring CMA-ES components, we are able to obtain competitive results for different multi-objective benchmark functions.}, notes = {Also known as \cite{2754754} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Asta:2015:GECCO, author = {Shahriar Asta and Ender Ozcan}, title = {A Tensor Analysis Improved Genetic Algorithm for Online Bin Packing}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {799--806}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754787}, doi = {doi:10.1145/2739480.2754787}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Mutation in a Genetic Algorithm is the key variation operator adjusting the genetic diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value of a gene. In this study, we describe a novel data science approach to adaptively generate the mutation probability for each locus. The trail of high quality candidate solutions obtained during the search process is represented as a 3rd order tensor. Factorizing that tensor captures the common pattern between those solutions, identifying the degree of mutation which is likely to yield improvement at each locus. An online bin packing problem is used as an initial case study to investigate the proposed approach for generating locus dependent mutation probabilities. The empirical results show that the tensor approach improves the performance of a standard Genetic Algorithm on almost all classes of instances, significantly.}, notes = {Also known as \cite{2754787} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bonnevay:2015:GECCO, author = {Stephane Bonnevay and Philippe Aubertin and Gerald Gavin}, title = {A Genetic Algorithm to Solve a Real 2-D Cutting Stock Problem with Setup Cost in the Paper Industry}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {807--814}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754660}, doi = {doi:10.1145/2739480.2754660}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper deals with the Two-Dimensional Cutting Stock Problem with Setup Cost (2CSP-S). This problem is composed of three optimization sub-problems: a 2-D Bin Packing (2BP) problem (to place images on patterns), a Linear Programming (LP) problem (to find for each pattern the number of stock sheets to be printed) and a combinatorial problem (to find the number of each image on each pattern). In this article, we solve the 2CSP-S focusing on this third sub-problem. A genetic algorithm was developed to automatically find the proper number of each image on patterns. It is important to notice that our approach is not a new packing technique. This work was conducted for a paper industry company and experiments were realized on real and artificial datasets.}, notes = {Also known as \cite{2754660} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{dePerthuisdeLaillevault:2015:GECCO, author = {Axel {de Perthuis de Laillevault} and Benjamin Doerr and Carola Doerr}, title = {Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {815--822}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754760}, doi = {doi:10.1145/2739480.2754760}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {That the initialization can have a significant impact on the performance of evolutionary algorithms (EAs) is a well known fact in the empirical evolutionary computation literature. Surprisingly, it has nevertheless received only little attention from the theoretical community. We bridge this gap by providing a thorough runtime analysis for a simple iterated random sampling initialization. In the latter, instead of starting an EA with a random sample, it is started in the best of k search points that are taken from the search space uniformly at random. Implementing this strategy comes at almost no cost, neither in the actual coding work nor in terms of wall-clock time. Taking the best of two random samples already decreases the Theta(n log n) expected runtime of the (1+1)~EA and Randomized Local Search on OneMax by an additive term of order sqrt(n). The optimal gain that one can achieve with iterated random sampling is an additive term of order sqrt(n) log n. This also determines the best possible mutation-based EA for OneMax, a question left open in [Sudholt, IEEE TEC 2013]. At the heart of our analysis is a very precise bound for the maximum of k independent Binomially distributed variables with success probability 1/2.}, notes = {Also known as \cite{2754760} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ding:2015:GECCO, author = {Dan Ding and Guizhen Zhu and Xiaoyun Wang}, title = {A Genetic Algorithm for Searching the Shortest Lattice Vector of SVP Challenge}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {823--830}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754639}, doi = {doi:10.1145/2739480.2754639}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a genetic algorithm for solving the shortest vector problem (SVP) based on sparse representation of short lattice vectors, which, we prove, can guarantee finding the shortest lattice vector under a Markov analysis. With some heuristic improvements (local search and heuristic pruning), the SVP genetic algorithm, by experimental results, outperforms other SVP algorithms, like the famous Kannan-Helfrich algorithm under SVP challenge benchmarks. In summary, we, for the first time, adopt the genetic algorithm in solving the shortest vector problem, based on which lattice-based cryptosystem is as a promising candidate for post-quantum cryptography.}, notes = {Also known as \cite{2754639} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCO, author = {Benjamin Doerr and Carola Doerr and Timo Koetzing}, title = {Solving Problems with Unknown Solution Length at (Almost) No Extra Cost}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {831--838}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754681}, doi = {doi:10.1145/2739480.2754681}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most research in the theory of evolutionary computation assumes that the problem at hand has a fixed problem size. This assumption does not always apply to real-world optimization challenges, where the length of an optimal solution may be unknown a priori. Following up on previous work of Cathabard, Lehre, and Yao [FOGA 2011] we analyze variants of the (1+1) evolutionary algorithm for problems with unknown solution length. For their setting, in which the solution length is sampled from a geometric distribution, we provide mutation rates that yield an expected optimization time that is of the same order as that of the (1+1) EA knowing the solution length. We then show that almost the same run times can be achieved even if no a priori information on the solution length is available. Finally, we provide mutation rates suitable for settings in which neither the solution length nor the positions of the relevant bits are known. Again we obtain almost optimal run times for the OneMax and LeadingOnes test functions, thus solving an open problem from Cathabard et al.}, notes = {Also known as \cite{2754681} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCOa, author = {Carola Doerr and Johannes Lengler}, title = {Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {839--846}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754654}, doi = {doi:10.1145/2739480.2754654}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Black-box complexity theory provides lower bounds for the runtime classes of black-box optimizers like evolutionary algorithms and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new \emph{elitist black-box model}, in which algorithms are required to base all decisions solely on (a fixed number of) the best search points sampled so far. Our model combines features of the ranking-based and the memory-restricted black-box models with elitist selection. We provide several examples for which the elitist black-box complexity is exponentially larger than that the respective complexities in all previous black-box models, thus showing that the elitist black-box complexity can be much closer to the runtime of typical evolutionary algorithms. We also introduce the concept of $p$-Monte Carlo black-box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Even for small $p$, the $p$-Monte Carlo black-box complexity of a function class F can be smaller by an exponential factor than its typically regarded Las Vegas complexity (which measures the expected time it takes to optimize F).}, notes = {Also known as \cite{2754654} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ge:2015:GECCO, author = {Yong-Feng Ge and Yue-Jiao Gong and Wei-Jie Yu and Xiao-Min Hu and Jun Zhang}, title = {Reconstructing Cross-Cut Shredded Text Documents: A Genetic Algorithm with Splicing-Driven Reproduction}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {847--853}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754677}, doi = {doi:10.1145/2739480.2754677}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we focus on reconstruction of cross-cut shredded text documents (RCCSTD), which is of high interest in the fields of forensics and archeology. A novel genetic algorithm, with splicing-driven crossover, four mutation operators, and a row-oriented elitism strategy, is proposed to improve the capability of solving RCCSTD in complex space. We also design a novel and comprehensive objective function based on both edge and empty vector-based splicing error to guarantee that the correct reconstruction always has the lowest cost value. Experiments are conducted on six RCCSTD scenarios, with experimental results showing that the proposed algorithm significantly outperforms the previous best-known algorithms for this problem.}, notes = {Also known as \cite{2754677} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Goldman:2015:GECCO, author = {Brian W. Goldman and William F. Punch}, title = {Gray-Box Optimization using the Parameter-less Population Pyramid}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {855--862}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754775}, doi = {doi:10.1145/2739480.2754775}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Unlike black-box optimization problems, gray-box optimization problems have known, limited, non-linear relationships between variables. Though more restrictive, gray-box problems include many real-world applications in network security, computational biology, VLSI design, and statistical physics. Leveraging these restrictions, the Hamming-Ball Hill Climber (HBHC) can efficiently find high quality local optima. We show how 1) a simple memetic algorithm in conjunction with HBHC can find global optima for some gray-box problems and 2) a gray-box version of the Parameter-less Population Pyramid (P3), using both the HBHC and the known information about variable relationships, outperforms all of the examined algorithms. While HBHC's inclusion into P3 adds a parameter, we show experimentally it can be fixed to 1 without adversely effecting search. We provide experimental evidence on NKq-Landscapes and Ising Spin Glasses that Gray-Box P3 is effective at finding the global optima even for problems with thousands of variables. This capability is complemented by its efficiency, with running time and memory usage decreased by up to a linear factor from Black-Box P3. On NKq this results in a 375x speedup for problems with at least 1,000 variables.}, notes = {Also known as \cite{2754775} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Inoue:2015:GECCO, author = {Kazuyuki Inoue and Taku Hasegawa and Yuta Araki and Naoki Mori and Keinosuke Matsumoto}, title = {Adaptive Control of Parameter-less Population Pyramid on the Local Distribution of Inferior Individuals}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {863--870}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754818}, doi = {doi:10.1145/2739480.2754818}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many evolutionary techniques such as genetic algorithms (GAs) employ parameters that facilitate user control of search dynamics. However, these parameters require time-consuming tuning processes to avoid problems such as premature convergence. Unlike many GAs, the Parameter-less Population Pyramid (P3) is an optimization model that avoids premature convergence due to the pyramid-like structure of populations, and thus P3 can be applied to a wide range of problems without parameter tuning. P3 approaches to search would be useful for constructing a novel theory for optimal search using GAs. In this study, we propose a novel technique analysis based on the Distribution of Inferior Individuals in the local neighborhood (DII analysis). DII analysis can be applied to local search techniques, including P3. The computational complexity of applied problems can be estimated based on a number of local optima according to the results obtained using DII. We also propose combining P3 with DII analysis (P3-DII), which controls the maximum number of fitness evaluations performed by genetic operators. The computational experiments were carried out taking several combinational problems as examples. According to our experimental results, we demonstrated that P3-DII found several optimal solutions that P3 failed to find.}, notes = {Also known as \cite{2754818} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Liang:2015:GECCO, author = {Jason Zhi Liang and Risto Miikkulainen}, title = {Evolutionary Bilevel Optimization for Complex Control Tasks}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {871--878}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754732}, doi = {doi:10.1145/2739480.2754732}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most optimization algorithms must undergo time consuming parameter adaptation in order to optimally solve complex, real-world control tasks. Parameter adaptation is inherently a bilevel optimization problem where the lower level objective function is the performance of the control parameters discovered by an optimization algorithm and the upper level objective function is the performance of the algorithm given its parametrization. In this paper, a novel method called MetaEvolutionary Algorithm (MEA) is presented and shown to be capable of efficiently discovering optimal parameters for neuroevolution to solve control problems. In two challenging examples, double pole balancing and helicopter hovering, MEA discovers optimized parameters that result in better performance than hand tuning and other automatic methods. Bilevel optimization in general and MEA in particular, is thus a promising approach for solving difficult control tasks.}, notes = {Also known as \cite{2754732} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Luiz:2015:GECCO, author = {Thiago A. Luiz and Alan R.R. Freitas and Frederico G. Guimaraes}, title = {A New Perspective on Channel Allocation in WLAN: Considering the Total Marginal Utility of the Connections for the Users}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {879--886}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754663}, doi = {doi:10.1145/2739480.2754663}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The channel allocation problem consists in defining the frequency used by Access Points (APs) in Wireless Local Area Networks (WLAN). An overlap of channels in a WLAN is the major factor of performance reduction for the users in a network. For this reason, we propose a new model for channel allocation that aims to maximize the total quality of the connection of the user by considering their marginal utility. The results show that an allocation model that does not take into account the total utility of each connection tends to prioritize the quality of connection of a few users and lead to a large unbalance in the distribution of connection speed between users. Thus, the new model can handle the importance of degradation caused by the levels of interference in the user connection separately.}, notes = {Also known as \cite{2754663} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Manso:2015:GECCO, author = {Antonio M.R. Manso and Luis M.P. Correia}, title = {Parasite Diversity in Symbiogenetic Multiset Genetic Algorithm: Optimization of Large Binary Problems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {887--894}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754780}, doi = {doi:10.1145/2739480.2754780}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Symbiogenetic MuGA (SMuGA) is a co-evolutionary model exploiting the concept of symbiosis over the Multiset Genetic Algorithm (MuGA). It evolves two species: hosts that represent a solution to the problem, and parasites that represent part-solutions. SMuGA has been proved valuable in the optimization of a variety of deceptive functions. However its performance decreased in large scale difficult problems. This paper presents a new version of SMuGA with improvements centered on the evolution process of the parasites. The most significant advance is provided by using a diversity measure to modulate the evolution of parasites. The algorithm is tested with very good results in deceptive functions with up to 1024 bits. The paper is concluded with an analysis of the advantages and limitations of the approach, and with perspectives for future developments.}, notes = {Also known as \cite{2754780} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Morales-Reyes:2015:GECCO, author = {Alicia Morales-Reyes and Hugo Jair Escalante and Martin Letras and Rene Cumplido}, title = {An Empirical Analysis on Dimensionality in Cellular Genetic Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {895--902}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754699}, doi = {doi:10.1145/2739480.2754699}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cellular or fine grained Genetic Algorithms (GAs) are a massively parallel algorithmic approach to GAs. Decentralizing their population allows alternative ways to explore and to exploit the solutions landscape. Individuals interact locally through nearby neighbors while the entire population is globally exploring the search space throughout a predefined population's topology. Having a decentralized population requires the definition of other algorithmic configuration parameters; such as shape and number of individuals within the local neighborhood, population's topology shape and dimension, local instead of global selection criteria, among others. In this article, attention is paid to the population's topology dimension in cGAs. Several benchmark problems are assessed for 1, 2, and 3 dimensions while combining a local selection criterion that significantly affect overall selective pressure. On the other hand, currently available high performance processing platforms such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) offer massively parallel fabrics. Therefore, having a strong empirical base to understand structural properties in cellular GAs would allow to combine physical properties of these platforms when designing hardware architectures to tackle difficult optimization problems where timing constraints are mandatory.}, notes = {Also known as \cite{2754699} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pourhassan:2015:GECCOa, author = {Mojgan Pourhassan and Wanru Gao and Frank Neumann}, title = {Maintaining 2-Approximations for the Dynamic Vertex Cover Problem Using Evolutionary Algorithms}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {903--910}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754700}, doi = {doi:10.1145/2739480.2754700}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms have been frequently used to deal with dynamic optimization problems, but their success is hard to understand from a theoretical perspective. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for dynamic combinatorial optimization problems. We examine a dynamic version of the classical vertex cover problem and analyse evolutionary algorithms with respect to their ability to maintain a 2-approximation. Analysing the different evolutionary algorithms studied by Jansen et al. (2013), we point out where two previously studied approaches are not able to maintain a 2-approximation even if they start with a solution of that quality. Furthermore, we point out that the third approach is very effective in maintaining 2-approximations for the dynamic vertex cover problem.}, notes = {Also known as \cite{2754700} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Sadowski:2015:GECCO, author = {Krzysztof L. Sadowski and Peter A.N. Bosman and Dirk Thierens}, title = {A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {911--918}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754740}, doi = {doi:10.1145/2739480.2754740}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel clustering-based model-building evolutionary algorithm to tackle optimization problems that have both binary and real-valued variables. The search space is clustered every generation using a distance metric that considers binary and real-valued variables jointly in order to capture and exploit dependencies between variables of different types. After clustering, linkage learning takes place within each cluster to capture and exploit dependencies between variables of the same type. We compare this with a model-building approach that only considers dependencies between variables of the same type. Additionally, since many real-world problems have constraints, we examine the use of different well-known approaches to handling constraints: constraint domination, dynamic penalty and global competitive ranking. We experimentally analyze the performance of the proposed algorithms on various unconstrained problems as well as a selection of well-known MINLP benchmark problems that all have constraints, and compare our results with the Mixed-Integer Evolution Strategy (MIES). We find that our approach to clustering that is aimed at the processing of dependencies between binary and real-valued variables can significantly improve performance in terms of required population size and function evaluations when solving problems that exhibit properties such as multiple optima, strong mixed dependencies and constraints.}, notes = {Also known as \cite{2754740} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Stefanello:2015:GECCO, author = {Fernando Stefanello and Vaneet Aggarwal and Luciana Salete Buriol and Jose Fernando Goncalves and Mauricio G.C. Resende}, title = {A Biased Random-key Genetic Algorithm for Placement of Virtual Machines across Geo-Separated Data Centers}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {919--926}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754768}, doi = {doi:10.1145/2739480.2754768}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cloud computing has recently emerged as a new technology for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware and allowing business owners to start from small and increase resources only when there is a rise in service demand. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers, meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard Generalized Quadratic Assignment Problem (GQAP). We formalize the problem and propose a novel algorithm based on a biased random-key genetic algorithm (BRKGA) to find near-optimal solutions for the problem. The experimental results show that the proposed algorithm is effective in quickly finding feasible solutions and it produces better results than a baseline aproach provided by a commercial solver and a multi-start algorithm.}, notes = {Also known as \cite{2754768} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Whitley:2015:GECCO, author = {Darrell Whitley}, title = {Mk Landscapes, NK Landscapes, MAX-kSAT: A Proof that the Only Challenging Problems are Deceptive}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {927--934}, keywords = {Genetic Algorithms}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754809}, doi = {doi:10.1145/2739480.2754809}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box optimization, the optimizer is given access to the set of M subfunctions. If the set of subfunctions is k-bounded and separable, the Gray Box optimizer is guaranteed to return the global optimum with 1 evaluation. A problem is said to be order k deceptive if the average values of hyperplanes over combinations of k variables cannot be used to infer a globally optimal solution. Hyperplane averages are always efficiently computable for Mk Landscapes. If a problem is not deceptive, the Gray Box optimizer also returns the global optimum after 1 evaluation. Finally, these concepts are used to understand the nonlinearity of problems in the complexity class P, such as Adjacent NK Landscapes. These ideas are also used to understand the problem structure of NP Hard problems such as MAX-kSAT and general Mk Landscapes. In general, NP Hard problems are profoundly deceptive.}, notes = {Also known as \cite{2754809} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Cheney:2015:GECCO, author = {Nick Cheney and Josh Bongard and Hod Lipson}, title = {Evolving Soft Robots in Tight Spaces}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {935--942}, keywords = {Generative and Developmental Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754662}, doi = {doi:10.1145/2739480.2754662}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Soft robots have become increasingly popular in recent years -- and justifiably so. Their compliant structures and (theoretically) infinite degrees of freedom allow them to undertake tasks which would be impossible for their rigid body counterparts, such as conforming to uneven surfaces, efficiently distributing stress, and passing through small apertures. Previous work in the automated deign of soft robots has shown examples of these squishy creatures performing traditional robotic task like locomoting over flat ground. However, designing soft robots for traditional robotic tasks fails to fully use their unique advantages. In this work, we present the first example of a soft robot evolutionarily designed for reaching or squeezing through a small aperture -- a task naturally suited to its type of morphology. We optimize these creatures with the CPPN-NEAT evolutionary algorithm, introducing a novel implementation of the algorithm which includes multi-objective optimization while retaining its speciation feature for diversity maintenance. We show that more compliant and deformable soft robots perform more effectively at this task than their less flexible counterparts. This work serves mainly as a proof of concept, but we hope that it helps to open the door for the better matching of tasks with appropriate morphologies in robotic design in the future.}, notes = {Also known as \cite{2754662} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Gomes:2015:GECCO, author = {Jorge Gomes and Pedro Mariano and Anders Lyhne Christensen}, title = {Devising Effective Novelty Search Algorithms: A Comprehensive Empirical Study}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {943--950}, keywords = {Generative and Developmental Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754736}, doi = {doi:10.1145/2739480.2754736}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolutionary dynamics and performance of novelty search. In this paper, we conduct a comprehensive empirical study focused on novelty search's algorithmic components. We study the k parameter -- the number of nearest neighbours used in the computation of novelty scores; the use and function of an archive; how to combine novelty search with fitness-based evolution; and how to configure the mutation rate of the underlying evolutionary algorithm. Our study is conducted in a simulated maze navigation task. Our results show that the configuration of novelty search can have a significant impact on performance and behaviour space exploration. We conclude with a number of guidelines for the implementation and configuration of novelty search, which should help future practitioners to apply novelty search more effectively.}, notes = {Also known as \cite{2754736} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Lehman:2015:GECCO, author = {Joel Lehman and Risto Miikkulainen}, title = {Enhancing Divergent Search through Extinction Events}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {951--958}, keywords = {Generative and Developmental Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754668}, doi = {doi:10.1145/2739480.2754668}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A challenge in evolutionary computation is to create representations as evolvable as those in natural evolution. This paper hypothesizes that extinction events, i.e. mass extinctions, can significantly increase evolvability, but only when combined with a divergent search algorithm, i.e. a search driven towards diversity (instead of optimality). Extinctions amplify diversity-generation by creating unpredictable evolutionary bottlenecks. Persisting through multiple such bottlenecks is more likely for lineages that diversify across many niches, resulting in indirect selection pressure for the capacity to evolve. This hypothesis is tested through experiments in two evolutionary robotics domains. The results show that combining extinction events with divergent search increases evolvability, while combining them with convergent search offers no similar benefit. The conclusion is that extinction events may provide a simple and effective mechanism to enhance performance of divergent search algorithms.}, notes = {Also known as \cite{2754668} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Nguyen:2015:GECCOa, author = {Anh Mai Nguyen and Jason Yosinski and Jeff Clune}, title = {Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {959--966}, keywords = {Generative and Developmental Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754703}, doi = {doi:10.1145/2739480.2754703}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search avoids this problem by encouraging a search in all interesting directions. That occurs by replacing a performance objective with a reward for novel behaviors, as defined by a human-crafted, and often simple, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a novelty pressure in image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g. churches, mosques, obelisks, etc.). Here we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: e.g. producing intelligent software, robot controllers, optimized physical components, and art.}, notes = {Also known as \cite{2754703} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pugh:2015:GECCO, author = {Justin K. Pugh and L. B. Soros and Paul A. Szerlip and Kenneth O. Stanley}, title = {Confronting the Challenge of Quality Diversity}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {967--974}, keywords = {Generative and Developmental Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754664}, doi = {doi:10.1145/2739480.2754664}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In contrast to the conventional role of evolution in evolutionary computation (EC) as an optimization algorithm, a new class of evolutionary algorithms has emerged in recent years that instead aim to accumulate as diverse a collection of discoveries as possible, yet where each variant in the collection is as fit as it can be. Often applied in both neuroevolution and morphological evolution, these new quality diversity (QD) algorithms are particularly well-suited to evolution's inherent strengths, thereby offering a promising niche for EC within the broader field of machine learning. However, because QD algorithms are so new, until now no comprehensive study has yet attempted to systematically elucidate their relative strengths and weaknesses under different conditions. Taking a first step in this direction, this paper introduces a new benchmark domain designed specifically to compare and contrast QD algorithms. It then shows how the degree of alignment between the measure of quality and the behavior characterization (which is an essential component of all QD algorithms to date) impacts the ultimate performance of different such algorithms. The hope is that this initial study will help to stimulate interest in QD and begin to unify the disparate ideas in the area.}, notes = {Also known as \cite{2754664} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Al-Sahaf:2015:GECCO, author = {Harith Al-Sahaf and Mengjie Zhang and Mark Johnston}, title = {Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {975--982}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754661}, doi = {doi:10.1145/2739480.2754661}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Texture classification aims at categorising instances that have a similar repetitive pattern. In computer vision, texture classification represents a fundamental element in a wide variety of applications, which can be performed by detecting texture primitives of the different classes. Using image descriptors to detect prominent features has been widely adopted in computer vision. Building an effective descriptor becomes more challenging when there are only a few labelled instances. This paper proposes a new Genetic Programming (GP) representation for evolving an image descriptor that operates directly on the raw pixel values and uses only two instances per class. The new method synthesises a set of mathematical formulas that are used to generate the feature vector, and the classification is then performed using a simple instance-based classifier. Determining the length of the feature vector is automatically handled by the new method. Two GP and nine well-known non-GP methods are compared on two texture image data sets for texture classification in order to test the effectiveness of the proposed method. The proposed method is also compared to three hand-crafted descriptors namely domain-independent features, local binary patterns, and Haralick texture features. The results show that the proposed method has superior performance over the competitive methods.}, notes = {Also known as \cite{2754661} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Arnaldo:2015:GECCO, author = {Ignacio Arnaldo and Una-May O'Reilly and Kalyan Veeramachaneni}, title = {Building Predictive Models via Feature Synthesis}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {983--990}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754693}, doi = {doi:10.1145/2739480.2754693}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce Evolutionary Feature Synthesis (EFS), a regression method that generates readable, nonlinear models of small to medium size datasets in seconds. EFS is, to the best of our knowledge, the fastest regression tool based on evolutionary computation reported to date. The feature search involved in the proposed method is composed of two main steps: feature composition and feature subset selection. EFS adopts a bottom-up feature composition strategy that eliminates the need for a symbolic representation of the features and exploits the variable selection process involved in pathwise regularized linear regression to perform the feature subset selection step. The result is a regression method that is competitive against neural networks, and outperforms both linear methods and Multiple Regression Genetic Programming, up to now the best regression tool based on evolutionary computation.}, notes = {Also known as \cite{2754693} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Burks:2015:GECCO, author = {Armand R. Burks and William F. Punch}, title = {An Efficient Structural Diversity Technique for Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {991--998}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754649}, doi = {doi:10.1145/2739480.2754649}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic diversity plays an important role in avoiding premature convergence, which is a phenomenon that stifles the search effectiveness of evolutionary algorithms. However, approaches that avoid premature convergence by maintaining genetic diversity can do so at the cost of efficiency, requiring more fitness evaluations to find high quality solutions. We introduce a simple and efficient genetic diversity technique that is capable of avoiding premature convergence while maintaining a high level of search quality in tree-based genetic programming. Our method finds solutions to a set of benchmark problems in significantly fewer fitness evaluations than the algorithms that we compared against.}, notes = {Also known as \cite{2754649} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Castelli:2015:GECCO, author = {Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Sara Silva and Emigdio Z-Flores and Pierrick Legrand}, title = {Geometric Semantic Genetic Programming with Local Search}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {999--1006}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754795}, doi = {doi:10.1145/2739480.2754795}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Since its introduction, Geometric Semantic Genetic Programming (GSGP) has aroused the interest of numerous researchers and several studies have demonstrated that GSGP is able to effectively optimize training data by means of small variation steps, that also have the effect of limiting overfitting. In order to speed up the search process, in this paper we propose a system that integrates a local search strategy into GSGP (called GSGP-LS). Furthermore, we present a hybrid approach, that combines GSGP and GSGP-LS, aimed at exploiting both the optimization speed of GSGP-LS and the ability to limit overfitting of GSGP. The experimental results we present, performed on a set of complex real-life applications, show that GSGP-LS achieves the best training fitness while converging very quickly, but severely overfits. On the other hand, GSGP converges slowly relative to the other methods, but is basically not affected by overfitting. The best overall results were achieved with the hybrid approach, allowing the search to converge quickly, while also exhibiting a noteworthy ability to limit overfitting. These results are encouraging, and suggest that future GSGP algorithms should focus on finding the correct balance between the greedy optimization of a local search strategy and the more robust geometric semantic operators.}, notes = {Also known as \cite{2754795} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Chennupati:2015:GECCO, author = {Gopinath Chennupati and R. Muhammad Atif Azad and Conor Ryan}, title = {Performance Optimization of Multi-Core Grammatical Evolution Generated Parallel Recursive Programs}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1007--1014}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754746}, doi = {doi:10.1145/2739480.2754746}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Although Evolutionary Computation (EC) has been used with considerable success to evolve computer programs, the majority of this work has targeted the production of serial code. Recent work with Grammatical Evolution (GE) produced Multi-core Grammatical Evolution (MCGE-II), a system that natively produces parallel code, including the ability to execute recursive calls in parallel. This paper extends this work by including practical constraints into the grammars and fitness functions, such as increased control over the level of parallelism for each individual. These changes execute the best-of-generation programs faster than the original MCGE-II with an average factor of 8.13 across a selection of hard problems from the literature. We analyze the time complexity of these programs and identify avoiding excessive parallelism as a key for further performance scaling. We amend the grammars to evolve a mix of serial and parallel code, which spawns only as many threads as is efficient given the underlying OS and hardware; this speeds up execution by a factor of 9.97.}, notes = {Also known as \cite{2754746} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Dick:2015:GECCO, author = {Grant Dick and Aysha P. Rimoni and Peter A. Whigham}, title = {A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1015--1022}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754771}, doi = {doi:10.1145/2739480.2754771}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Difficult benchmark problems are in increasing demand in Genetic Programming (GP). One problem seeing increased usage is the oral bioavailability problem, which is often presented as a challenging problem to both GP and other machine learning methods. However, few properties of the bioavailability data set have been demonstrated, so attributes that make it a challenging problem are largely unknown. This work uncovers important properties of the bioavailability data set, and suggests that the perceived difficulty in this problem can be partially attributed to a lack of pre-processing, including features within the data set that contain no information, and contradictory relationships between the dependent and independent features of the data set. The paper then re-examines the performance of GP on this data set, and contextualises this performance relative to other regression methods. Results suggest that a large component of the observed performance differences on the bioavailability data set can be attributed to variance in the selection of training and testing data. Differences in performance between GP and other methods disappear when multiple training/testing splits are used within experimental work, with performance typically no better than a null modelling approach of reporting the mean of the training data.}, notes = {Also known as \cite{2754771} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ffrancon:2015:GECCO, author = {Robyn Ffrancon and Marc Schoenauer}, title = {Memetic Semantic Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1023--1030}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754697}, doi = {doi:10.1145/2739480.2754697}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal should-be values each subtree should return, whilst assuming that the rest of the tree is unchanged, so as to minimize the fitness of the tree. To this end, the Random Desired Output (RDO) mutation operator, proposed in [17], uses SB in choosing, from a given library, a tree whose semantics are preferred to the semantics of a randomly selected subtree from the parent tree. Pushing this idea one step further, this paper introduces the Brando (BRANDO) operator, which selects from the parent tree the overall best subtree for applying RDO, using a small randomly drawn static library. Used within a simple Iterated Local Search framework, BRANDO can find the exact solution of many popular Boolean benchmarks in reasonable time whilst keeping solution trees small, thus paving the road for truly memetic GP algorithms.}, notes = {Also known as \cite{2754697} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Fieldsend:2015:GECCO, author = {Jonathan E. Fieldsend and Alberto Moraglio}, title = {Strength Through Diversity: Disaggregation and Multi-Objectivisation Approaches for Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1031--1038}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754643}, doi = {doi:10.1145/2739480.2754643}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An underlying problem in genetic programming (GP) is how to ensure sufficient useful diversity in the population during search. Having a wide range of diverse (sub)component structures available for recombination and/or mutation is important in preventing premature converge. We propose two new fitness disaggregation approaches that make explicit use of the information in the test cases (i.e., program semantics) to preserve diversity in the population. The first method preserves the best programs which pass each individual test case, the second preserves those which are non-dominated across test cases (multi-objectivisation). We use these in standard GP, and compare them to using standard fitness sharing, and using standard (aggregate) fitness in tournament selection. We also examine the effect of including a simple anti-bloat criterion in the selection mechanism. We find that the non-domination approach, employing anti-bloat, significantly speeds up convergence to the optimum on a range of standard Boolean test problems. Furthermore, its best performance occurs with a considerably smaller population size than typically employed in GP.}, notes = {Also known as \cite{2754643} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Helmuth:2015:GECCO, author = {Thomas Helmuth and Lee Spector}, title = {General Program Synthesis Benchmark Suite}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1039--1046}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754769}, doi = {doi:10.1145/2739480.2754769}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent interest in the development and use of non-trivial benchmark problems for genetic programming research has highlighted the scarcity of general program synthesis (also called traditional programming) benchmark problems. We present a suite of 29 general program synthesis benchmark problems systematically selected from sources of introductory computer science programming problems. This suite is suitable for experiments with any program synthesis system driven by input/output examples. We present results from illustrative experiments using our reference implementation of the problems in the PushGP genetic programming system. The results show that the problems in the suite vary in difficulty and can be useful for assessing the capabilities of a program synthesis system.}, notes = {Also known as \cite{2754769} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Jia:2015:GECCO, author = {Baozhu Jia and Marc Ebner and Christian Schack}, title = {A GP-based Video Game Player}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1047--1053}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754735}, doi = {doi:10.1145/2739480.2754735}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A general video game player is an an agent that can learn to play different video games with no specific domain knowledge. We are working towards developing a GP-based general video game player. Our system currently extracts game state features from screen grabs. This information is then passed on to the game player. Fitness is computed from data obtained directly from the internals of the game simulator. For this paper, we compare three different types of game state features. These features differ in how they describe the position to the nearest object surrounding the player. We have tested our genetic programming game player system on three games: Space Invaders, Frogger and Missile Command. Our results show that a playing strategy for each game can be found efficiently for all three representations.}, notes = {Also known as \cite{2754735} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{LaCava:2015:GECCO, author = {William {La Cava} and Thomas Helmuth and Lee Spector and Kourosh Danai}, title = {Genetic Programming with Epigenetic Local Search}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1055--1062}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754763}, doi = {doi:10.1145/2739480.2754763}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We focus on improving genetic programming through local search of the space of program structures using an inheritable epigenetic layer that specifies active and inactive genes. We explore several genetic programming implementations that represent the different properties that epigenetics can provide, such as passive structure, phenotypic plasticity, and inheritable gene regulation. We apply these implementations to several symbolic regression and program synthesis problems. For the symbolic regression problems, the results indicate that epigenetic local search consistently improves genetic programming by producing smaller solution programs with better fitness. Furthermore, we find that incorporating epigenetic modification as a mutation step in program synthesis problems can improve the ability of genetic programming to find exact solutions. By analyzing population homology we show that the epigenetic implementations maintain diversity in silenced portions of programs which may provide protection from premature convergence.}, notes = {Also known as \cite{2754763} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Langdon:2015:GECCO, author = {William B. Langdon and Brian Yee Hong Lam and Justyna Petke and Mark Harman}, title = {Improving CUDA DNA Analysis Software with Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1063--1070}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754652}, doi = {doi:10.1145/2739480.2754652}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We genetically improve BarraCUDA using a BNF grammar incorporating C scoping rules with GP. Barracuda maps next generation DNA sequences to the human genome using the Burrows-Wheeler algorithm (BWA) on nVidia Tesla parallel graphics hardware (GPUs). GI using phenotypic tabu search with manually grown code can graft new features giving more than 100 fold speed up on a performance critical kernel without loss of accuracy.}, notes = {Also known as \cite{2754652} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Leonard:2015:GECCO, author = {Philip Leonard and David Jackson}, title = {Efficient Evolution of High Entropy RNGs Using Single Node Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1071--1078}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754820}, doi = {doi:10.1145/2739480.2754820}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Random Number Generators are an important aspect of many modern day software systems, cryptographic protocols and modelling techniques. To be more accurate, it is Pseudo Random Number Generators (PRNGs) that are more commonly used over their expensive, and less practical hardware based counterparts. Given that PRNGs rely on some deterministic algorithm (typically a Linear Congruential Generator) we can leverage Shannon's theory of information as our fitness function in order to generate these algorithms by evolutionary means. In this paper we compare traditional Genetic Programming (GP) against its graph based implementation, Single Node Genetic Programming (SNGP), for this task. We show that with SNGPs unique program structure and use of dynamic programming, it is possible to obtain smaller, higher entropy PRNGs, over six times faster and produced at a solution rate twice that achieved using Koza's standard GP model. We also show that the PRNGs obtained from evolutionary methods produce higher entropy outputs than other widely used PRNGs and Hardware RNGs (specifically recordings of atmospheric noise), as well as surpassing them in a variety of other statistical tests presented in the NIST RNG test suite.}, notes = {Also known as \cite{2754820} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{McPhee:2015:GECCO, author = {Nicholas Freitag McPhee and M. Kirbie Dramdahl and David Donatucci}, title = {Impact of Crossover Bias in Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1079--1086}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754778}, doi = {doi:10.1145/2739480.2754778}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In tree-based genetic programming (GP) with sub-tree crossover, the parent contributing the root portion of the tree (the root parent) often contributes more to the semantics of the resulting child than the non-root parent. Previous research demonstrated that when the root parent had greater fitness than the non-root parent, the fitness of the child tended to be better than if the reverse were true. Here we explore the significance of that asymmetry by introducing the notion of crossover bias, where we bias the system in favor of having the more fit parent as the root parent. In this paper we apply crossover bias to several problems. In most cases we found that crossover bias either improved performance or had no impact. We also found that the effectiveness of crossover bias is dependent on the problem, and significantly dependent on other parameter choices. While this work focuses specifically on sub-tree crossover in tree-based GP, artificial and biological evolutionary systems often have substantial asymmetries, many of which remain understudied. This work suggests that there is value in further exploration of the impacts of these asymmetries.}, notes = {Also known as \cite{2754778} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Miranda:2015:GECCO, author = {Pericles Barbosa Miranda and Ricardo Bastos Prudencio}, title = {GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1087--1094}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754819}, doi = {doi:10.1145/2739480.2754819}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work, we propose a framework to automatically generate effective PSO designs by adopting Grammatical Evolution (GE). In the proposed framework, GE searches for adequate structures and parameter values (e.g., acceleration constants, velocity equations and different particles' topology) in order to evolve the PSO design. For this, a high-level Backus--Naur Form (BNF) grammar was developed, representing the search space of possible PSO designs. In order to verify the performance of the proposed method, we performed experiments using 16 diverse continuous optimization problems, with different levels of difficulty. In the performed experiments, we identified the parameters and components that most affected the PSO performance, as well as identified designs that could be reused across different problems. We also demonstrated that the proposed method generates useful designs which achieved competitive solutions when compared to well succeeded algorithms from the literature.}, notes = {Also known as \cite{2754819} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Picek:2015:GECCOa, author = {Stjepan Picek and Claude Carlet and Domagoj Jakobovic and Julian F. Miller and Lejla Batina}, title = {Correlation Immunity of Boolean Functions: An Evolutionary Algorithms Perspective}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1095--1102}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754764}, doi = {doi:10.1145/2739480.2754764}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Boolean functions are essential in many stream ciphers. When used in combiner generators, they need to have sufficiently high values of correlation immunity, alongside other properties. In addition, correlation immune functions with small Hamming weight reduce the cost of masking countermeasures against side-channel attacks. Various papers have examined the applicability of evolutionary algorithms for evolving cryptographic Boolean functions. However, even when authors considered correlation immunity, it was not given the highest priority. Here, we examine the effectiveness of three different EAs, namely, Genetic Algorithms, Genetic Programming (GP) and Cartesian GP for evolving correlation immune Boolean functions. Besides the properties of balancedness and correlation immunity, we consider several other relevant cryptographic properties while maintaining the optimal trade-offs among them. We show that evolving correlation immune Boolean functions is an even harder objective than maximizing nonlinearity.}, notes = {Also known as \cite{2754764} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Thorhauer:2015:GECCO, author = {Ann Thorhauer and Franz Rothlauf}, title = {On the Bias of Syntactic Geometric Recombination in Genetic Programming and Grammatical Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1103--1110}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754726}, doi = {doi:10.1145/2739480.2754726}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {For fixed-length binary representations as used in genetic algorithms, standard recombination operators (e.g.,~one-point crossover) are unbiased. Thus, the application of recombination only reshuffles the alleles and does not change the statistical properties in the population. Using a geometric view on recombination operators, most search operators for fixed-length strings are geometric, which means that the distances between offspring and their parents are less than, or equal to, the distance between their parents. In genetic programming (GP) and grammatical evolution (GE), the situation is different since the recombination operators are applied to variable-length structures. Thus, most recombination operators for GE and GP are not geometric. This paper focuses on the bias of recombination in GE and GP and studies whether the application of recombination alone produces specific types of solutions with a higher probability. We consider two different types of recombination operators: standard recombination and syntactic geometric recombination. In our experiments, we performed random walks through the binary tree search space and found that syntactic geometric recombination operators are biased and strongly reduce population diversity. In a performance comparison, we found that syntactic geometric recombination leads to large fitness improvements in the first generations, but that fitness converges after several generations and no further search is possible.}, notes = {Also known as \cite{2754726} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Whigham:2015:GECCO, author = {Peter A. Whigham and Grant Dick and James Maclaurin and Caitlin A. Owen}, title = {Examining the "Best of Both Worlds" of Grammatical Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1111--1118}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754784}, doi = {doi:10.1145/2739480.2754784}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Grammatical Evolution (GE) has a long history in evolutionary computation. Central to the behaviour of GE is the use of a linear representation and grammar to map individuals from search spaces into problem spaces. This genotype to phenotype mapping is often argued as a distinguishing property of GE relative to other techniques, such as context-free grammar genetic programming (CFG-GP). Since its initial description, GE research has attempted to incorporate information from the grammar into crossover, mutation, and individual initialisation, blurring the distinction between genotype and phenotype and creating GE variants closer to CFG-GP. This is argued to provide GE with the best of both worlds, allowing degrees of grammatical bias to be introduced into operators to best suit the given problem. This paper examines the behaviour of three grammar-based search methods on several problems from previous GE research. It is shown that, unlike CFG-GP, the performance of pure GE on the examined problems closely resembles that of random search. The results suggest that further work is required to determine the cases where the best of both worlds of GE are required over a straight CFG-GP approach.}, notes = {Also known as \cite{2754784} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Zhu:2015:GECCO, author = {Ling Zhu and Sandeep Kulkarni}, title = {Using Model Checking Techniques For Evaluating the Effectiveness of Evolutionary Computing in Synthesis of Distributed Fault-Tolerant Programs}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1119--1126}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754779}, doi = {doi:10.1145/2739480.2754779}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In most applications using genetic programming (GP), objective functions are obtained by a terminating calculation. However, the terminating calculation cannot evaluate distributed fault-tolerant programs accurately. A key distinction in synthesizing distributed fault-tolerant programs is that they are inherently non-deterministic, potentially having infinite computations and executing in an unpredictable environment. In this study, we apply a model checking technique - Binary Decision Diagrams (BDDs) - to GP, evaluating distributed programs by computing reachable states of the given program and identifying whether it satisfies its specification. We present scenario-based multi-objective approach that each program is evaluated under different scenarios which represent various environments. The computation of the programs are considered in two different semantics respectively: interleaving and maximum-parallelism. In the end, we illustrate our approach with a Byzantine agreement problem, a token ring problem and a consensus protocol using failure detector S. For the first time, this work automatically synthesizes the consensus protocol with S. The results show the proposed method enhances the effectiveness of GP in all studied cases when using maximum-parallelism semantic.}, notes = {Also known as \cite{2754779} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Zutty:2015:GECCO, author = {Jason Zutty and Daniel Long and Heyward Adams and Gisele Bennett and Christina Baxter}, title = {Multiple Objective Vector-Based Genetic Programming Using Human-Derived Primitives}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1127--1134}, keywords = {genetic algorithms, genetic programming}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754694}, doi = {doi:10.1145/2739480.2754694}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions. As an example, GTMOEP was applied to the problem of predicting how long an emergency responder can remain in a hazmat suit before the effects of heat stress cause the user to become unsafe. An existing third-party physics model was leveraged for predicting core temperature from various situational parameters. However, a sustained high heart rate also means that a user is unsafe. To improve performance, GTMOEP was evaluated to predict an expected pull time, computed from both thresholds during human trials. GTMOEP produced dominant solutions in multiple objective space to the performance of predictions made by the physics model alone, resulting in a safer algorithm for emergency responders to determine operating times in harsh environments. The program generated by GTMOEP will be deployed to a mobile application for their use.}, notes = {Also known as \cite{2754694} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bahcceci:2015:GECCO, author = {Erkin Bahceci and Riitta Katila and Risto Miikkulainen}, title = {Evolving Strategies for Social Innovation Games}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1135--1142}, keywords = {Integrative Genetic and Evolutionary Computation}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754790}, doi = {doi:10.1145/2739480.2754790}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively in competitive problem-solving domains. This paper formalizes human creative problem solving as competitive multi-agent search, and advances the hypothesis that evolutionary computation can be used to discover effective strategies for it. In experiments in a social innovation game (similar to a fantasy sports league), neural networks were first trained to model individual human players. These networks were then used as opponents to evolve better game-play strategies with the NEAT neuroevolution method. Evolved strategies scored significantly higher than the human models by innovating, retaining, and retrieving less and by imitating more, thus providing insight into how performance could be improved in such domains. Evolutionary computation in competitive multi-agent search thus provides a possible framework for understanding and supporting various human creative activities in the future.}, notes = {Also known as \cite{2754790} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Kelly:2015:GECCO, author = {Stephen Kelly and Malcolm I. Heywood}, title = {Knowledge Transfer from Keepaway Soccer to Half-field Offense through Program Symbiosis: Building Simple Programs for a Complex Task}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1143--1150}, keywords = {genetic algorithms, genetic programming, Integrative Genetic and Evolutionary Computation}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754798}, doi = {doi:10.1145/2739480.2754798}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Half-field Offense (HFO) is a sub-task of Robocup 2D Simulated Soccer. HFO is a challenging, multi-agent machine learning problem in which a team of offense players attempt to manoeuvre the ball past a defending team and around the goalie in order to score. The agent's sensors and actuators are noisy, making the problem highly stochastic and partially observable. These same real-world characteristics have made Keepaway soccer, which represents one sub-task of HFO, a popular testbed in the reinforcement learning and task-transfer literature in particular. We demonstrate how policies initially evolved for Keepaway can be reused within a symbiotic framework for coevolving policies in genetic programming (GP), with no additional training or transfer function, in order to improve learning in the HFO task. Moreover, the highly modular policies discovered by GP are shown to be significantly less complex than solutions based on traditional value-function optimization while achieving the same level of play in HFO.}, notes = {Also known as \cite{2754798} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Z-Flores:2015:GECCO, author = {Emigdio Z-Flores and Leonardo Trujillo and Oliver Schuetze and Pierrick Legrand}, title = {A Local Search Approach to Genetic Programming for Binary Classification}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1151--1158}, keywords = {genetic algorithms, genetic programming, Integrative Genetic and Evolutionary Computation}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754797}, doi = {doi:10.1145/2739480.2754797}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In standard genetic programming (GP), a search is performed over a syntax space defined by the set of primitives, looking for the best expressions that minimize a cost function based on a training set. However, most GP systems lack a numerical optimization method to fine tune the implicit parameters of each candidate solution. Instead, GP relies on more exploratory search operators at the syntax level. This work proposes a memetic GP, tailored for binary classification problems. In the proposed method, each node in a GP tree is weighted by a real-valued parameter, which is then numerically optimized using a continuous transfer function and the Trust Region algorithm is used as a local search method. Experimental results show that potential classifiers produced by GP are improved by the local searcher, and hence the overall search is improved achieving significant performance gains, that are competitive with state-of-the-art methods on well-known benchmarks.}, notes = {Also known as \cite{2754797} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ha:2015:GECCO, author = {Sungjoo Ha and Byung-Ro Moon}, title = {Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1159--1166}, keywords = {genetic algorithms, genetic programming, Parallel Evolutionary Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754669}, doi = {doi:10.1145/2739480.2754669}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We tackle the problem of knowledge discovery in time series data using genetic programming and GPGPUs. Using genetic programming, various precursor patterns that have certain attractive qualities are evolved to predict the events of interest. Unfortunately, evolving a set of diverse patterns typically takes huge execution time, sometimes longer than one month for this case. In this paper, we address this problem by proposing a parallel GP framework using GPGPUs, particularly in the context of big financial data. By maximally exploiting the structure of the nVidia GPGPU platform on stock market time series data, we were able see more than 250-fold reduction in the running time.}, notes = {Also known as \cite{2754669} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Nowak:2015:GECCO, author = {Krzysztof Nowak and Dario Izzo and Daniel Hennes}, title = {Injection, Saturation and Feedback in Meta-Heuristic Interactions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1167--1174}, keywords = {Parallel Evolutionary Systems}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754729}, doi = {doi:10.1145/2739480.2754729}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Meta-heuristics have proven to be an efficient method of handling difficult global optimization tasks. A recent trend in evolutionary computation is the use of several meta-heuristics at the same time, allowing for occasional information exchange among them in hope to take advantage from the best algorithmic properties of all. Such an approach is inherently parallel and, with some restrictions, has a straight forward implementation in a heterogeneous island model. We propose a methodology for characterizing the interplay between different algorithms, and we use it to discuss their performance on real-parameter single objective optimization benchmarks. We introduce the new concepts of feedback, saturation and injection, and show how they are powerful tools to describe the interplay between different algorithms and thus to improve our understanding of the internal mechanism at work in large parallel evolutionary set-ups.}, notes = {Also known as \cite{2754729} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Araujo:2015:GECCO, author = {Ricardo de A Araujo and Adriano L.I. Oliveira and Silvio R. {de L. Meira}}, title = {A Model with Evolutionary Covariance-based Learning for High-Frequency Financial Forecasting}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1175--1182}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754815}, doi = {doi:10.1145/2739480.2754815}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Several approaches have been investigated to develop models able to solve forecasting problems. However, a limitation arises in the particular case of daily-frequency financial forecasting and is called the random walk dilemma (RWD). In this context, the concept of time phase adjustment can be included in forecasting models to overcome such a drawback. But the evolution of trading systems has increased the frequency for performing operations in the stock market for fractions of seconds, which requires the analysis of high-frequency financial time series. Thus, this work proposes a model, called the increasing decreasing linear neuron (IDLN), to forecast high-frequency financial time series from the Brazilian stock market. Furthermore, an evolutionary covariance-based method with automatic time phase adjustment is presented for the design of the proposed model, and the obtained results overcame those obtained by classical forecasting models in the literature.}, notes = {Also known as \cite{2754815} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bartoli:2015:GECCO, author = {Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao and Marco Virgolin}, title = {Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1183--1190}, keywords = {genetic algorithms, genetic programming, Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754706}, doi = {doi:10.1145/2739480.2754706}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There is an increasing interest in the development of techniques for automatic relation extraction from unstructured text. The biomedical domain, in particular, is a sector that may greatly benefit from those techniques due to the huge and ever increasing amount of scientific publications describing observed phenomena of potential clinical interest. In this paper, we consider the problem of automatically identifying sentences that contain interactions between genes and proteins, based solely on a dictionary of genes and proteins and a small set of sample sentences in natural language. We propose an evolutionary technique for learning a classifier that is capable of detecting the desired sentences within scientific publications with high accuracy. The key feature of our proposal, that is internally based on Genetic Programming, is the construction of a model of the relevant syntax patterns in terms of standard part-of-speech annotations. The model consists of a set of regular expressions that are learned automatically despite the large alphabet size involved. We assess our approach on two realistic datasets and obtain 74percent accuracy, a value sufficiently high to be of practical interest and that is in line with significant baseline methods.}, notes = {Also known as \cite{2754706} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Carosio:2015:GECCO, author = {Grazieli Luiza Costa Carosio and Thomas Donald Humphries and Ronald Dale Haynes and Colin Glennie Farquharson}, title = {A Closer Look At Differential Evolution For The Optimal Well Placement Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1191--1198}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754772}, doi = {doi:10.1145/2739480.2754772}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Energy demand has increased considerably with the growth of world population, increasing the interest in the hydrocarbon reservoir management problem. Companies are concerned with maximizing oil recovery while minimizing capital investment and operational costs. A first step in solving this problem is to consider optimal well placement. In this work, we investigate the Differential Evolution (DE) optimization method, using distinct configurations with respect to population size, mutation factor, crossover probability, and mutation strategy, to solve the well placement problem. By assuming a bare control procedure, one optimizes the parameters representing positions of injection and production wells. The Tenth SPE Comparative Solution Project and MATLAB Reservoir Simulation Toolbox (MRST) are the benchmark dataset and simulator used, respectively. The goal is to evaluate the performance of DE in solving this important real-world problem. We show that DE can find high-quality solutions, when compared with a reference from the literature, and a preliminary analysis on the results of multiple experiments gives useful information on how DE configuration impacts its performance.}, notes = {Also known as \cite{2754772} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Fitzgerald:2015:GECCO, author = {Jeannie M. Fitzgerald and Conor Ryan and David Medernach and Krzysztof Krawiec}, title = {An Integrated Approach to Stage 1 Breast Cancer Detection}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1199--1206}, keywords = {genetic algorithms, genetic programming, Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754761}, doi = {doi:10.1145/2739480.2754761}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present an automated, end-to-end approach for Stage~1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. In the final phase, the most salient of these features are fed into a Multi-Objective Genetic Programming system which then evolves classifiers capable of identifying those segments which may have suspicious areas that require further investigation. A key aspect of this work is the examination of several new experimental configurations which focus on textural asymmetry between breasts. The best evolved classifier using such a configuration can deliver results of 100percent accuracy on true positives and a false positive per image rating of just 0.33, which is better than the current state of the art.}, notes = {Also known as \cite{2754761} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Izzo:2015:GECCO, author = {Dario Izzo and Ingmar Getzner and Daniel Hennes and Luis Felismino Simoes}, title = {Evolving Solutions to TSP Variants for Active Space Debris Removal}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1207--1214}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754727}, doi = {doi:10.1145/2739480.2754727}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The space close to our planet is getting more and more polluted. Orbiting debris are posing an increasing threat to operational orbits and the cascading effect, known as Kessler syndrome, may result in a future where the risk of orbiting our planet at some altitudes will be unacceptable. Many argue that the debris density at the Low Earth Orbit (LEO) has already reached a level sufficient to trigger such a cascading effect. An obvious consequence is that we may soon have to actively clean space from debris. Such a space mission will involve a complex combinatorial decision as to choose which debris to remove and in what order. In this paper, we find that this part of the design of an active debris removal mission (ADR) can be mapped into increasingly complex variants to the classic Travelling Salesman Problem (TSP) and that they can be solved by the Inver-over algorithm improving the current state-of-the-art in ADR mission design. We define static and dynamic cases, according to whether we consider the debris orbits as fixed in time or subject to orbital perturbations. We are able, for the first time, to select optimally objects from debris clouds of considerable size: hundreds debris pieces considered while previous works stopped at tens.}, notes = {Also known as \cite{2754727} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Lee:2015:GECCO, author = {Sangyeop Lee and Jinhyun Kim and Jae Woo Kim and Byung-Ro Moon}, title = {Finding an Optimal LEGO Brick Layout of Voxelized 3D Object Using a Genetic Algorithm}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1215--1222}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754667}, doi = {doi:10.1145/2739480.2754667}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a genetic algorithm for a LEGO(R) brick layout problem. The task is to build a given 3D object with LEGO(R) bricks. A brick layout is modeled as a solution to a combinatorial optimization problem, through intermediate voxelization, which tries to maximize the connectivity and then minimize the number of used bricks. We attack the problem in the context of genetic search. The proposed randomized greedy algorithm produces initial solutions, and the solutions are effectively improved by an evolutionary process. New domain-specific methods are proposed as well, which include a random boundary mutation and a thickening approach. We tested our algorithm on various objects collected from the web. Experimental results showed that the algorithm produces efficient, and mostly optimal solutions for benchmark models. Unlike some previous works, our algorithm is not limited to assemble few specific objects, but it can deal with diverse kind of objects. To the best of our knowledge, this is the most extensive empirical study on the problem.}, notes = {Also known as \cite{2754667} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Luckehe:2015:GECCO, author = {Daniel Lueckehe and Markus Wagner and Oliver Kramer}, title = {On Evolutionary Approaches to Wind Turbine Placement with Geo-Constraints}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1223--1230}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754690}, doi = {doi:10.1145/2739480.2754690}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Wind turbine placement, i.e., the geographical planning of wind turbine locations, is an important first step to an efficient integration of wind energy. The turbine placement problem becomes a difficult optimization problem due to varying wind distributions at different locations and due to the mutual interference in the wind field known as wake effect. Artificial and environmental geological constraints make the optimization problem even more difficult to solve. In our paper, we focus on the evolutionary turbine placement based on an enhanced wake effect model fed with real-world wind distributions. We model geo-constraints with real-world data from OpenStreetMap. Besides the realistic modeling of wakes and geo-constraints, the focus of the paper is on the comparison of various evolutionary optimization approaches. We propose four variants of evolution strategies with turbine-oriented mutation operators and compare to state-of-the-art optimizers like the CMA-ES in a detailed experimental analysis on three benchmark scenarios.}, notes = {Also known as \cite{2754690} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Luong:2015:GECCO, author = {Ngoc Hoang Luong and Han {La Poutre} and Peter A.N. Bosman}, title = {Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1231--1238}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754682}, doi = {doi:10.1145/2739480.2754682}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. We compare two evolutionary algorithms (EAs) for optimizing expansion plans: the classic genetic algorithm (GA) with uniform crossover and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) that learns and exploits linkage information between problem variables. We study the impact of incorporating different levels of problem-specific knowledge in the variation operators as well as two constraint-handling techniques: constraint domination and repair mechanisms. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions to the DNEP problem. GOMEA is found to have far more robust performance even when an out-of-box variant is used that doesn't exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for real-world applications like DNEP, EAs that have the ability to model and exploit problem structures, such as GOMEAs and estimation-of-distribution algorithms, should be given priority, especially when problem-specific knowledge is not straightforward to exploit, e.g. in the case of black-box optimization.}, notes = {Also known as \cite{2754682} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Michalak:2015:GECCO, author = {Krzysztof Michalak}, title = {Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1239--1246}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754645}, doi = {doi:10.1145/2739480.2754645}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multiobjective optimization of portfolios aims at finding sets of stocks which are expected to provide a possibly high return while retaining a moderate level of risk. The Pareto front of portfolios generated by the optimization algorithm represents attainable trade-offs between returns obtained by the portfolios and the level of risk involved in the investment. This paper studies the relationship between location of portfolios in the Pareto front and future returns of these portfolios. It is observed that the highest future returns can be obtained for the portfolios with the highest return and risk measures observed in the past but also for those with the lowest return and risk in the Pareto front. Neither constantly selecting portfolios with high return on historical data nor, conversely, those with low historical risk (but also low return) yields high future returns. Based on these observations a method is proposed for adaptively selecting the best portfolios for investment from solutions contained in the Pareto front. The proposed method selects high-return (but also high-risk) portfolios or low-risk (but also low-return) portfolios based on the behaviour of the stock marked index in the time period preceding the moment of investment. The proposed method outperforms both the strategy of always selecting the portfolios with the highest return from the past and the risk-averse strategy of selecting portfolios with low risk measure.}, notes = {Also known as \cite{2754645} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Molka:2015:GECCO, author = {Thomas Molka and David Redlich and Marc Drobek and Xiao-Jun Zeng and Wasif Gilani}, title = {Diversity Guided Evolutionary Mining of Hierarchical Process Models}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1247--1254}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754765}, doi = {doi:10.1145/2739480.2754765}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Easy-to-understand and up-to-date models of business processes are important for enterprises, as they aim to describe how work is executed in reality and provide a starting point for process analysis and optimization. With an increasing amount of event data logged by information systems today, the automatic discovery of process models from process logs has become possible. Whereas most existing techniques focus on the discovery of well-formalized models (e.g. Petri nets) which are popular among researchers, business analysts prefer business domain-specific models (such as Business Process Model Notation, BPMN) which are not well formally specified. We present and evaluate an approach for discovering the latter type of process models by formally specifying a hierarchical view on business process models and applying an evolution strategy on it. The evolution strategy efficiently finds process models which best represent a given event log by using fast methods for process model conformance checking, and is partly guided by the diversity of the process model population. The approach contributes to the field of evolutionary algorithms by showing that they can be successfully applied in the real-world use case of process discovery, and contributes to the process discovery domain by providing a promising alternative to existing methods.}, notes = {Also known as \cite{2754765} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Pirpinia:2015:GECCO, author = {Kleopatra Pirpinia and Tanja Alderliesten and Jan-Jakob Sonke and Marcel {van Herk} and Peter A.N. Bosman}, title = {Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1255--1262}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754719}, doi = {doi:10.1145/2739480.2754719}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registration (DIR). A key question is to which extent gradient techniques can also play a role in a multi-objective approach to DIR. We therefore aim to exploit gradient information within an evolutionary-algorithm-based multi-objective optimization framework for DIR. Although an analytical description of the multi-objective gradient (the set of all Pareto-optimal improving directions) is available, it is nontrivial how to best choose the most appropriate direction per solution because these directions are not necessarily uniformly distributed in objective space. To address this, we employ a Monte-Carlo method to obtain a discrete, spatially-uniformly distributed approximation of the set of Pareto-optimal improving directions. We then apply a diversification technique in which each solution is associated with a unique direction from this set based on its multi- as well as single-objective rank. To assess its utility, we compare a state-of-the-art multi-objective evolutionary algorithm with three different hybrid versions thereof on several benchmark problems and two medical DIR problems. Results show that the diversification strategy successfully leads to unbiased improvement, helping an adaptive hybrid scheme solve all problems, but the evolutionary algorithm remains the most powerful optimization method, providing the best balance between proximity and diversity.}, notes = {Also known as \cite{2754719} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Reynoso-Meza:2015:GECCO, author = {Gilberto Reynoso-Meza and Leandro {dos Santos Coelho} and Roberto Z. Freite}, title = {Efficient Sampling of PI Controllers in Evolutionary Multiobjective Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1263--1270}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754807}, doi = {doi:10.1145/2739480.2754807}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Proportional-Integral (PI) controllers remain as a practical and reliable solution for multivariable control for several industrial applications. Efforts to develop new tuning techniques fulfilling several performance indicators and guaranteeing robustness are worthwhile. Evolutionary multiobjective optimization (EMO) has been used for multivariable PI controller tuning, due to their flexibility and its advantages to depict the trade off among conflicting objectives. It is a regular practice bounding the search space as a hyperbox; nevertheless, the shape of the feasible space of PI parameters which are internally stable for a given control loop is irregular. Therefore, such hyperbox could enclose feasible and unfeasible solutions or contain a subset of the feasible set. In the former case, convergence capabilities of an algorithm could be compromised; in the latter case, search space is not fully explored. In this work, a coding mechanism is proposed in order to explore more efficiently the PI parameters feasible set (that is, all feasible solutions and only feasible solutions) in EMO. With the example provided, the advantages to approximate a Pareto front for 2, 3 and 5 objectives are shown, validating the mechanism as useful for EMO in multivariable PI controller tuning.}, notes = {Also known as \cite{2754807} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Schlottfeldt:2015:GECCO, author = {Shana Schlottfeldt and Maria Emilia M.T. Walter and Jon Timmis and Andre C.P.L.F. Carvalho and Mariana P.C. Telles and Jose Alexandre F. Diniz-Filho}, title = {Using Multi-Objective Artificial Immune Systems to Find Core Collections Based on Molecular Markers}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1271--1278}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754653}, doi = {doi:10.1145/2739480.2754653}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Germplasm collections are an important strategy for conservation of diversity, a challenge in ecoinformatics. It is common to select a core to represent the genetic diversity of a germplasm collection, aiming to minimize the costs of conservation, while ensuring the maximization of genetic variation. For the problem of finding a core for a germplasm collection, we proposed the use of a constrained multi-objective artificial immune algorithm (MAIS), based on principles of systematic conservation planning (SCP), and incorporating heterozygosity information. Therefore, optimization takes genotypic diversity and variability patterns into account. As a case study, we used Dipteryx alata molecular marker information. We were able to identify within several accessions, the exact entries that should be chosen to preserve species diversity. MAIS presented better performance measure results when compared to NSGA-II. The proposed approach can be used to help construct cores with maximal genetic richness, and also be extended to in situ conservation. As far as we know, this is the first time that an AIS algorithm is applied to the problem of finding a core for a germplasm collection using heterozygosity information as well.}, notes = {Also known as \cite{2754653} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Stanislawska:2015:GECCO, author = {Karolina Stanislawska and Krzysztof Krawiec and Timo Vihma}, title = {Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1279--1286}, keywords = {genetic algorithms, genetic programming, Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754675}, doi = {doi:10.1145/2739480.2754675}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Earth surface and atmosphere exchange heat via turbulent fluxes. An accurate description of the heat exchange is essential in modelling the weather and climate. In these models the heat fluxes are described applying the Monin-Obukhov similarity theory, where the flux depends on the air-surface temperature difference and wind speed. The theory makes idealized assumptions and the resulting estimates often have large errors. This is the case particularly in conditions when the air is warmer than the Earth surface, i.e., the atmospheric boundary layer is stably stratified, and turbulence is therefore weak. This is a common situation over snow and ice in the Arctic and Antarctic. In this paper, we present alternative models for heat flux estimation evolved by means of genetic programming (GP). To this aim, we use the best heat flux data collected in the Arctic and Antarctic sea ice zones. We obtain GP models that are more accurate, robust, and conceptually novel from the viewpoint of meteorology. Contrary to the Monin-Obukhov theory, the GP equations are not solely based on the air-surface temperature difference and wind speed, but include also radiative fluxes that improve the performance of the method. These results open the door to a new class of approaches to heat flux prediction with potential applications in weather and climate models.}, notes = {Also known as \cite{2754675} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Stolfi:2015:GECCO, author = {Daniel H. Stolfi and Enrique Alba}, title = {Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1287--1294}, keywords = {Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754742}, doi = {doi:10.1145/2739480.2754742}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels.}, notes = {Also known as \cite{2754742} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{YousefiZowj:2015:GECCO, author = {Afsoon {Yousefi Zowj} and Josh C. Bongard and Christian Skalka}, title = {A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1295--1302}, keywords = {genetic algorithms, genetic programming, Real World Applications}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754751}, doi = {doi:10.1145/2739480.2754751}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Resource constrained sensor systems are an increasingly attractive option in a variety of environmental monitoring domains, due to continued improvements in sensor technology. However, sensors for the same measurement application can differ in terms of cost and accuracy, while fluctuations in environmental conditions can impact both application requirements and available energy. This raises the problem of automatically controlling heterogeneous sensor suites in resource constrained sensor system applications, in a manner that balances cost and accuracy of available sensors. We present a method that employs a hierarchy of model ensembles trained by genetic programming (GP): if model ensembles that poll low-cost sensors exhibit too much prediction uncertainty, they automatically transfer the burden of prediction to other GP-trained model ensembles that poll more expensive and accurate sensors. We show that, for increasingly challenging datasets, this hierarchical approach makes predictions with equivalent accuracy yet lower cost than a similar yet non-hierarchical method in which a single GP-generated model determines which sensors to poll at any given time. Our results thus show that a hierarchy of GP-trained ensembles can serve as a control algorithm for heterogeneous sensor suites in resource constrained sensor system applications that balances cost and accuracy.}, notes = {Also known as \cite{2754751} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Assuncc:2015:GECCO, author = {Wesley K.G. Assuncao and Roberto E. Lopez-Herrejon and Lukas Linsbauer and Silvia R. Vergilio and Alexander Egyed}, title = {Extracting Variability-Safe Feature Models from Source Code Dependencies in System Variants}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1303--1310}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754720}, doi = {doi:10.1145/2739480.2754720}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To effectively cope with increasing customization demands, companies that have developed variants of software systems are faced with the challenge of consolidating all the variants into a Software Product Line, a proven development paradigm capable of handling such demands. A crucial step in this challenge is to reverse engineer feature models that capture all the required feature combinations of each system variant. Current research has explored this task using propositional logic, natural language, and search-based techniques. However, using knowledge from the implementation artifacts for the reverse engineering task has not been studied. We propose a multi-objective approach that not only uses standard precision and recall metrics for the combinations of features but that also considers variability-safety, i.e. the property that, based on structural dependencies among elements of implementation artifacts, asserts whether all feature combinations of a feature model are in fact well-formed software systems. We evaluate our approach with five case studies and highlight its benefits for the software engineer.}, notes = {Also known as \cite{2754720} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Belluz:2015:GECCO, author = {Jany Belluz and Marco Gaudesi and Giovanni Squillero and Alberto Tonda}, title = {Operator Selection using Improved Dynamic Multi-Armed Bandit}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1311--1317}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754712}, doi = {doi:10.1145/2739480.2754712}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms greatly benefit from an optimal application of the different genetic operators during the optimization process: thus, it is not surprising that several research lines in literature deal with the self-adapting of activation probabilities for operators. The current state of the art revolves around the use of the Multi-Armed Bandit (MAB) and Dynamic Multi-Armed bandit (D-MAB) paradigms, that modify the selection mechanism based on the rewards of the different operators. Such methodologies, however, update the probabilities after each operator's application, creating possible issues with positive feedbacks and impairing parallel evaluations, one of the strongest advantages of evolutionary computation in an industrial perspective. Moreover, D-MAB techniques often rely upon measurements of population diversity, that might not be applicable to all real-world scenarios. In this paper, we propose a generalization of the D-MAB approach, paired with a simple mechanism for operator management, that aims at removing several limitations of other D-MAB strategies, allowing for parallel evaluations and self-adaptive parameter tuning. Experimental results show that the approach is particularly effective with frameworks containing many different operators, even when some of them are ill-suited for the problem at hand, or are sporadically failing, as it commonly happens in the real world.}, notes = {Also known as \cite{2754712} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bossek:2015:GECCO, author = {Jakob Bossek and Bernd Bischl and Tobias Wagner and Guenter Rudolph}, title = {Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1319--1326}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754673}, doi = {doi:10.1145/2739480.2754673}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms' performance. Since the manual exploration of the parameter space is tedious -- even for few parameters -- several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.}, notes = {Also known as \cite{2754673} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Bruce:2015:GECCO, author = {Bobby R. Bruce and Justyna Petke and Mark Harman}, title = {Reducing Energy Consumption Using Genetic Improvement}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1327--1334}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754752}, doi = {doi:10.1145/2739480.2754752}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software's non-functional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program's execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25percent}, notes = {Also known as \cite{2754752} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCOb, author = {Benjamin Doerr and Carola Doerr}, title = {Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1335--1342}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754684}, doi = {doi:10.1145/2739480.2754684}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {While evolutionary algorithms are known to be very successful for a broad range of applications, the algorithm designer is often left with many algorithmic choices, for example, the size of the population, the mutation rates, and the crossover rates of the algorithm. These parameters are known to have a crucial influence on the optimization time, and thus need to be chosen carefully, a task that often requires substantial efforts. Moreover, the optimal parameters can change during the optimization process. It is therefore of great interest to design mechanisms that dynamically choose best-possible parameters. An example for such an update mechanism is the one-fifth success rule for step-size adaption in evolutionary strategies. While in continuous domains this principle is well understood also from a mathematical point of view, no comparable theory is available for problems in discrete domains. In this work we show that the one-fifth success rule can be effective also in discrete settings. We regard the (1+(lambda,lambda)) GA proposed in [Doerr/Doerr/Ebel: From black-box complexity to designing new genetic algorithms, TCS 2015]. We prove that if its population size is chosen according to the one-fifth success rule then the expected optimization time on OneMax is linear. This is better than what any static population size lambda can achieve and is asymptotically optimal also among all adaptive parameter choices.}, notes = {Also known as \cite{2754684} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Guizzo:2015:GECCO, author = {Giovani Guizzo and Gian Mauricio Fritsche and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo}, title = {A Hyper-Heuristic for the Multi-Objective Integration and Test Order Problem}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1343--1350}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754725}, doi = {doi:10.1145/2739480.2754725}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective evolutionary algorithms (MOEAs) have been efficiently applied to Search-Based Software Engineering (SBSE) problems. However, skilled software engineers waste significant effort designing such algorithms for a particular problem, adapting them, selecting operators and configuring parameters. Hyper-heuristics can help in these tasks by dynamically selecting or creating heuristics. Despite of such advantages, we observe a lack of works regarding this subject in the SBSE field. Considering this fact, this work introduces HITO, a Hyper-heuristic for the Integration and Test Order Problem. It includes a set of well-defined steps and is based on two selection functions (Choice Function and Multi-armed Bandit) to select the best low-level heuristic (combination of mutation and crossover operators) in each mating. To perform the selection, a quality measure is proposed to assess the performance of low-level heuristics throughout the evolutionary process. HITO was implemented using NSGA-II and evaluated to solve the integration and test order problem in seven systems. The introduced hyper-heuristic obtained the best results for all systems, when compared to a traditional algorithm.}, notes = {Also known as \cite{2754725} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Ouni:2015:GECCO, author = {Ali Ouni and Raula {Gaikovina Kula} and Marouane Kessentini and Katsuro Inoue}, title = {Web Service Antipatterns Detection Using Genetic Programming}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1351--1358}, keywords = {genetic algorithms, genetic programming, Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754724}, doi = {doi:10.1145/2739480.2754724}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Service-Oriented Architecture (SOA) is an emerging paradigm that has radically changed the way software applications are architected, designed and implemented. SOA allows developers to structure their systems as a set of ready-made, reusable and compostable services. The leading technology used today for implementing SOA is Web Services. Indeed, like all software, Web services are prone to change constantly to add new user requirements or to adapt to environment changes. Poorly planned changes may risk introducing antipatterns into the system. Consequently, this may ultimately leads to a degradation of software quality, evident by poor quality of service (QoS). In this paper, we introduce an automated approach to detect Web service antipatterns using genetic programming. Our approach consists of using knowledge from real-world examples of Web service antipatterns to generate detection rules based on combinations of metrics and threshold values. We evaluate our approach on a benchmark of 310 Web services and a variety of five types of Web service antipatterns. The statistical analysis of the obtained results provides evidence that our approach is efficient to detect most of the existing antipatterns with a score of 85percent of precision and 87percent of recall.}, notes = {Also known as \cite{2754724} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Poulding:2015:GECCO, author = {Simon Poulding and Robert Feldt}, title = {Heuristic Model Checking using a Monte-Carlo Tree Search Algorithm}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1359--1366}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754767}, doi = {doi:10.1145/2739480.2754767}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Monte-Carlo Tree Search algorithms have proven extremely effective at playing games that were once thought to be difficult for AI techniques owing to the very large number of possible game states. The key feature of these algorithms is that rather than exhaustively searching game states, the algorithm navigates the tree using information returned from a relatively small number of random game simulations. A practical limitation of software model checking is the very large number of states that a model can take. Motivated by an analogy between exploring game states and checking model states, we propose that Monte-Carlo Tree Search algorithms might also be applied in this domain to efficiently navigate the model state space with the objective of finding counterexamples which correspond to potential software faults. We describe such an approach based on Nested Monte-Carlo Search---a tree search algorithm applicable to single player games---and compare its efficiency to traditional heuristic search algorithms when using Java PathFinder to locate deadlocks in 12 Java programs.}, notes = {Also known as \cite{2754767} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Shamshiri:2015:GECCO, author = {Sina Shamshiri and Jose Miguel Rojas and Gordon Fraser and Phil McMinn}, title = {Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1367--1374}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754696}, doi = {doi:10.1145/2739480.2754696}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Achieving high structural coverage is an important aim in software testing. Several search-based techniques have proved successful at automatically generating tests that achieve high coverage. However, despite the well established arguments behind using evolutionary search algorithms (e.g., genetic algorithms) in preference to random search, it remains an open question whether the benefits can actually be observed in practice when generating unit test suites for object-oriented classes. In this paper, we report an empirical study on the effects of using a genetic algorithm (GA) to generate test suites over generating test suites incrementally with random search, by applying the EvoSuite unit test suite generator to 1,000 classes randomly selected from the SF110 corpus of open source projects. Surprisingly, the results show little difference between the coverage achieved by test suites generated with evolutionary search compared to those generated using random search. A detailed analysis reveals that the genetic algorithm covers more branches of the type where standard fitness functions provide guidance. In practice, however, we observed that the vast majority of branches in the analyzed projects provide no such guidance.}, notes = {Also known as \cite{2754696} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Wu:2015:GECCO, author = {Fan Wu and Westley Weimer and Mark Harman and Yue Jia and Jens Krinke}, title = {Deep Parameter Optimisation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1375--1382}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754648}, doi = {doi:10.1145/2739480.2754648}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce a mutation-based approach to automatically discover and expose deep (previously unavailable) parameters that affect a program's runtime costs. These discovered parameters, together with existing (shallow) parameters, form a search space that we tune using search-based optimisation in a bi-objective formulation that optimises both time and memory consumption. We implemented our approach and evaluated it on four real-world programs. The results show that we can improve execution time by 12percent or achieve a 21percent memory consumption reduction in the best cases. In three subjects, our deep parameter tuning results in a significant improvement over the baseline of shallow parameter tuning, demonstrating the potential value of our deep parameter extraction approach.}, notes = {Also known as \cite{2754648} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Zhang:2015:GECCO, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, title = {SPRINT Multi-Objective Model Racing}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1383--1390}, keywords = {Search-Based Software Engineering and Self-* Search}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754791}, doi = {doi:10.1145/2739480.2754791}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective model selection, which is an important aspect of Machine Learning, refers to the problem of identifying a set of Pareto optimal models from a given ensemble of models. This paper proposes SPRINT-Race, a multi-objective racing algorithm based on the Sequential Probability Ratio Test with an Indifference Zone. In SPRINT-Race, a non-parametric ternary-decision sequential analogue of the sign test is adopted to identify pair-wise dominance and non-dominance relationship. In addition, a Bonferroni approach is employed to control the overall probability of any erroneous decisions. In the fixed confidence setting, SPRINT-Race tries to minimize the computational effort needed to achieve a predefined confidence about the quality of the returned models. The efficiency of SPRINT-Race is analyzed on artificially-constructed multi-objective model selection problems with known ground-truth. Moreover, SPRINT-Race is applied to identifying the Pareto optimal parameter settings of Ant Colony Optimization algorithms in the context of solving Traveling Salesman Problems. The experimental results confirm the advantages of SPRINT-Race for multi-objective model selection.}, notes = {Also known as \cite{2754791} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Colby:2015:GECCO, author = {Mitchell Colby and Kagan Tumer}, title = {An Evolutionary Game Theoretic Analysis of Difference Evaluation Functions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1391--1398}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754770}, doi = {doi:10.1145/2739480.2754770}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the key difficulties in cooperative coevolutionary algorithms is solving the credit assignment problem. Given the performance of a team of agents, it is difficult to determine the effectiveness of each agent in the system. One solution to solving the credit assignment problem is the difference evaluation function, which has produced excellent results in many multiagent coordination domains, and exhibits the desirable theoretical properties of alignment and sensitivity. However, to date, there has been no prescriptive theoretical analysis deriving conditions under which difference evaluations improve the probability of selecting optimal actions. In this paper, we derive such conditions. Further, we prove that difference evaluations do not alter the Nash equilibria locations or the relative ordering of fitness values for each action, meaning that difference evaluations do not typically harm converged system performance in cases where the conditions are not met. We then demonstrate the theoretical findings using an empirical basins of attraction analysis.}, notes = {Also known as \cite{2754770} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Corus:2015:GECCO, author = {Dogan Corus and Jun He and Thomas Jansen and Pietro S. Oliveto and Dirk Sudholt and Christine Zarges}, title = {On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1399--1406}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754799}, doi = {doi:10.1145/2739480.2754799}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1)EA using standard bit mutation it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1)EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We show that an easiest function for the hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator. Nevertheless, by combining the advantages of both operators, the hybrid algorithm has optimal asymptotic performance on both OneMax and MinBlocks.}, notes = {Also known as \cite{2754799} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Dang:2015:GECCOa, author = {Duc-Cuong Dang and Thomas Jansen and Per Kristian Lehre}, title = {Populations can be Essential in Dynamic Optimisation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1407--1414}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754808}, doi = {doi:10.1145/2739480.2754808}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future. However, rigorous, theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases. This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation. The ability of evolutionary algorithms to track optimal solutions is investigated by considering a Hamming ball of optimal points that moves randomly through the search space. It is shown that algorithms based on a single individual are likely to be unable to track the optimum while non-elitist population-based evolutionary algorithms can be able to do so with overwhelmingly high probability. It is shown that this holds for a range of the most commonly used selection mechanisms even without diversity enhancing mechanisms. Appropriate parameter settings to achieve this behaviour are derived for these selection mechanisms.}, notes = {Also known as \cite{2754808} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCOc, author = {Benjamin Doerr and Frank Neumann and Andrew M. Sutton}, title = {Improved Runtime Bounds for the (1+1) EA on Random 3-CNF Formulas Based on Fitness-Distance Correlation}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1415--1422}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754659}, doi = {doi:10.1145/2739480.2754659}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {With this paper, we contribute to the theoretical understanding of randomized search heuristics by investigating their behavior on random 3-SAT instances. We improve the results for the (1+1) EA obtained by Sutton and Neumann [PPSN 2014, 942--951] in three ways. First, we reduce the upper bound by a linear factor and prove that the (1+1) EA obtains optimal solutions in time $O(n \log n)$ with high probability on asymptotically almost all high-density satisfiable 3-CNF formulas. Second, we extend the range of densities for which this bound holds to satisfiable formulas of at least logarithmic density. Finally, we complement these mathematical results with numerical experiments that summarize the behavior of the (1+1) EA on formulas along the density spectrum, and suggest that the implicit constants hidden in our bounds are low. Our proofs are based on analyzing the run of the algorithm by establishing a fitness-distance correlation. This approach might be of independent interest and we are optimistic that it is useful for the analysis of randomized search heuristics in various other settings. To our knowledge, this is the first time that fitness-distance correlation is explicitly used to rigorously prove a performance statement for an evolutionary algorithm.}, notes = {Also known as \cite{2754659} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCOd, author = {Benjamin Doerr and Carola Doerr}, title = {A Tight Runtime Analysis of the (1+({\$\lambda\$}, {\$\lambda\$})) Genetic Algorithm on OneMax}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1423--1430}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754683}, doi = {doi:10.1145/2739480.2754683}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Understanding how crossover works is still one of the big challenges in evolutionary computation research, and making our understanding precise and proven by mathematical means might be an even bigger one. As one of few examples where crossover provably is useful, the (1+(lambda, lambda)) Genetic Algorithm (GA) was proposed recently in [Doerr, Doerr, Ebel. Lessons From the Black-Box: Fast Crossover-Based Genetic Algorithms. TCS 2015]. Using the fitness level method, the expected optimization time on general OneMax functions was analyzed and a O(max{n log(n) / lambda, lambda n}) bound was proven for any offspring population size lambda elementof [1..n]. We improve this work in several ways, leading to sharper bounds and a better understanding of how the use of crossover speeds up the runtime in this algorithm. We first improve the upper bound on the runtime to O(max{n log(n) / lambda, n lambda log log(lambda)/log(lambda)}). This improvement is made possible from observing that in the parallel generation of lambda offspring via crossover (but not mutation), the best of these often is better than the expected value, and hence several fitness levels can be gained in one iteration. We then present the first lower bound for this problem. It matches our upper bound for all values of lambda. This allows to determine the asymptotically optimal value for the population size. It is lambda = Theta(sqrt{log(n) log log(n)/ log log log(n)}), which gives an optimization time of Theta(n sqrt{log(n) log log log(n) / log log(n)}). Hence the improved runtime analysis both gives a runtime guarantee improved by a super-constant factor and yields a better actual runtime (faster by more than a constant factor) by suggesting a better value for the parameter lambda. We finally give a tail bound for the upper tail of the runtime distribution, which shows that the actual runtime exceeds our runtime guarantee by a factor of (1+delta) with probability O((n/lambda2)-delta) only.}, notes = {Also known as \cite{2754683} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Doerr:2015:GECCOe, author = {Carola Doerr and Johannes Lengler}, title = {OneMax in Black-Box Models with Several Restrictions}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1431--1438}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754678}, doi = {doi:10.1145/2739480.2754678}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {As in classical runtime analysis the OneMax problem is the most prominent test problem also in black-box complexity theory. It is known that the unrestricted, the memory-restricted, and the ranking-based black-box complexities of this problem are all of order n/log n, where n denotes the length of the bit strings. The combined memory-restricted ranking-based black-box complexity of OneMax, however, was not known. We show in this work that it is Theta(n) for the smallest possible size bound, that is, for (1+1) black-box algorithms. We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizing OneMax with failure probability o(1). This is quite surprising given that all previously regarded algorithms with o(n log n) runtime on OneMax, in particular the quite natural (1+(lambda,lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution. Also for other settings of mu and lambda we show that the (mu+lambda) elitist memory-restricted ranking-based black-box complexity of OneMax is as small as (an advanced version of) the information-theoretic lower bound. Our result enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n log n) iterations.}, notes = {Also known as \cite{2754678} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Giessen:2015:GECCO, author = {Christian Gie\ssen and Carsten Witt}, title = {Population Size vs. Mutation Strength for the (1+{\$\lambda\$}) EA on OneMax}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1439--1446}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754738}, doi = {doi:10.1145/2739480.2754738}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The (1+1) EA with mutation probability c/n, where c>0 is an arbitrary constant, is studied for the classical OneMax function. Its expected optimization time is analyzed exactly (up to lower order terms) as a function of c and lambda. It turns out that 1/n is the only optimal mutation probability if lambda=o(ln n ln ln n/ln ln ln n), which is the cut-off point for linear mnspeed-up. However, if lambda is above this cut-off point then the standard mutation probability 1/n is no longer the only optimal choice. Instead, the expected number of generations is (up to lower order terms) independent of c, irrespectively of it being less than 1 or greater. The results are obtained by a careful study of order statistics of the binomial distribution and variable drift theorems for upper and lower bounds.}, notes = {Also known as \cite{2754738} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Lissovoi:2015:GECCO, author = {Andrei Lissovoi and Carsten Witt}, title = {On the Utility of Island Models in Dynamic Optimization}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1447--1454}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754734}, doi = {doi:10.1145/2739480.2754734}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A simple island model with lambda islands and migration occurring after every tau iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+lambda) EA if tau=1, i.e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to lambda=O(n1-epsilon), the (1+lambda) EA is still not able to track the optimum of Maze. If the migration interval is increased, the algorithm is able to track the optimum even for logarithmic lambda. Finally, the relationship of tau, lambda, and the ability of the island model to track the optimum is investigated more closely.}, notes = {Also known as \cite{2754734} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } @inproceedings{Paixao:2015:GECCO, author = {Tiago Paixao and Jorge {Perez Heredia} and Dirk Sudholt and Barbora Trubenova}, title = {First Steps Towards a Runtime Comparison of Natural and Artificial Evolution}, booktitle = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = {2015}, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, isbn13 = {978-1-4503-3472-3}, pages = {1455--1462}, keywords = {Theory}, month = {11-15 July}, organisation = {SIGEVO}, address = {Madrid, Spain}, doi = {http://dx.doi.org/10.1145/2739480.2754758}, doi = {doi:10.1145/2739480.2754758}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1)EA. We show that SSWM can have a moderate advantage over the (1+1)EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1)EA by taking advantage of information on the fitness gradient.}, notes = {Also known as \cite{2754758} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, } Generate More BibTeX @proceedings(Silva:2015:GECCO, title = {GECCO '15: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference}, year = 2015, editor = {Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr}, address = {Madrid, Spain}, publisher_address = {New York, NY, USA}, month = {11-15 July}, organisation = {SIGEVO}, keywords = {genetic algorithms, genetic programming, Ant Colony Optimization and Swarm Intelligence, Artificial Immune Systems and Artificial Chemistries, Artificial Life/Robotics/Evolvable Hardware, Biological and Biomedical Applications, Continuous Optimization, Digital Entertainment Technologies and Arts, Evolutionary Combinatorial Optimization and Metaheuristics, Estimation of Distribution Algorithms, Evolutionary Machine Learning, Evolutionary Multiobjective Optimization, Generative and Developmental Systems, Integrative Genetic and Evolutionary Computation, Parallel Evolutionary Systems, Real World Applications, Search-Based Software Engineering and Self-* Search, Theory}, ISBN13 = {978-1-4503-3472-3}, url = {http://dl.acm.org/citation.cfm?id=2739482}, notes = {GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)}, )