%urls added 8 Aug 2013 %WBL 4 Aug 2013 %processed by gecco2013_toc.awk $Revision: 1.39 $ ARGC=3 Fri Aug 02 20:29:22 BST 2013 %1 gecco2013_toc.txt %2 gecco2013.bib %WBL 28 Jul 2020 bugfix @ for gecco_errors.txt from Paul Ortyl Jul 12, 2020 %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Bonyadi:2013:GECCO, author = {Mohammadreza Bonyadi and Xiang Li and Zbigniew Michalewicz}, title = {A hybrid particle swarm with velocity mutation for constraint optimization problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1--8}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463378}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Two approaches for solving numerical continuous domain constrained optimisation problems are proposed and experimented with. The first approach is based on particle swarm optimisation algorithm with a new mutation operator in its velocity updating rule. Also, a gradient mutation is proposed and incorporated into the algorithm. This algorithm uses e-level constraint handling method. The second approach is based on covariance matrix adaptation evolutionary strategy with the same method for handling constraints. It is experimentally shown that the first approach needs less number of function evaluations than the second one to find a feasible solution while the second approach is more effective in optimising the objective value. Thus, a hybrid approach is proposed (third approach) which uses the first approach for locating potentially different feasible solutions and the second approach for further improving the solutions found so far. Also, a multi-swarm mechanism is used in which several instances of the first approach are run to locate potentially different feasible solutions. The proposed hybrid approach is applied to 18 standard constrained optimisation benchmarks with up to 30 dimensions. Comparisons with two other state-of-the-art approaches show that the hybrid approach performs better in terms of finding feasible solutions and minimising the objective function.}, notes = {Also known as \cite{2463378} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Ding:2013:GECCO, author = {Ke Ding and Shaoqiu Zheng and Ying Tan}, title = {A GPU-based parallel fireworks algorithm for optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {9--16}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463377}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Swarm intelligence algorithms have been widely used to solve difficult real world problems in both academic and engineering domains. Thanks to the inherent parallelism, various parallelised swarm intelligence algorithms have been proposed to speed up the optimisation process, especially on the massively parallel processing architecture GPUs. However, conventional swarm intelligence algorithms are usually not designed specifically for the GPU architecture.They neither can fully exploit the tremendous computational power of GPUs nor can extend effectively as the problem scales go large. To address this shortcoming, a novel GPU-based Fireworks Algorithm (GPU-FWA) is proposed in this paper. In order to fully leverage GPUs' high performance, GPU-FWA modified the original FWA so that it is more suitable for the GPU architecture. An implementation of GPU-FWA on the CUDA platform is presented and then tested on a suite of well-known benchmark optimisation problems. We extensively evaluated GPU-FWA and compared it with FWA and PSO, with respect to both running time and solution quality, on a state-of-the-art commodity Fermi GPU.Experimental results demonstrate that GPU-FWA generally outperforms both FWA and PSO, and enjoys a significant speedup as high as 200x, compared to the sequential version of FWA and PSO running on an up-to-date CPU. GPU-FWA also enjoys the advantages of being easy to implement and scalable.}, notes = {Also known as \cite{2463377} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Ferrante:2013:GECCO, author = {Eliseo Ferrante and Du\'{e}\, {n}ez-Guzm\'{a}n, Edgar and Turgut, Ali Emre and Wenseleers, Tom}, title = {GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {17--24}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463385}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we propose GESwarm, a novel tool that can automatically synthesise collective behaviours for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behaviour representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyse, to modify, and to tease apart the inherent principles that lead to the desired collective behaviour. In contrast, our representation is based on completely readable and analysable individual-level rules that lead to a desired collective behaviour. The core of our method is a grammar that can generate a rich variety of collective behaviours. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behaviour against an hand-coded one for performance, scalability and flexibility, showing that collective behaviours evolved with GESwarm can outperform the hand-coded one.}, notes = {Also known as \cite{2463385} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Gong:2013:GECCO, author = {Yue-jiao Gong and Jun Zhang}, title = {Small-world particle swarm optimization with topology adaptation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {25--32}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463381}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Traditional particle swarm optimisation (PSO) algorithms adopt completely regular network as topologies, which may encounter the problems of premature convergence and insufficient efficiency. In order to improve the performance of PSO, this paper proposes a novel topology based on small-world network. Each particle in the swarm interacts with its cohesive neighbours and by chance to communicate with some distant particles via small-world randomisation. In order to improve search diversity, each dimension of the swarm is assigned with a specific network, and the particle is allowed to follow the historical information of different neighbours on different dimensions. Moreover, in the proposed small-world topology, the neighbourhood size and the randomisation probability are adaptively adjusted based on the convergence state of the swarm. By applying the topology adaptation mechanism, the particle swarm is able to balance its exploitation and exploration abilities during the search process. Experiments were conducted on a set of classical benchmark functions. The results verify the effectiveness and high efficiency of the proposed PSO algorithm with adaptive small-world topology when compared with some other PSO variants.}, notes = {Also known as \cite{2463381} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kundu:2013:GECCO, author = {Souvik Kundu and Subhodip Biswas and Swagatam Das and Ponntuthurai N. Suganthan}, title = {Crowding-based local differential evolution with speciation-based memory archive for dynamic multimodal optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {33--40}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463392}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the real world, many problems are multimodal as well as dynamic. Such problems require optimisers which not only locate multiple optima in a single run but also track the changing optima positions in the dynamic environments. In this paper a niching parameter free algorithm is designed which can locate multiple optima in changing environments. The proposed algorithm integrates the crowding concept with a competent Evolutionary Algorithm (EA) called Differential Evolution (DE) for maintaining the multiple peaks in a single run. To avoid the use of niching parameter that requires prior knowledge about the fitness landscape, the authors have used local mutation for searching the solution space. A speciation-based memory archive is integrated for regeneration of population after an environmental change is detected. Experimental analysis is conducted on the Moving Peaks Benchmark problem and the performance of the proposed algorithm is compared with other peer algorithms to highlight the overall effectiveness of our work.}, notes = {Also known as \cite{2463392} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Letendre:2013:GECCO, author = {Kenneth Letendre and Melanie E. Moses}, title = {Synergy in ant foraging strategies: memory and communication alone and in combination}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {41--48}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463389}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Collective foraging is a canonical problem in the study of social insect behaviour, as well as in biologically inspired engineered systems. Pheromone recruitment is a well-studied mechanism by which ants coordinate their foraging. Another mechanism for information use is the memory of individual ants, which allows an ant to return to a site it has previously visited. There is synergy in the use of social and private information: ants with poor private information can follow pheromone trails; while ants with private information can ignore trails and instead rely on memory. We developed an agent-based model of foraging by harvester ants, and optimised the model to maximise foraging rate using genetic algorithms. We found that ants' individual memory provided greater benefit in terms of increased foraging rate than pheromone trails in a variety of food distributions. When the two strategies are used together, they out-perform either strategy alone. We compare the behaviour of these models to observations of harvester ants in the field. We discuss why individual memory is more beneficial in this system than pheromone trails. We suggest that individual memory may be an important addition to ant colony optimisation and swarm robotics systems, and that genetic algorithms may be useful in finding an adaptive balance between individual foraging based on memory and recruitment based on communication.}, notes = {Also known as \cite{2463389} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Marinakis:2013:GECCO, author = {Yannis Marinakis and Magdalene Marinaki}, title = {Combinatorial expanding neighborhood topology particle swarm optimization for the vehicle routing problem with stochastic demands}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {49--56}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463375}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces a new algorithmic nature inspired approach that uses Particle Swarm Optimisation (PSO) with different neighbourhood topologies for successfully solving one of the most computationally complex problems, the Vehicle Routing Problem with Stochastic Demands. The proposed method (the Combinatorial Expanding Neighbourhood Topology Particle Swarm Optimisation (CENTPSO)) by using an expanding neighbourhood topology manages to increase the performance of the algorithm. The algorithm starts from a small size neighbourhood. In each iteration the size of the neighbourhood is increased and it ends to a neighbourhood that includes all the swarm. By doing this, it manages to take advantage of the exploration abilities of a global neighbourhood structure and of the exploitation abilities of a local neighbourhood structure. A different way is proposed to calculate the position of each particle which will not lead to any loose of information and will speed up the whole procedure. This is achieved by a replacement of the equation of positions with a novel procedure that includes a Path Relinking Strategy and by a different role of the velocities of the particles. The algorithm is tested on a set of benchmark instances from the literature finding new best solutions in 27 of 40 instances.}, notes = {Also known as \cite{2463375} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Mora:2013:GECCO, author = {Antonio M. Mora and Pablo Garc\'{\i}a-S\'{a}nchez and Juan J. Merelo and Pedro \'{A}. Castillo}, title = {Migration study on a pareto-based island model for MOACOAs}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {57--64}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463390}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimisation algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighbourhood migration topology, and a constant migration rate. In the present work two additional neighbourhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighbourhood migration topology yields the best results.}, notes = {Also known as \cite{2463390} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Moritz:2013:GECCO, author = {Ruby LV Moritz and Enrico Reich and Maik Schwarz and Matthias Bernt and Martin Middendorf}, title = {Refined ranking relations for multi objective optimization andapplication to P-ACO}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {65--72}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463388}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Two new ranking methods for solutions of multi objective optimisation problems are proposed in this paper. Theoretical results show that both new ranking methods form a total preorder and are refinements of the Pareto dominance relation. These properties make the ranking methods suitable for the selection of a subset of good solutions from a set of non-dominated solutions as needed by meta-heuristics. In particular, this is shown experimentally for a Population-based ACO that uses the ranking methods to solve a multi objective flow shop problem.}, notes = {Also known as \cite{2463388} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Otero:2013:GECCO, author = {Fernando E.B. Otero and Alex A. Freitas}, title = {Improving the interpretability of classification rules discovered by an ant colony algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {73--80}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463382}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The vast majority of Ant Colony Optimisation (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules) i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivation for discovering a set of rules is to improve the interpretation of individual rules and evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms and the cAnt-MinerPB producing ordered rules are also presented.}, notes = {Also known as \cite{2463382} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Rada-Vilela:2013:GECCO, author = {Juan Rada-Vilela and Mengjie Zhang and Mark Johnston}, title = {Optimal computing budget allocation in particle swarm optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {81--88}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463373}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle Swarm Optimisation (PSO) is a population-based algorithm whose performance deteriorates in optimisation problems subject to noise. An approach to mitigate the effect of noise is to incorporate resampling methods to evaluate the solutions multiple times and hence estimate better their objective values. The state of the art incorporates a resampling method named Optimal Computing Budget Allocation (OCBA) in order to improve the accuracy of the estimated best solutions. However, the state of the art spends over 95percent of the function evaluations on OCBA while the remaining ones are left for PSO to find better solutions. In this paper, we investigate different distributions such that fewer function evaluations are spent on resampling and more on searching. Moreover, we develop a new algorithm in which the function evaluations spent on resampling are further used to provide a more robust updating mechanism in PSO via hypothesis testing. Experiments on large-scale function optimisation problems with multiplicative Gaussian noise show that our approach has a better overall performance than the state of the art when resampling every two or more iterations. However, the state of the art finds the best solutions when resampling every iteration.}, notes = {Also known as \cite{2463373} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Ren:2013:GECCO, author = {Zhigang Ren and Muyi Wang and Jie Wu}, title = {A novel multimodal-problem-oriented particle swarm optimization algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {89--96}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463391}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present a novel particle swarm optimisation (PSO) variant named scatter learning PSO algorithm (SLPSOA) for solving multimodal problems. SLPSOA takes full account of the distribution information of exemplars while following the basic framework of PSO. It constructs an exemplar pool (EP) which is composed of a certain number of relatively high-quality solutions scattering in the solution space, and allows each particle to select a solution from EP as the exemplar using the roulette wheel rule, with the aim of leading the particles to promising solution regions. In addition, SLPSOA employs Solis and Wets? algorithm as a local searcher to enhance its fine search ability in the newly found solution regions. SLPSOA was tested on 16 benchmark functions, and compared with five existing typical PSO algorithms. Computational results demonstrate that it can manage to prevent premature convergence and produce competitive solutions.}, notes = {Also known as \cite{2463391} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Serpell:2013:GECCO, author = {Martin Serpell and James Smith}, title = {Initial application of ant colony optimisation to statistical disclosure control}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {97--104}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463386}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper Ant Colony Optimisation (ACO) is applied in the field of Statistical Disclosure Control (SDC) for the first time. It has been applied to a permutation problem found in Cell Suppression. ACO has successfully improved the suppression patterns created to protect published statistical tables but when compared to using the Genetic Algorithm (GA) it has not performed as well. It has however performed well enough to merit further investigation into its use in SDC. In particular research into how to construct a distance matrix for the Cell Suppression Problem (CSP) may both improve the performance of ACO when it is applied in that field and provide further insight into SDC.}, notes = {Also known as \cite{2463386} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Souravlias:2013:GECCO, author = {Dimitris Souravlias and Konstantinos E. Parsopoulos}, title = {Particle swarm optimization with budget allocation through neighborhood ranking}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {105--112}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463379}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Standard Particle Swarm Optimisation (PSO) allocates the total available computational budget, in terms of function evaluations, equally among the particles at each iteration of the algorithm. The present work introduces an alternative, which employs neighbourhood ranking for allocating the computational budget to the particles. The proposed PSO variant favours the particles that belong to more promising neighbourhoods by providing them with more function evaluations than the rest, based on a stochastic neighbourhood selection scheme. Preliminary experimental results on standard test problems reveal that the proposed approach is highly competitive.}, notes = {Also known as \cite{2463379} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Voglis:2013:GECCO, author = {Costas Voglis and Panagiotis E. Hadjidoukas and Konstantinos E. Parsopoulos and Dimitrios G. Papageorgiou and Isaac E. Lagaris}, title = {Adaptive memetic particle swarm optimization with variable local search pool size}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {113--120}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463383}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose an adaptive Memetic Particle Swarm Optimisation algorithm where local search is selected from a pool of different algorithms. The choice of local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of local search. Our study investigates whether the pool size affects the memetic algorithm's performance, as well as the possible benefit of using the adaptive strategy against a baseline static one. For this purpose, we employed the memetic algorithms framework provided in the recent MEMPSODE optimisation software, and tested the proposed algorithms on the Benchmarking Black Box Optimisation (BBOB 2012) test suite. The obtained results lead to a series of useful conclusions.}, notes = {Also known as \cite{2463383} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Volkel:2013:GECCO, author = {Gunnar V\"{o}lkel and Markus Maucher and Hans A. Kestler}, title = {Group-based ant colony optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {121--128}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463387}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce Group-Based Ant Colony Optimisation which uses a parallel construction principle on group-structured solution encodings. We compare the parallel construction method with the classical sequential one. In this context we also perform simulation experiments for the Vehicle Routing Problem with Time Windows using the Solomon [8] and the Homberger & Gehring [5] instances.}, notes = {Also known as \cite{2463387} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Woolard:2013:GECCO, author = {Matthaus Martin Woolard and Jonathan Edward Fieldsend}, title = {On the effect of selection and archiving operators in many-objective particle swarm optimisation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {129--136}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463380}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multi-objective optimisation, however its performance on many-objective optimisation (problems with four or more competing objectives) has been less well examined. Many-objective optimisation is well-known to cause problems for Pareto-based evolutionary optimisers, so it is of interest to see how well PSO copes in this domain, and how non-Pareto quality measures perform when integrated into PSO. Here we compare and contrast the performance of canonical PSO, using a wide range of many-objective quality measures, on a number of different parametrised test functions for up to 20 competing objectives. We examine the use of eight quality measures as selection operators for guides when truncated non-dominated archives of guides are maintained, and as maintenance operators, for choosing which solutions should be maintained as guides from one generation to the next. We find that the Controlling Dominance Area of Solutions approach performs exceptionally well as a quality measure to determine archive membership for global and local guides. As a selection operator, the Average Rank and Sum of Ratios measures are found to generally provide the best performance.}, notes = {Also known as \cite{2463380} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Xue:2013:GECCO, author = {Bing Xue and Mengjie Zhang and Yan Dai and Will N. Browne}, title = {PSO for feature construction and binary classification}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {137--144}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463376}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In classification, the quality of the data representation significantly influences the performance of a classification algorithm. Feature construction can improve the data representation by constructing new high-level features. Particle swarm optimisation (PSO) is a powerful search technique, but has never been applied to feature construction. This paper proposes a PSO based feature construction approach (PSOFC) to constructing a single new high-level feature using original low-level features and directly addressing binary classification problems without using any classification algorithm. Experiments have been conducted on seven datasets of varying difficulty. Three classification algorithms (decision trees, naive Bayes, and k-nearest neighbours) are used to evaluate the performance of the constructed feature on test set. Experimental results show that a classification algorithm using the single constructed feature often achieves similar (or even better) classification performance than using all the original features, and in almost all cases, adding the constructed feature to the original features significantly improves its classification performance. In most cases, PSOFC as a classification algorithm (using the constructed feature only) achieves better classification performance than a classification algorithm using all the original features, but needs much less computational cost. This paper represents the first study on using PSO for feature construction in classification.}, notes = {Also known as \cite{2463376} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Yang:2013:GECCO, author = {Ming Yang and Zhihua Cai and Changhe Li and Jing Guan}, title = {An improved adaptive differential evolution algorithm with population adaptation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {145--152}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463374}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In differential evolution (DE), there are many adaptive algorithms proposed for parameters adaptation. However, they mainly aim at tuning the amplification factor F and crossover probability CR. When the population diversity is at a low level or the population becomes stagnant, the population is not able to improve any more. To enhance the performance of DE algorithms, in this paper, we propose a method of population adaptation. The proposed method can identify the moment when the population diversity is poor or the population stagnates by measuring the Euclidean distances between individuals of a population. When the moment is identified, the population will be regenerated to increase diversity or to eliminate the stagnation issue. The population adaptation is incorporated into the jDE algorithm and is tested on a set of 25 scalable CEC05 benchmark functions. The results show that the population adaptation can significantly improve the performance of the jDE algorithm. Even if the population size of jDE is small, the jDE algorithm with population adaptation also has a superior performance in comparisons with several other peer algorithms for high-dimension function optimisation.}, notes = {Also known as \cite{2463374} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Yu:2013:GECCO, author = {Wei-jie Yu and Jun Zhang and Wei-neng Chen}, title = {Adaptive artificial bee colony optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {153--158}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463384}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a novel greedy position update strategy for the ABC algorithm. The greedy position update strategy is implemented mainly in two steps. In the first step, good solutions randomly chosen from the top t solutions in the current population are used to guide the search process of onlooker bees. In the second step, the new parameter t is adaptively adjusted in each iteration of the algorithm. The adjustment is simply based on determining whether the globally best solution is obtained by the employed bees or the onlooker bees. The effect of the proposed greedy position update strategy is evaluated on a set of benchmark functions. Experimental results show that the proposed strategy can significantly improve the performance of the classic ABC algorithm. In addition, ABC using the proposed strategy exhibits very competitive performance when compared with some existing ABC variants.}, notes = {Also known as \cite{2463384} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bongard:2013:GECCO, author = {Josh C. Bongard and Gregory S. Hornby}, title = {Combining fitness-based search and user modeling in evolutionary robotics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {159--166}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2500097}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behaviour optimisation in robotics seems particularly well-suited for this approach because humans have intuitions about how animals---and thus robots---should and should not behave, and can visually detect non-optimal behaviours that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superiour solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.}, notes = {Also known as \cite{2500097} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Cheney:2013:GECCO, author = {Nick Cheney and Robert MacCurdy and Jeff Clune and Hod Lipson}, title = {Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {167--174}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463404}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to have hit a complexity ceiling. One hypothesis for the lack of increased complexity is that most work, including Sims', evolves morphologies composed of rigid elements, such as solid cubes and cylinders, limiting the design space. A second hypothesis is that the encodings of previous work have been overly regular, not allowing complex regularities with variation. Here we test both hypotheses by evolving soft robots with multiple materials and a powerful generative encoding called a compositional pattern-producing network (CPPN). Robots are selected for locomotion speed. We find that CPPNs evolve faster robots than a direct encoding and that the CPPN morphologies appear more natural. We also find that locomotion performance increases as more materials are added, that diversity of form and behaviour can be increased with different cost functions without stifling performance, and that organisms can be evolved at different levels of resolution. These findings suggest the ability of generative soft-voxel systems to scale towards evolving a large diversity of complex, natural, multi-material creatures. Our results suggest that future work that combines the evolution of CPPN-encoded soft, multi-material robots with modern diversity-encouraging techniques could finally enable the creation of creatures far more complex and interesting than those produced by Sims nearly twenty years ago.}, notes = {Also known as \cite{2463404} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Cully:2013:GECCO, author = {Antoine Cully and Jean-Baptiste Mouret}, title = {Behavioral repertoire learning in robotics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {175--182}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463399}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each controller with regard to this task (e.g. walking speed). However, learning advanced, input-driven controllers (e.g. walking in each direction) requires testing each controller on a large sample of the possible input signals. This costly process makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learning technique that generates a behavioural repertoire by taking advantage of the candidate solutions that are usually discarded. Instead of evolving a single, general controller, BR-evolution thus evolves a collection of simple controllers, one for each variant of the target behaviour; to distinguish similar controllers, it uses a performance objective that allows it to produce a collection of diverse but high-performing behaviours. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimises all the achievable behaviours of a robot.}, notes = {Also known as \cite{2463399} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Dick:2013:GECCO, author = {Grant Dick}, title = {A true finite-state baseline for tartarus}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {183--190}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463400}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Tartarus problem is a benchmark problem for non-Markovian decision making. In order to achieve high fitness, individuals must make efficient use of internal state. Finite-state machines are an ideal candidate for exploring the Tartarus problem, and there are several examples from previous work that use a finite-state approach. However, the input space of the Tartarus problem is quite large, so these approaches typically augment the internal states of the finite-state machine with methods to compress the large input space into one of lower dimension. Therefore, the behaviour of a finite-state machine representation that manipulates rules for every possible input is unknown. This paper explores a finite-state machine that manages all 6561 inputs of the Tartarus problem without requiring input space transformation. Far from being ineffective, the results suggest that the evolved FSMs are able to achieve a high level of fitness in a reasonable time frame. Through analysis of the turn-back behaviour of individuals, a simple heuristic is introduced into the representation that further improves fitness.}, notes = {Also known as \cite{2463400} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Dinu:2013:GECCO, author = {Cristian M. Dinu and Plamen Dimitrov and Berend Weel and A. E. Eiben}, title = {Self-adapting fitness evaluation times for on-line evolution of simulated robots}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {191--198}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463405}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper is concerned with on-line evolutionary robotics, where robot controllers are being evolved during a robots' operative time. This approach offers the ability to cope with environmental changes without human intervention, but to be effective it needs an automatic parameter control mechanism to adjust the evolutionary algorithm (EA) appropriately. In particular, mutation step sizes ($\sigma$) and the time spent on fitness evaluation ($\tau$) have a strong influence on the performance of an EA. In this paper, we introduce and experimentally validate a novel method for self-adapting $\tau$ during run time. The results show that this mechanism is viable: the EA using this self-adaptative control scheme consistently shows decent performance without a priori tuning or human intervention during a run.}, notes = {Also known as \cite{2463405} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Gomes:2013:GECCO, author = {Jorge Gomes and Anders L. Christensen}, title = {Generic behaviour similarity measures for evolutionary swarm robotics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {199--206}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463398}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novelty search has shown to be a promising approach for the evolution of controllers for swarms of robots. In existing studies, however, the experimenter had to craft a task-specific behaviour similarity measure. The reliance on hand-crafted similarity measures places an additional burden to the experimenter and introduces a bias in the evolutionary process. In this paper, we propose and compare two generic behaviour similarity measures: combined state count and sampled average state. The proposed measures are based on the values of sensors and effectors recorded for each individual robot of the swarm. The characterisation of the group-level behaviour is then obtained by combining the sensor-effector values from all the robots. We evaluate the proposed measures in an aggregation task and in a resource sharing task. We show that the generic measures match the performance of task-specific measures in terms of solution quality. Our results indicate that the proposed generic measures operate as effective behaviour similarity measures, and that it is possible to leverage the benefits of novelty search without having to craft task-specific similarity measures.}, notes = {Also known as \cite{2463398} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Haasdijk:2013:GECCO, author = {Evert Haasdijk and Berend Weel and A. E. Eiben}, title = {Right on the MONEE: combining task- and environment-driven evolution}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {207--214}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463396}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolution can be employed for two goals. Firstly, to provide a force for adaptation to the environment as it does in nature and in many artificial life implementations, this allows the evolving population to survive. Secondly, evolution can provide a force for optimisation as is mostly seen in evolutionary robotics research, this causes the robots to do something useful. We propose the MONEE algorithmic framework as an approach to combine these two facets of evolution: to combine environment-driven and task-driven evolution. To achieve this, MONEE employs environment-driven and task-based parent selection schemes in parallel. We test this approach in a simulated experimental setting where the robots are tasked to collect two different kinds of puck. MONEE allows the robots to adapt their behaviour to successfully tackle these tasks while ensuring an equitable task distribution at no cost in task performance through a market-based mechanism. In environments that discourage robots performing multiple tasks and in environments where one task is easier than the other, MONEE's market mechanism prevents the population completely focusing on one task.}, notes = {Also known as \cite{2463396} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lehman:2013:GECCO, author = {Joel Lehman and Kenneth O. Stanley and Risto Miikkulainen}, title = {Effective diversity maintenance in deceptive domains}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {215--222}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463393}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Diversity maintenance techniques in evolutionary computation are designed to mitigate the problem of deceptive local optima by encouraging exploration. However, as problems become more difficult, the heuristic of fitness may become increasingly uninformative. Thus, simply encouraging genotypic diversity may fail to much increase the likelihood of evolving a solution. In such cases, diversity needs to be directed towards potentially useful structures. A representative example of such a search process is novelty search, which builds diversity by rewarding behavioural novelty. In this paper the effectiveness of fitness, novelty, and diversity maintenance objectives are compared in two evolutionary robotics domains. In a biped locomotion domain, genotypic diversity maintenance helps evolve biped control policies that travel farther before falling. However, the best method is to optimise a fitness objective and a behavioural novelty objective together. In the more deceptive maze navigation domain, diversity maintenance is ineffective while a novelty objective still increases performance. The conclusion is that while genotypic diversity maintenance works in well-posed domains, a method more directed by phenotypic information, like novelty search, is necessary for highly deceptive ones.}, notes = {Also known as \cite{2463393} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Li:2013:GECCO, author = {Wei Li and Melvin Gauci and Roderich Gross}, title = {A coevolutionary approach to learn animal behavior through controlled interaction}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {223--230}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465801}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a method that allows a machine to infer the behaviour of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behaviour. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.}, notes = {Also known as \cite{2465801} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Medernach:2013:GECCO, author = {David Medernach and Taras Kowaliw and Conor Ryan and Rene Doursat}, title = {Long-term evolutionary dynamics in heterogeneous cellular automata}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {231--238}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463395}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we study open-ended evolution through the analysis of a new model, HetCA, for 'heterogeneous cellular automata'. Striving for simplicity, HetCA is based on classical two-dimensional CA, but differs from them in several key ways: cells include properties of 'age', 'decay', and 'quiescence'; cells use a heterogeneous transition function, one inspired by genetic programming; and there exists a notion of genetic transfer between adjacent cells. The cumulative effect of these changes is the creation of an evolving ecosystem of competing cell colonies. To evaluate the results of our new model, we define a measure of phenotypic diversity on the space of cellular automata. Via this measure, we contrast HetCA to several controls known for their emergent behaviours---homogeneous CA and the Game of Life---and several variants of our model. This analysis demonstrates that HetCA has a capacity for long-term phenotypic dynamics not readily achieved in other models. Runs exceeding one million time steps do not exhibit stagnation or even cyclic behaviour. Further, we show that the design choices are well motivated, as the exclusion of any one of them disrupts the long-term dynamics.}, notes = {Also known as \cite{2463395} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Moore:2013:GECCO, author = {Jared M. Moore and Anthony J. Clark and Philip K. McKinley}, title = {Evolution of station keeping as a response to flows in an aquatic robot}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {239--246}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463402}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Developing complex behaviours for aquatic robots is a difficult engineering challenge due to the uncertainty of an underwater environment. Neuroevolution provides one method of dealing with this type of problem. Artificial neural networks discern different conditions by mapping sensory input to responses, and evolutionary computation provides a training algorithm suitable to the high dimensionality of the problem. In this paper, we present results of applying neuroevolution to an aquatic robot tasked with station keeping, that is, maintaining a given position despite surrounding water flow. The virtual device exposed to evolution is modelled after a physical counterpart that has been fabricated with a 3D printer and tested in physical environments. Evolved behaviours exhibit a variety of unexpected, complex fin/flipper movements that enable the robot to achieve and maintain station, despite water flow from different directions. Moreover, the results show that evolved controllers are able to effectively carry out this task using only information from a simulated accelerometer and gyroscope, matching the inertial measurement unit (IMU) on the actual robot.}, notes = {Also known as \cite{2463402} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Olson:2013:GECCO, author = {Randal S. Olson and David B. Knoester and Christoph Adami}, title = {Critical interplay between density-dependent predation and evolution of the selfish herd}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {247--254}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463394}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Animal grouping behaviours have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favour the evolution of grouping behaviour. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that the predator attack mode plays a critical role in the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of 'domains of danger.' Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work verifies Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalises the domain of danger concept to density-dependent predation.}, notes = {Also known as \cite{2463394} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Risi:2013:GECCO, author = {Sebastian Risi and Kenneth O. Stanley}, title = {Confronting the challenge of learning a flexible neural controller for a diversity of morphologies}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {255--262}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463397}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The ambulatory capabilities of legged robots offer the potential for access to dangerous and uneven terrain without a risk to human life. However, while machine learning has proved to be effective at training such robots to walk, a significant limitation of such approaches is that controllers trained for a specific robot are likely to fail when transferred to a robot with a slightly different morphology. This paper confronts this challenge with a novel strategy: Instead of training a controller for a particular quadruped morphology, it evolves a special function (through a method called HyperNEAT) that takes morphology as input and outputs an entire neural network controller fitted to the specific morphology. Once such a relationship is learnt the output controllers are able to work on a diversity of different morphologies. Highlighting the unique potential of such an approach, in this paper a neural controller evolved for three different robot morphologies, which differ in the length of their legs, can interpolate to never-seen intermediate morphologies without any further training. Thus this work suggests a new research path towards learning controllers for whole ranges of morphologies: Instead of learning controllers themselves, it is possible to learn the relationship between morphology and control.}, notes = {Also known as \cite{2463397} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Risi:2013:GECCOa, author = {Sebastian Risi and Daniel Cellucci and Hod Lipson}, title = {Ribosomal robots: evolved designs inspired by protein folding}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {263--270}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463403}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The biological process of ribosomal assembly is one of the most versatile systems in nature. With only a few small building blocks, this natural process is capable of synthesising the multitude of complex chemicals that form the basis of all organic life. This paper presents a robotics design and manufacturing scheme which seeks to capture some of the versatility of the ribosomal process. In this scheme, a custom 'printer' folds a long ribbon of material in which control elements such as motors have been embedded into a morphology that is capable of accomplishing a pre-defined task. The evolved folding patterns are encoded with a special kind of compositional pattern producing network (CPPN), which can compactly describe patterns with regularities such as symmetry, repetition, and repetition with variation. This paper tests the efficacy of this design scheme and the effects of different ribbon lengths on the ability to produce walking robot morphologies. We show that a single strip of material can be folded into a variety of different morphologies displaying different forms of locomotion. Thus, the results presented here suggest a promising new method for the automated design and manufacturing of robotic systems.}, notes = {Also known as \cite{2463403} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chowdhury:2013:GECCO, author = {Ahsan Raja Chowdhury and Madhu Chetty and Nguyen Xuan Vinh}, title = {Inferring large scale genetic networks with S-system model}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {271--278}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463409}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Gene regulatory network (GRN) reconstruction from high-throughput microarray data is an important problem in systems biology. The S-System model, a differential equation based approach, is among the mainstream approaches for modelling GRNs. It has the ability to represent GRNs accurately with precise regulatory weights. However, the current applications of S-System are limited to small and medium scale network, as inferring large network requires inhibitive computational cost. In this paper, we propose a novel S-System based framework to reconstruct biologically relevant GRNs by exploiting their special topological structure. In GRNs, the complex interactions occurring amongst transcription factors (TFs) and target genes (TGs) are unidirectional, i.e., TFs to TGs, and the vice-versa is biologically irrelevant. In addition, TFs can regulate themselves while only self-regulations may exist for TGs. As such, we decompose GRN into two sub-networks representing TF-TF and TF-TG interactions. We learn the sub-networks separately by adapting the traditional S-System model, and combining the solutions to get the entire network. Our experimental studies indicate that the proposed approach can scale up to larger networks, not achievable with other current S-System based approaches, yet with higher accuracy.}, notes = {Also known as \cite{2463409} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Morshed:2013:GECCO, author = {Nizamul Morshed and Madhu Chetty and Nguyen Xuan Vinh and Terry Caelli}, title = {mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {279--286}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463406}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Solutions for deriving the most consistent Bayesian gene regulatory network model from given data sets using evolutionary algorithms typically only result in locally optimal solutions. Further, due to genetic drift, merely increasing the size of the population does not overcome this limitation. In this paper, we propose a two-stage genetic algorithm that systematically searches the whole search space using frequent subgraph mining techniques. The approach finds representative patterns present in different local optimal solutions in the first stage and then combines these frequent subgraphs (motifs) in the second stage to converge to the global optima. We apply the algorithm to both synthetic and real life networks of yeast and E.coli and show the effectiveness of our approach.}, notes = {Also known as \cite{2463406} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Olson:2013:GECCOa, author = {Brian Olson and Kenneth De Jong and Amarda Shehu}, title = {Off-lattice protein structure prediction with homologous crossover}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {287--294}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463407}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ab-initio structure prediction refers to the problem of using only knowledge of the sequence of amino acids in a protein molecule to find spatial arrangements, or conformations, of the amino-acid chain capturing the protein in its biologically-active or native state. This problem is a central challenge in computational biology. It can be posed as an optimisation problem, but current top ab-initio protocols employ Monte Carlo sampling rather than evolutionary algorithms (EAs) for conformational search. This paper presents a hybrid EA that incorporates successful strategies used in state-of-the-art ab-initio protocols. Comparison to a top Monte-Carlo-based sampling method shows that the domain-specific enhancements make the proposed hybrid EA competitive. A detailed analysis on the role of crossover operators and a novel implementation of homologous 1-point crossover shows that the use of crossover with mutation is more effective than mutation alone in navigating the protein energy surface.}, notes = {Also known as \cite{2463407} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Sapin:2013:GECCO, author = {Emmanuel Sapin and Ed Keedwell and Tim Frayling}, title = {Subset-based ant colony optimisation for the discovery of gene-gene interactions in genome wide association studies}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {295--302}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463410}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper an ant colony optimisation approach for the discovery of gene-gene interactions in genome-wide association study (GWAS) data is proposed. The subset-based approach includes a novel encoding mechanism and tournament selection to analyse full scale GWAS data consisting of hundreds of thousands of variables to discover associations between combinations of small DNA changes and Type II diabetes. The method is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover combinations that are statistically significant and biologically relevant within reasonable computational time.}, notes = {Also known as \cite{2463410} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Zaharie:2013:GECCO, author = {Daniela Zaharie and Lavinia Moatar-Moleriu and Viorel Negru}, title = {Particularities of evolutionary parameter estimation in multi-stage compartmental models of thymocyte dynamics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {303--310}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463408}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The aim of this paper is twofold. Firstly, it presents an extension of a multi-stage compartmental model in order to make it more appropriate in modelling various perturbations of thymocyte dynamics.Secondly, it proposes an evolutionary approach, based on the JADE algorithm, for simultaneously estimating the number of division stages, the rates associated to cellular processes (e.g. proliferation, death, migration) and the parameters corresponding to the proposed perturbation functions. Several quality of fit measures are investigated and their relationship with the variability of experimental data is exploited in order to select the optimisation criterion.}, notes = {Also known as \cite{2463408} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Dorin:2013:GECCO, author = {Alan Dorin}, title = {Aesthetic selection and the stochastic basis of art, design and interactive evolutionary computation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {311--318}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463412}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present data demonstrating that the application of interactive evolution and related techniques has been growing since the early 1990s. Much research has honed the technique for specific applications. In this paper, we explicitly consider the interaction between chance and human creative tendencies as exercised by manual selection during interactive evolutionary computation. Since stochastic processes have interacted with dynamical human and technological processes for creative design throughout the history of art, we survey a few pertinent examples as we tackle interactive evolutionary computing specifically. In this context, chance governs the crossover and mutation of genes and therefore ultimately decides which forms will be displayed to a user for consideration. We derive some simple suggestions as to how chance's role may be extended in interactive evolution, demonstrate these in practice, and discuss how such randomness benefits human creativity.}, notes = {Also known as \cite{2463412} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Eigenfeldt:2013:GECCO, author = {Arne Eigenfeldt and Philippe Pasquier}, title = {Evolving structures for electronic dance music}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {319--326}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463415}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present GESMI (Generative Electronica Statistical Modeling Instrument), a software system that generates Electronic Dance Music (EDM) using evolutionary methods. While using machine learning, GESMI rests on a corpus analysed and transcribed by domain experts. We describe a method for generating the overall form of a piece and individual parts, including specific patterns sequences, using evolutionary algorithms. Lastly, we describe how the user can use contextually-relevant target features to query the generated database of strong individual patterns. As our main focus is upon artistic results, our methods themselves use an iterative, somewhat evolutionary, design process based upon our reaction to results.}, notes = {Also known as \cite{2463415} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Eisenmann:2013:GECCO, author = {Jonathan Eisenmann and Matthew Lewis and Rick Parent}, title = {Trace selection for interactive evolutionary algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {327--334}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463414}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a selection method for use with interactive evolutionary algorithms and sensitivity analysis in spatiotemporal domains. Recent work in the field has made it possible to give feedback to an interactive evolutionary system with a finer granularity than the typical wholesale selection method. This recent development allows the user to drive the evolutionary search in a more precise way by allowing him to select a part of a phenotype to indicate fitness. The method has potential to alleviate the human fatigue bottleneck, so it seems ideally suited for use in domains that vary in both space and time, such as character motion or cloth simulation where evaluation times are long. However no evolutionary interface has been developed yet which will allow for selecting parts of time-varying phenotypes. We present a selection interface that should be fast and intuitive enough to minimise the interaction bottleneck in evolutionary algorithms that receive feedback at the phenotype part level.}, notes = {Also known as \cite{2463414} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lessin:2013:GECCO, author = {Dan Lessin and Don Fussell and Risto Miikkulainen}, title = {Open-ended behavioral complexity for evolved virtual creatures}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {335--342}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463411}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the 19 years since Karl Sims' landmark publication on evolving virtual creatures (Sims, 1994), much of the future work he proposed has been implemented, having a significant impact on multiple fields including graphics, evolutionary computation, and artificial life. There has, however been one notable exception to this progress. Despite the potential benefits, there has been no clear increase in the behavioural complexity of evolved virtual creatures (EVCs) beyond the light following demonstrated in Sims' original work. This paper presents an open-ended method to move beyond this limit, making use of high-level human input in the form of a syllabus of intermediate learning tasks--along with mechanisms for preservation, reuse, and combination of previously learnt tasks. This method (named ESP for its three components: encapsulation, syllabus, and pandemonium) is employed to evolve a virtual creature with behavioural complexity that clearly exceeds previously achieved levels. ESP thus demonstrates that EVCs may indeed have the potential to one day rival the behavioural complexity--and therefore the entertainment value--of their non-virtual counterparts.}, notes = {Also known as \cite{2463411} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Liapis:2013:GECCO, author = {Antonios Liapis and Georgios N. Yannakakis and Julian Togelius}, title = {Enhancements to constrained novelty search: two-population novelty search for generating game content}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {343--350}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463416}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.}, notes = {Also known as \cite{2463416} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Perez:2013:GECCO, author = {Diego Perez and Spyridon Samothrakis and Simon Lucas and Philipp Rohlfshagen}, title = {Rolling horizon evolution versus tree search for navigation in single-player real-time games}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {351--358}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463413}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In real-time games, agents have limited time to respond to environmental cues. This requires either a policy defined up-front or, if one has access to a generative model, a very efficient rolling horizon search. In this paper, different search techniques are compared in a simple, yet interesting, real-time game known as the Physical Travelling Salesman Problem (PTSP).We introduce a rolling horizon version of a simple evolutionary algorithm that handles macro-actions and compare it against Monte Carlo Tree Search (MCTS), an approach known to perform well in practice, as well as random search. The experimental setup employs a variety of settings for both the action space of the agent as well as the algorithms used. We show that MCTS is able to handle very fine-grained searches whereas evolution performs better as we move to coarser-grained actions; the choice of algorithm becomes irrelevant if the actions are even more coarse-grained. We conclude that evolutionary algorithms can be a viable and competitive alternative to MCTS.}, notes = {Also known as \cite{2463413} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bosman:2013:GECCO, author = {Peter A.N. Bosman and Dirk Thierens}, title = {More concise and robust linkage learning by filtering and combining linkage hierarchies}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {359--366}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463420}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genepool Optimal Mixing Evolutionary Algorithms (GOMEAs) were recently proposed as a new way of designing linkage-friendly, efficiently-scalable evolutionary algorithms (EAs). GOMEAs combine the building of linkage models with an intensive, greedy mixing procedure. Recent results indicate that the use of hierarchical linkage models in GOMEAs lead to the most robust and efficient performance. Two of such GOMEA instances are the Linkage Tree Genetic Algorithm (LTGA) and the Multi-scale Linkage Neighbours Genetic Algorithm (MLNGA). The linkage models in these GOMEAs have their individual merits and drawbacks. In this paper, we propose enhancement techniques targeted at filtering out superfluous linkage sets from hierarchical linkage models and we consider a way to construct a linkage model that combines the strengths of different linkage models. We then propose a new GOMEA instance, called the Linkage Trees and Neighbours Genetic Algorithm (LTNGA), that combines the models of LTGA and MLNGA. LTNGA performs comparable or better than the best of either LTGA or MLNGA on various problems, including typical linkage benchmark problems and instances of the well-known combinatorial problem MAXCUT, especially when the proposed filtering techniques are used.}, notes = {Also known as \cite{2463420} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chen:2013:GECCO, author = {Wei-Ming Chen and Chu-Yu Hsu and Tian-Li Yu and Wei-Che Chien}, title = {Effects of discrete hill climbing on model building forestimation of distribution algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {367--374}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463418}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hybridisation of global and local searches is a well-known technique for optimisation algorithms. Hill climbing is one of the local search methods. On estimation of distribution algorithms (EDAs), hill climbing strengthens the signals of dependencies on correlated variables and improves the quality of model building, which reduces the required population size and convergence time. However, hill climbing also consumes extra computational time. In this paper, analytical models are developed to investigate the effects of combining two different hill climbers with the extended compact genetic algorithm and the dependency structure matrix genetic algorithm. By using the one-max problem and the 5-bit non-overlapping trap problem as the test problems, the performances of different hill climbers are compared. Both analytical models and experiments reveal that the greedy hill climber reduces the number of function evaluations for EDAs to find the global optimum.}, notes = {Also known as \cite{2463418} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Hsu:2013:GECCO, author = {Po-Chun Hsu and Tian-Li Yu}, title = {A niching scheme for EDAs to reduce spurious dependencies}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {375--382}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463421}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a niching scheme, the dependency structure matrix restricted tournament replacement (DSMRTR). The restricted tournament replacement (RTR) is a well-known niching scheme in the field of estimation of distribution algorithms (EDAs). However, RTR induces spurious dependencies among variables, which impair the performance of EDAs. This paper uses building-block-wise distances to define a new distance metric, the one-niche distance. For those EDAs which provide explicit linkage information, the one-niche distances can be directly incorporated into RTR. For EDAs without such information, DSMRTR constructs a dependency structure matrix via the differential mutual complement to estimate the one-niche distances. Empirical results show that DSMRTR induces fewer spurious dependencies than RTR does while maintaining enough diversity for EDAs.}, notes = {Also known as \cite{2463421} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kaban:2013:GECCO, author = {Ata Kaban and Jakramate Bootkrajang and Robert John Durrant}, title = {Towards large scale continuous EDA: a random matrix theory perspective}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {383--390}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463423}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging and as a result existing EDAs lose their strengths in large scale problems. Large scale continuous global optimisation is key to many real world problems of modern days. Scaling up EAs to large scale problems has become one of the biggest challenges of the field. This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective EDA-type algorithms for large scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections of the set of fittest search points to low dimensions as a basis for developing a new and generic divide-and-conquer methodology. This is rooted in the theory of random projections developed in theoretical computer science, and will exploit recent advances of non-asymptotic random matrix theory.}, notes = {Also known as \cite{2463423} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lanillos:2013:GECCO, author = {Pablo Lanillos and Ya\, {n}ez-Zuluaga, Javier and Ruz, Jos\'{e} Jaime and Besada-Portas, Eva}, title = {A bayesian approach for constrained multi-agent minimum time search in uncertain dynamic domains}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {391--398}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463417}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a Bayesian approach for minimising the time of finding an object of uncertain location and dynamics using several moving sensing agents with constrained dynamics. The approach exploits twice the Bayesian theory: on one hand, it uses a Bayesian formulation of the objective functions that compare the constrained paths of the agents and on the other hand, a Bayesian optimisation algorithm to solve the problem. By combining both elements, our approach handles successfully this complex problem, as illustrated by the results over different scenarios presented and statistically analysed in the paper. Finally, the paper also discusses other formulations of the problem and compares the properties of our approach with others closely related.}, notes = {Also known as \cite{2463417} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Regnier-Coudert:2013:GECCO, author = {Olivier Regnier-Coudert and John McCall and Mayowa Ayodele}, title = {Geometric-based sampling for permutation optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {399--406}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463422}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There exist several operators to search through permutation spaces that can benefit search and score algorithms when combined. This paper presents COMpetitive Mutating Agents (COMMA), an algorithm which uses geometric mutation operators to create a geometrically defined distribution of solutions. Sampling from the distribution generates solutions in a similar fashion as with Estimation of Distribution Algorithms (EDAs). COMMA is applied on classical permutation optimisation benchmarks, namely the Quadratic Assignment and the Permutation Flowshop Scheduling Problems and its performance is compared with those of reference EDAs. Although COMMA does not require a model building step, results suggest that it is competitive with state-of-the-art EDAs. In addition, COMMA's underlying geometric-based sampling could be transposed to representations other than permutations.}, notes = {Also known as \cite{2463422} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wang:2013:GECCO, author = {Shih-Ming Wang and Jie-Wei Wu and Wei-Ming Chen and Tian-Li Yu}, title = {Design of test problems for discrete estimation of distribution algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {407--414}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463419}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Two types of problem structures, overlapping and conflict structures, are challenging for the estimation of distribution algorithms (EDAs) to solve. To test the capabilities of different EDAs of dealing with overlapping and conflict structures, some test problems have been proposed. However, the upper-bound of the degree of overlap and the effect of conflict have not been fully investigated. This paper investigates how to properly define the degree of overlap and the degree of conflict to reflect the difficulties of problems for the EDAs. A new test problem is proposed with the new definitions of the degree of overlap and the degree of conflict. A framework for building the proposed problem is presented, and some model-building genetic algorithms are tested by the problem. This test problem can be applied to further researches on overlapping and conflict structures.}, notes = {Also known as \cite{2463419} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{AitElhara:2013:GECCO, author = {Ouassim Ait Elhara and Anne Auger and Nikolaus Hansen}, title = {A median success rule for non-elitist evolution strategies: study of feasibility}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {415--422}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463429}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Success rule based step-size adaptation, namely the one-fifth success rule, has shown to be effective for single parent evolution strategies (ES), e.g. the (1+1)-ES. The success rule remains feasible in non-elitist single parent strategies, where the target success rate must be roughly inversely proportional to the population size. This success rule is, however, not easily applicable to multi-parent strategies. In this paper, we introduce the median success rule for step-size adaptation, applicable to non-elitist multi-recombinant evolution strategies. The median success rule compares the median fitness of the population to a fitness from the previous iteration. The comparison fitness is chosen to achieve a target success rate of 1/2, thereby a deviation from the target can be measured reliably in comparatively few iteration steps. As a prerequisite for feasibility of the median success rule, we studied the way the fitness comparison quantile depends on the search space dimension, the population size, the parent number, the recombination weights and the objective function. The findings are encouraging: the choice of the comparison quantile appears to be relatively uncritical and experiments on a variety of functions, also in combination with CMA, reveal reasonable behaviour.}, notes = {Also known as \cite{2463429} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Arnold:2013:GECCO, author = {Dirk V. Arnold}, title = {On the behaviour of the (1, {\$\lambda\$})-es for a conically constrained problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {423--430}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463426}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We consider a conically constrained optimisation problem where the optimal solution lies at the apex of the cone and study the behaviour of a (1,lambda)-ES that handles constraints by resampling infeasible candidate solutions. Expressions that describe the strategy's single-step behaviour are derived. Assuming that the mutation strength is adapted in a scale-invariant manner, a simple zeroth-order model is used to determine the speed of convergence of the strategy. We then derive expressions that approximately characterise the step size and convergence rate attained when using cumulative step size adaptation and compare the values with optimal ones.}, notes = {Also known as \cite{2463426} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Glasmachers:2013:GECCO, author = {Tobias Glasmachers}, title = {A natural evolution strategy with asynchronous strategy updates}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {431--438}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463424}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a generic method for turning a modern, non-elitist evolution strategy with fully adaptive covariance matrix into an asynchronous algorithm. This algorithm can process the result of an evaluation of the fitness function anytime and update its search strategy, without the need to synchronise with the rest of the population. The asynchronous update builds on the recent developments of natural evolution strategies and information geometric optimisation. Our algorithm improves on the usual generational scheme in two respects. Remarkably, the possibility to process fitness values immediately results in a speed-up of the sequential algorithm. Furthermore, our algorithm is much better suited for parallel processing. It allows to use more processors than offspring individuals in a meaningful way.}, notes = {Also known as \cite{2463424} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Loshchilov:2013:GECCO, author = {Ilya Loshchilov and Marc Schoenauer and Michele Sebag}, title = {Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {439--446}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463427}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new mechanism for a better exploitation of surrogate models in the framework of Evolution Strategies (ESs). This mechanism is instantiated here on the self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (saACM-ES), a recently proposed surrogate-assisted variant of CMA-ES. As well as in the original saACM-ES, the expensive function is optimised by exploiting the surrogate model, whose hyper-parameters are also optimised online. The main novelty concerns a more intensive exploitation of the surrogate model by using much larger population sizes for its optimisation. The new variant of saACM-ES significantly improves the original saACM-ES and further increases the speed-up compared to the CMA-ES, especially on unimodal functions (e.g., on 20-dimensional Rotated Ellipsoid, saACM-ES is 6 times faster than aCMA-ES and almost by one order of magnitude faster than CMA-ES). The empirical validation on the BBOB-2013 noiseless testbed demonstrates the efficiency and the robustness of the proposed mechanism.}, notes = {Also known as \cite{2463427} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lu:2013:GECCO, author = {Jianfeng Lu and Bin Li and Yaochu Jin}, title = {An evolution strategy assisted by an ensemble of local Gaussian process models}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {447--454}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463425}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Surrogate models used in evolutionary algorithms (EAs) aim to reduce computationally expensive objective function evaluations. However, low-quality surrogates may mislead EAs and as a result, surrogate-assisted EAs may fail to locate the global optimum. Among various machine learning models for surrogates, Gaussian Process (GP) models have shown to be effective as GP models are able to provide fitness estimation as well as a confidence level. One weakness of GP models is that the computational cost for training increases rapidly as the number of training samples increases. To reduce the computational cost for training, here we propose to adopt an ensemble of local Gaussian Process models. Different from independent local Gaussian Process models, local Gaussian Process models share the same model parameters. Then the performance of the covariance matrix adaptation evolution strategy (CMA-ES) assisted by an ensemble of local Gaussian Process models with five different sampling strategies is compared. Experiments on eight benchmark functions demonstrate that ensembles of local Gaussian Process models can provide reliable fitness prediction and uncertainty estimation. Among the compared strategies, the clustering technique using the lower confidence bound sampling strategy exhibits the best global search performance.}, notes = {Also known as \cite{2463425} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Zhabitsky:2013:GECCO, author = {Mikhail Zhabitsky and Evgeniya Zhabitskaya}, title = {Asynchronous differential evolution with adaptive correlation matrix}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {455--462}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463428}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Differential evolution (DE) is an efficient algorithm to solve global optimisation problems. It has a simple internal structure and uses a few control parameters. In this paper we incorporate crossover based on adaptive correlation matrix into Asynchronous differential evolution (ADE). Thanks to the proposed crossover the novel algorithm automatically adapts to the landscape of the optimised objective function. Combined with an adaptive scheme for the mutation scale factor and an automatic inflation of the population size this results in quasi parameter-free algorithm from the user's point of view. The performance of the Asynchronous differential evolution with adaptive correlation matrix is reported on the set of BBOB-2012 benchmark functions.}, notes = {Also known as \cite{2463428} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Andrade:2013:GECCO, author = {Carlos E. Andrade and Fl\'{a}vio K. Miyazawa and Mauricio G.C. Resende}, title = {Evolutionary algorithm for the k-interconnected multi-depot multi-traveling salesmen problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {463--470}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463434}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce the $k$-Interconnected Multi-Depot Multi-Travelling Salesmen Problem, a new problem that resembles some network design and location routing problems but carries the inherent difficulty of not having a fixed set of depots or terminals. We propose a heuristic based on a biased random-key genetic algorithm to solve it. This heuristic uses local search procedures to best choose the terminal vertices and improve the tours of a given solution. We compare our heuristic with a multi-start procedure using the same local improvements and we show that the proposed algorithm is competitive.}, notes = {Also known as \cite{2463434} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Basseur:2013:GECCO, author = {Matthieu Basseur and Adrien Go\"{e}ffon and Arnaud Liefooghe and S\'{e}bastien Verel}, title = {On set-based local search for multiobjective combinatorial optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {471--478}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463430}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we formalise a multiobjective local search paradigm by combining set-based multiobjective optimisation and neighbourhood-based search principles. Approximating the Pareto set of a multiobjective optimisation problem has been recently defined as a set problem, in which the search space is made of all feasible solution-sets. We here introduce a general set-based local search algorithm, explicitly based on a set-domain search space, evaluation function, and neighbourhood relation. Different classes of set-domain neighbourhood structures are proposed, each one leading to a different set-based local search variant. The corresponding methodology generalises and unifies a large number of existing approaches for multiobjective optimisation. Preliminary experiments on multiobjective NK-landscapes with objective correlation validates the ability of the set-based local search principles. Moreover, our investigations shed the light to further research on the efficient exploration of large-size set-domain neighbourhood structures.}, notes = {Also known as \cite{2463430} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Basseur:2013:GECCOa, author = {Matthieu Basseur and Adrien Goeffon}, title = {Hill-climbing strategies on various landscapes: an empirical comparison}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {479--486}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463439}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Climbers constitute a central component of modern heuristics, including metaheuristics, hybrid metaheuristics and hyperheuristics. Several important questions arise while designing a climber, and choices are often arbitrary, intuitive or experimentally decided. The paper provides guidelines to design climbers considering a landscape shape under study. In particular, we aim at competing best improvement and first improvement strategies, as well as evaluating the behaviour of different neutral move policies. Some conclusions are assessed by an empirical analysis on a large variety of landscapes. This leads us to use the NK-landscapes family, which allows to define landscapes of different size, rugosity and neutrality levels. Experiments show the ability of first improvement to explore rugged landscapes, as well as the interest of accepting neutral moves at each step of the search. Moreover, we point out that reducing the precision of a fitness function could help to optimise problems.}, notes = {Also known as \cite{2463439} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Benz:2013:GECCO, author = {Florian Benz and Timo K\"{o}tzing}, title = {An effective heuristic for the smallest grammar problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {487--494}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463441}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The smallest grammar problem is the problem of finding the smallest context-free grammar that generates exactly one given sequence. Approximating the problem with a ratio of less than 8569/8568 is known to be NP-hard. Most work on this problem has focused on finding decent solutions fast (mostly in linear time), rather than on good heuristic algorithms. Inspired by a new perspective on the problem presented by Carrascosa et al.\ (2010), we investigate the performance of different heuristics on the problem. The aim is to find a good solution on large instances by allowing more than linear time. We propose a hybrid of a max-min ant system and a genetic algorithm that in combination with a novel local search outperforms the state of the art on all files of the Canterbury corpus, a standard benchmark suite. Furthermore, this hybrid performs well on a standard DNA corpus.}, notes = {Also known as \cite{2463441} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chalupa:2013:GECCO, author = {David Chalupa}, title = {An analytical investigation of block-based mutation operators for order-based stochastic clique covering algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {495--502}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463436}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We analyse the properties of a recently proposed order-based representation of the NP-hard (vertex) clique covering problem (CCP). In this representation, a permutation of vertices is mapped to a clique covering using greedy clique covering (GCC) and the identified cliques are put into the permutation as blocks. Block-based mutation operators can be then used to improve the clique covering in a stochastic algorithm, which is referred to as iterated greedy (IG). In this paper, we analytically investigate how the block-based mutation operators influence the quality of the solution. We formulate a sufficient condition for an improvement by a block-based operator to occur. We apply it in a proof of polynomial-time convergence of a block-based algorithm on paths. We also discuss the behaviour of the algorithm on complements of bipartite graphs, where it can have a spectrum of possible behaviour, ranging from polynomial-time convergence to getting stuck in a suboptimal solution. Worst-case result is proved for a graph class, where the algorithm gets stuck in a suboptimal solution with an overwhelming probability.}, notes = {Also known as \cite{2463436} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chen:2013:GECCOa, author = {Wenxiang Chen and Darrell Whitley and Doug Hains and Adele Howe}, title = {Second order partial derivatives for NK-landscapes}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {503--510}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463437}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Local search methods based on explicit neighbourhood enumeration require at least $O(n)$ time to identify all possible improving moves. For k-bounded pseudo-Boolean optimisation problems, recent approaches have achieved $O(k^2*2^{k})$ run time cost per move, where $n$ is the number of variables and $k$ is the number of variables per subfunction. Even though the bound is independent of $n$, the complexity per move is still exponential in $k$. In this paper, we propose a second order partial derivatives-based approach that executes first-improvement local search where the run time cost per move is time polynomial in $k$ and independent of $n$. This method is applied to NK-landscapes, where larger values of $k$ may be of particular interest.}, notes = {Also known as \cite{2463437} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chivilikhin:2013:GECCO, author = {Daniil Chivilikhin and Vladimir Ulyantsev}, title = {MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {511--518}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463440}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present MuACOsm, a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimisation (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM. The goal is to maximise the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem.}, notes = {Also known as \cite{2463440} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Corus:2013:GECCO, author = {Dogan Corus and Per Kristian Lehre and Frank Neumann}, title = {The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {519--526}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463442}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. With this paper, we start the runtime analysis of evolutionary algorithms for bi-level optimisation problems. We examine the NP-hard generalised minimum spanning tree problem and analyse the two approaches presented by Hu and Raidl [7] (2012) in the context of parametrised complexity (with respect to the number of clusters) that distinguish each other by the chosen representation of possible solutions. Our results show that a (1+1) EA working with the spanning nodes representation is not a fixed-parameter evolutionary algorithm for the problem, whereas the global structure representation enables to solve the problem in fixed-parameter time. Furthermore, we present hard instances for each approach and show that the two approaches are highly complementary by proving that they solve each other's hard instances very efficiently.}, notes = {Also known as \cite{2463442} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Drugan:2013:GECCO, author = {Madalina M. Drugan}, title = {Cartesian product of scalarization functions for many-objective QAP instances with correlated flow matrices: cartesian product of scalarization functions}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {527--534}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463433}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to simplify optimisation in many-objective search spaces, we propose the Cartesian product of scalarisation functions to reduce the number of objectives of the search space. To achieve this, we design a stochastic Pareto local search algorithm and we demonstrate their use on examples of product functions. We test this algorithm on generated many-objective quadratic assignment instances with correlated flow matrices. The experimental tests show a superior performance for the local search algorithms using product functions instead of the standard scalarisation functions. For instances with strong correlation between the flow matrices, product based algorithms have similar performance with the standard Pareto local search.}, notes = {Also known as \cite{2463433} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Gonccalves:2013:GECCO, author = {Frederico A.C.A. Gon\c{c}alves and Guimar\, {a}es, Frederico G. and Souza, Marcone J.F.}, title = {An evolutionary multi-agent system for database query optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {535--542}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465802}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Join query optimisation has a direct impact on the performance of a database system. This work presents an evolutionary multi-agent system applied to the join ordering problem related to database query planning. The proposed algorithm was implemented and embedded in the core of a database management system (DBMS). Parameters of the algorithm were calibrated by means of a factorial design and an analysis based on the variance. The algorithm was compared with the official query planner of the H2 DBMS, using a methodology based on benchmark tests. The results show that the proposed evolutionary multi-agent system was able to generate solutions associated with low execution costs in the majority of the cases.}, notes = {Also known as \cite{2465802} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Jacques:2013:GECCO, author = {Julie Jacques and Julien Taillard and David Delerue and Laetitia Jourdan and Clarisse Dhaenens}, title = {The benefits of using multi-objectivization for mining pittsburgh partial classification rules in imbalanced and discrete data}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {543--550}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463432}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A large number of rule interestingness measures have been used as objectives in multi-objective classification rule mining algorithms. Aggregation or Pareto dominance are commonly used to deal with these multiple objectives. This paper compares these approaches on a partial classification problem over discrete and imbalanced data. After performing a Principal Component Analysis (PCA) to select candidate objectives and find conflictive ones, the two approaches are evaluated. The Pareto dominance-based approach is implemented as a dominance-based local search (DMLS) algorithm using confidence and sensitivity as objectives, while the other is implemented as a single-objective hill climbing using F-Measure as an objective, which combines confidence and sensitivity. Results shows that the dominance-based approach obtains statistically better results than the single-objective approach.}, notes = {Also known as \cite{2463432} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Styles:2013:GECCO, author = {James Styles and Holger Hoos}, title = {Ordered racing protocols for automatically configuring algorithms for scaling performance}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {551--558}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463438}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Automated algorithm configuration has been proved to be an effective approach for achieving improved performance of solvers for many computationally hard problems. We consider the challenging situation where the kind of problem instances for which we desire optimised performance is too difficult to be used during the configuration process. Here, we propose a novel combination of racing techniques with existing algorithm configurators to meet this challenge. We demonstrate that, applied to state-of-the-art solver for propositional satisfiability, mixed integer programming and travelling salesman problems, the resulting algorithm configuration protocol achieves better results than previous approaches and in many cases closely matches the bound on performance obtained using an oracle selector. We also report results indicating that the performance of our new racing protocols is quite robust to variations in the confidence level of the test used for eliminating weak configurations, and that performance benefits from presenting instances ordered according to increasing difficulty during the race, something not done in standard racing procedures.}, notes = {Also known as \cite{2463438} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Yuan:2013:GECCO, author = {Yuan Yuan and Hua Xu}, title = {A memetic algorithm for the multi-objective flexible job shop scheduling problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {559--566}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463431}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimise the makespan, total workload and critical workload. By using well-designed chromosome encoding/decoding scheme and genetic operators, the non-dominated sorting genetic algorithm II (NSGA-II) is first adapted for the MO-FJSP. Then the MA is developed by incorporating a novel local search algorithm into the adapted NSGA-II, where several mechanisms to balance the genetic search and local search are employed. In the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimisation of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbour. Experimental results on well-known benchmark instances show that the proposed MA outperforms significantly two off-the-shelf multi-objective evolutionary algorithms and four state-of-the-art algorithms specially proposed for the MO-FJSP.}, notes = {Also known as \cite{2463431} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Yuen:2013:GECCO, author = {Shiu Yin Yuen and Chi Kin Chow and Xin Zhang}, title = {Which algorithm should i choose at any point of the search: an evolutionary portfolio approach}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {567--574}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463435}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem. A portfolio of evolutionary algorithms is first formed. Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Composite DE (CoDE), Particle Swarm Optimisation (PSO2011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary Algorithm (MultiEA). Experimental results on the full set of 25 CEC2005 benchmark functions show that MultiEA outperforms i) Multialgorithm Genetically Adaptive Method for Single Objective Optimisation (AMALGAM-SO); ii) Population-based Algorithm Portfolio (PAP); and iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA). The properties of the prediction measures are also studied. The portfolio approach proposed is generic. It can be applied to portfolios composed of non-evolutionary algorithms as well.}, notes = {Also known as \cite{2463435} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bringmann:2013:GECCO, author = {Karl Bringmann and Tobias Friedrich}, title = {Parameterized average-case complexity of the hypervolume indicator}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {575--582}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463450}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The hypervolume indicator (HYP) is a popular measure for the quality of a set of n solutions in Rd. We discuss its asymptotic worst-case run times and several lower bounds depending on different complexity-theoretic assumptions. Assuming that P!=NP, there is no algorithm with runtime poly(n,d). Assuming the exponential time hypothesis, there is no algorithm with runtime no(d). In contrast to these worst-case lower bounds, we study the average-case complexity of HYP for points distributed i.i.d. at random on a d-dimensional simplex. We present a general framework which translates any algorithm for HYP with worst-case runtime n f(d) to an algorithm with worst-case runtime n f(d)+1 and fixed-parameter-tractable (FPT) average-case runtime. This can be used to show that HYP can be solved in expected time O(d d2/2, n + d, n2), which implies that HYP is FPT on average while it is W[1]-hard in the worst-case. For constant dimension d this gives an algorithm for HYP with runtime O(n2) on average. This is the first result proving that HYP is asymptotically easier in the average case. It gives a theoretical explanation why most HYP algorithms perform much better on average than their theoretical worst-case runtime predicts.}, notes = {Also known as \cite{2463450} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Britto:2013:GECCO, author = {Andre Britto and Sanaz Mostaghim and Aurora Pozo}, title = {Iterated multi-swarm: a multi-swarm algorithm based on archiving methods}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {583--590}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463447}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Usually, Multi-Objective Evolutionary Algorithms face serious challengers in handling many objectives problems. This work presents a new Particle Swarm Optimisation algorithm, called Iterated Multi-Swarm (I-Multi Swarm), which explores specific characteristics of PSO to face Many-Objective Problems. The algorithm takes advantage of a Multi-Swarm approach to combine different archiving methods aiming to improve convergence to the Pareto-optimal front and diversity of the non-dominated solutions. I-Multi Swarm is evaluated through an empirical analysis that uses a set of many-Objective problems, quality indicators and statistical tests.}, notes = {Also known as \cite{2463447} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Denysiuk:2013:GECCO, author = {Roman Denysiuk and Lino Costa and Isabel Esp\'{\i}rito Santo}, title = {Many-objective optimization using differential evolution with variable-wise mutation restriction}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {591--598}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463445}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose an evolutionary algorithm for handling many-objective optimisation problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined multi-set of parents and offspring. Furthermore, we suggest a strategy for the restriction of the difference vector in DE operator in order to improve the convergence property in multi-modal fitness landscape. We compare MyO-DEMR with other state-of-the-art multiobjective evolutionary algorithms on a number of multiobjective optimisation problems having up to 20 dimensions. The results reveal that the proposed selection scheme is able to effectively guide the search in high-dimensional objective space. Moreover, MyO-DEMR demonstrates significantly superior performance on multi-modal problems comparing with other DE-based approaches.}, notes = {Also known as \cite{2463445} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Drozdik:2013:GECCO, author = {Martin Drozdik and Hernan Aguirre and Kiyoshi Tanaka}, title = {Attempt to reduce the computational complexity in multi-objective differential evolution algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {599--606}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463453}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nondominated sorting and diversity estimation procedures are an essential part of many multiobjective optimisation algorithms. In many cases these procedures are the computational bottleneck of the entire algorithm. We present the methods to decrease the cost of these procedures for multiobjective differential evolution (DE) algorithms. Our approach is to compute domination ranks and crowding distances for the population at the beginning of the algorithm and use a combination of well known data structures to efficiently update these attributes. Experiments show that the cost of improved nondominated sorting is sub-quadratic in the number of individuals. In practice using our methods the overall DE algorithm can run 2 to 100 times faster.}, notes = {Also known as \cite{2463453} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Everson:2013:GECCO, author = {Richard M. Everson and David J. Walker and Jonathan E. Fieldsend}, title = {Edges of mutually non-dominating sets}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {607--614}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463452}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective optimisation yields an estimated Pareto front of mutually non-dominating solutions, but with more than three objectives understanding the relationships between solutions is challenging. Natural solutions to use as landmarks are those lying near to the edges of the mutually non-dominating set. We propose four definitions of edge points for many-objective mutually non-dominating sets and examine the relations between them. The first defines edge points to be those that extend the range of the attainment surface. This is shown to be equivalent to finding points which are not dominated on projection onto subsets of the objectives. If the objectives are to be minimised, a further definition considers points which are not dominated under maximisation when projected onto objective subsets. A final definition looks for edges via alternative projections of the set. We examine the relations between these definitions and their efficacy for synthetic concave and convex-shaped sets, and on solutions to a prototypical many-objective optimisation problem, showing how they can reveal information about the structure of the estimated Pareto front.}, notes = {Also known as \cite{2463452} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Fortin:2013:GECCO, author = {F\'{e}lix-Antoine Fortin and Simon Grenier and Marc Parizeau}, title = {Generalizing the improved run-time complexity algorithm for non-dominated sorting}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {615--622}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463454}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper generalises the 'Improved Run-Time Complexity Algorithm for Non-Dominated Sorting' by Jensen, removing its limitation that no two solutions can share identical values for any of the problem's objectives. This constraint is especially limiting for discrete combinatorial problems, but can also lead the Jensen algorithm to produce incorrect results even for problems that appear to have a continuous nature, but for which identical objective values are nevertheless possible. Moreover, even when values are not meant to be identical, the limited precision of floating point numbers can sometimes make them equal anyway. Thus a fast and correct algorithm is needed for the general case. The paper shows that generalising the Jensen algorithm can be achieved without affecting its time complexity, and experimental results are provided to demonstrate speedups of up to two orders of magnitude for common problem sizes, when compared with the correct baseline algorithm from Deb.}, notes = {Also known as \cite{2463454} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Fortin:2013:GECCOa, author = {F\'{e}lix-Antoine Fortin and Marc Parizeau}, title = {Revisiting the NSGA-II crowding-distance computation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {623--630}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463456}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved.}, notes = {Also known as \cite{2463456} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kafafy:2013:GECCO, author = {Ahmed Kafafy and St\'{e}phane Bonnevay and Ahmed Bounekkar}, title = {A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {631--638}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463458}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hybrid evolutionary algorithms have been successfully applied to solve numerous multiobjective optimisation problems (MOP). In this paper, a new hybrid evolutionary approach based on search strategy adaptation (HESSA) is presented. In HESSA, the search process is carried out through adopting a pool of different search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected strategy. Then, according to the success of the generated offspring to update the population or the archive, the success ratio of the selected strategy is adapted. This provides the ability for HESSA to adopt the appropriate search strategy according to the problem on hand. Furthermore, the cooperation among different strategies leads to improve the exploration and the exploitation of the search space. The proposed pool is combined to a suitable evolutionary framework for supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external repository to be used as global guides. The proposed HESSA is verified against some of the state of the art MOEAs using a set of test problems commonly used in the literature. The experimental results indicate that HESSA is highly competitive and can be considered as a viable alternative.}, notes = {Also known as \cite{2463458} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kusuno:2013:GECCO, author = {Natsuki Kusuno and Hernan Aguirre and Kiyoshi Tanaka and Masataka Koishi}, title = {Evolutionary multi-objective optimization to attain practically desirable solutions}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {639--646}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463457}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work investigates two methods to search practically desirable solutions expanding the objective space with additional fitness functions associated to particular decision variables. The aim is to find solutions around preferred values of the chosen variables while searching for optimal solutions in the original objective space. Solutions to be practically desirable are constrained to be within a certain distance from the present non-dominated solutions set computed in the original objective space. The proposed methods are compared with an algorithm that simply restricts the range of decision variables around the preferred values and an algorithm that expands the space without constraining the distance from optimality. Our results show that the proposed methods can effectively find practically desirable solutions.}, notes = {Also known as \cite{2463457} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Miyakawa:2013:GECCO, author = {Minami Miyakawa and Keiki Takadama and Hiroyuki Sato}, title = {Two-stage non-dominated sorting and directed mating for solving problems with multi-objectives and constraints}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {647--654}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463449}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel constrained MOEA introducing a parents selection based on a two-stage non-dominated sorting of solutions and directed mating in the objective space. In the parents selection, first, we classify the entire population into several fronts by non-dominated sorting based on constraint violation values. Then, we re-classify each obtained front by non-dominated sorting based on objective function values, and select the parents population from upper fronts. The two-stage non-dominated sorting leads to find feasible solutions having better objective function values in the evolutionary process of infeasible solutions. Also, in the directed mating, we select a primary parent from the parents population and pick solutions dominating the primary parent from the entire population including infeasible solutions. Then we select a secondary parent from the picked solutions and apply genetic operators. The directed mating uses valuable genetic information of infeasible solutions to enhance convergence of each primary parent toward its search direction in the objective space. We compare the search performance of the two proposed algorithms using greedy selection (GS) and tournament selection (TS) in the directed mating with the conventional CNSGA-II and RTS algorithms on SRN, TNK, OSY and m objectives k knapsacks problems. We show that the proposed algorithms achieve higher search performance than CNSGA-II and RTS on all benchmark problems used in this work.}, notes = {Also known as \cite{2463449} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Priester:2013:GECCO, author = {Christopher Priester and Kaname Narukawa and Tobias Rodemann}, title = {A comparison of different algorithms for the calculation of dominated hypervolumes}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {655--662}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463451}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the fields of multi and many-objective optimisation methods, the hypervolume of a set of solutions is a very useful measure for assessing the current state of the optimisation process. It is also the fundamental quality criterion for the well-known SMS-EMOA (S-metric selection evolutionary multi-objective optimisation), which is one of the best many objective optimisation algorithms known at the moment. Unfortunately, the computation of the hypervolume for a given set of solutions is a time-consuming effort which scales unfavourably with the number of objectives and the size of the population. In this work we analysed a number of algorithms for hypervolume computation and systematically measured their computational effort for different numbers of objectives and population size. We compared three established standard algorithms that are used in the Shark optimisation library and a recent approach by While et al. We also included an approximation computation algorithm proposed by Ishibuchi et al., where we additionally evaluated the precision of the approximation computation and its impact on the selection process within an optimisation run. Our findings indicate that the algorithm by While et al. outperforms the three other exact algorithms for a wide range of settings. The Ishibuchi algorithm was shown to have a slightly negative effect on the selection process, but for very large population sizes or number of objectives, the approximation method might be the only viable alternative.}, notes = {Also known as \cite{2463451} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Rubio-Largo:2013:GECCO, author = {Alvaro Rubio-Largo and Qingfu Zhang and Miguel A. Vega-Rodriguez}, title = {MOEA/D for traffic grooming in WDM optical networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {663--670}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463443}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Optical networks have attracted much more attention in the last decades due to its huge bandwidth (Tbps). The Wavelength Division Multiplexing (WDM) is a technology that aims to make the most of this networks by dividing each single fibre link into several wavelengths of light or channels. Each channel operates in the range of Gbps; unfortunately, the requirements of the vast majority of current traffic connection requests are a few Mbps, causing a waste of bandwidth at each channel. We can solve this drawback by equipping each optical node with an access station for multiplexing or grooming several low-speed requests onto one single high-speed channel. This problem of grooming low-speed requests is known in the literature as the Traffic Grooming problem. In this work, we formulate the Traffic Grooming problem as a Multiobjective Optimisation Problem, optimising simultaneously the total throughput, the number of transceivers used, and the average propagation delay. We propose the use of the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The experiments are conducted on three optical network topologies and diverse scenarios. The results report that the MOEA/D algorithm works more efficiently than other multiobjective approaches and other single-objective heuristics published in the literature.}, notes = {Also known as \cite{2463443} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Tagawa:2013:GECCO, author = {Kiyoharu Tagawa and Akihiro Imamura}, title = {Many-hard-objective optimization using differential evolution based on two-stage constraint-handling}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {671--678}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463446}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper focus on the Many-Hard-objective Optimisation Problem (MHOP) in which a lot of objectives are limited by a goal point. In order to obtain an approximation of Pareto-optimal feasible solution set for MHOP, a new algorithm called Differential Evolution for Many-Hard-objective Optimisation (DEMHO) is proposed. For sorting non dominated solutions, DEMHO uses Pairwise Exclusive Hypervolume (PEH) with a newly proposed fast calculation algorithm. Besides, for handing the infeasible solutions of MHOP, a new two-stage truncation method is employed. Through the numerical experiment and the statistical test conducted on some instances of MHOP, the performance of DEMHO is assessed. As a case study, the usefulness of DEMHO is also demonstrated on an optimum design of SAW duplexer.}, notes = {Also known as \cite{2463446} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Verbeeck:2013:GECCO, author = {Denny Verbeeck and Francis Maes and Kurt De Grave and Hendrik Blockeel}, title = {Multi-objective optimization with surrogate trees}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {679--686}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463455}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective optimisation problems are usually solved with evolutionary algorithms when the objective functions are cheap to compute, or with surrogate-based optimisers otherwise. In the latter case, the objective functions are modelled with powerful non-linear model learners such as Gaussian Processes or Support Vector Machines, for which the training time can be prohibitively large when dealing with optimisation problems with moderately expensive objective functions. In this paper, we investigate the use of model trees as an alternative kind of model, providing a good compromise between high expressiveness and low training time. We propose a fast surrogate-based optimiser exploiting the structure of model trees for candidate selection. The empirical results show the promise of the approach for problems on which classical surrogate-based optimisers are painfully slow.}, notes = {Also known as \cite{2463455} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wagner:2013:GECCO, author = {Markus Wagner and Frank Neumann}, title = {A fast approximation-guided evolutionary multi-objective algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {687--694}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463448}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Approximation-Guided Evolution (AGE) [4] is a recently presented multi-objective algorithm that outperforms state-of-the-art multi-multi-objective algorithms in terms of approximation quality. This holds for problems with many objectives, but AGE's performance is not competitive on problems with few objectives. Furthermore, AGE is storing all non-dominated points seen so far in an archive, which can have very detrimental effects on its runtime. In this article, we present the fast approximation-guided evolutionary algorithm called AGE-II. It approximates the archive in order to control its size and its influence on the runtime. This allows for trading-off approximation and runtime, and it enables a faster approximation process. Our experiments show that AGE-II performs very well for multi-objective problems having few as well as many objectives. It scales well with the number of objectives and enables practitioners to add objectives to their problems at small additional computational cost.}, notes = {Also known as \cite{2463448} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wang:2013:GECCOa, author = {Rui Wang and Robin C. Purshouse and Peter J. Fleming}, title = {On finding well-spread pareto optimal solutions by preference-inspired co-evolutionary algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {695--702}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463444}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Preference-inspired co-evolutionary algorithm (PICEA) is a novel class of multi-objective evolutionary algorithm. In PICEA, the usual candidate solutions are guided toward the Pareto optimal front by co-evolving a set of decision maker preferences during the search process. PICEA-g is one realisation of PICEAs in which goal vectors are taken as preferences. This study points out one limitation of this method -the obtained solutions are distributed unevenly along the Pareto optimal front. To handle this limitation, an improved fitness assignment method is proposed in which the density information of the solutions is considered. Experimental results, in terms of the selected performance metrics, show this improved fitness assignment method is effective.}, notes = {Also known as \cite{2463444} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lakhman:2013:GECCO, author = {Konstantin Lakhman and Mikhail Burtsev}, title = {Neuroevolution results in emergence of short-term memory in multi-goal environment}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {703--710}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463465}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Animals behave adaptively in environments with multiple competing goals. Understanding of mechanisms underlying such goal-directed behaviour remains a challenge for neuroscience as well as for adaptive system and machine learning research. To address this problem we developed an evolutionary model of adaptive behaviour in a multi-goal stochastic environment. The proposed neuroevolutionary algorithm is based on neuron's duplication as a basic mechanism of agent's recurrent neural network development. Results of simulations demonstrate that in the course of evolution agents acquire the ability to store the short-term memory and use it in behaviour with alternative actions. We found that evolution discovered two mechanisms for short-term memory. The first mechanism is integration of sensory signals and ongoing internal neural activity, resulting in emergence of cell groups specialised on alternative actions. And the second mechanism is slow neurodynamical process that makes possible to encode the previous behavioural choice.}, notes = {Also known as \cite{2463465} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lockett:2013:GECCO, author = {Alan J. Lockett and Risto Miikkulainen}, title = {Neuroannealing: martingale optimization for neural networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {711--718}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463463}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Neural networks are effective tools to solve prediction, modelling, and control tasks. However, methods to train neural networks have been less successful on control problems that require the network to model intricately structured regions in state space. This paper presents neuroannealing, a method for training neural network controllers on such problems. Neuroannealing is based on evolutionary annealing, a global optimisation method that leverages all available information to search for the global optimum. Because neuroannealing retains all intermediate solutions, it is able to represent the fitness landscape more accurately than traditional generational methods and so finds solutions that require greater network complexity. This hypothesis is tested on two problems with fractured state spaces. Such problems are difficult for other methods such as NEAT because they require relatively deep network topology in order to extract the relevant features of the network inputs. Neuroannealing outperforms NEAT on these problems, supporting the hypothesis. Overall, neuroannealing is a promising approach for training neural networks to solve complex practical problems.}, notes = {Also known as \cite{2463463} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Morse:2013:GECCO, author = {Gregory Morse and Sebastian Risi and Charles R. Snyder and Kenneth O. Stanley}, title = {Single-unit pattern generators for quadruped locomotion}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {719--726}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463461}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Legged robots can potentially venture beyond the limits of wheeled vehicles. While creating controllers for such robots by hand is possible, evolutionary algorithms are an alternative that can reduce the burden of hand-crafting robotic controllers. Although major evolutionary approaches to legged locomotion can generate oscillations through popular techniques such as continuous time recurrent neural networks (CTRNNs) or sinusoidal input, they typically face a challenge in maintaining long-term stability. The aim of this paper is to address this challenge by introducing an effective alternative based on a new type of neuron called a single-unit pattern generator (SUPG). The SUPG, which is indirectly encoded by a compositional pattern producing network (CPPN) evolved by HyperNEAT, produces a flexible temporal activation pattern that can be reset and repeated at any time through an explicit trigger input, thereby allowing it to dynamically recalibrate over time to maintain stability. The SUPG approach, which is compared to CTRNNs and sinusoidal input, is shown to produce natural-looking gaits that exhibit superior stability over time, thereby providing a new alternative for evolving oscillatory locomotion.}, notes = {Also known as \cite{2463461} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Palmer:2013:GECCO, author = {Michael E. Palmer}, title = {Gene networks have a predictive long-term fitness}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {727--734}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463467}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Using a model of evolved gene regulatory networks, we illustrate several quantitative metrics relating to the long-term evolution of lineages. The k-generation fitness and k-generation survivability measure the evolutionary success of lineages. An entropy measure is used to quantify the predictability of lineage evolution. The metrics are readily applied to any system in which lineage membership can be periodically counted, and provide a quantitative characterisation of the genetic landscape, genotype-phenotype map, and fitness landscape. Evolution is shown to be surprisingly predictable in gene networks: only a small number of the possible outcomes are ever observed in multiple replicate experiments. We emphasise the view that the lineage (not the individual, or the genotype) is the evolving entity over the long term. Notably, the long-term fitness is distinct from the short-term fitness. Since evolution is repeatable over the long-term, this implies long-term selection on lineages is possible; the evolutionary process need not be 'short-sighted'. If we wish to evolve very complex artifacts, it will be expedient to promote the long-term evolution of the genetic architecture by tailoring our models to emphasise long-term selection.}, notes = {Also known as \cite{2463467} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Pugh:2013:GECCO, author = {Justin K. Pugh and Kenneth O. Stanley}, title = {Evolving multimodal controllers with HyperNEAT}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {735--742}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463459}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Natural brains effectively integrate multiple sensory modalities and act upon the world through multiple effector types. As researchers strive to evolve more sophisticated neural controllers, confronting the challenge of multimodality is becoming increasingly important. As a solution, this paper presents a principled new approach to exploiting indirect encoding to incorporate multimodality based on the HyperNEAT generative neuroevolution algorithm called the multi-spatial substrate (MSS). The main idea is to place each input and output modality on its own independent plane. That way, the spatial separation of such groupings provides HyperNEAT an a priori hint on which neurons are associated with which that can be exploited from the start of evolution. To validate this approach, the MSS is compared with more conventional approaches to HyperNEAT substrate design in a multiagent domain featuring three input and two output modalities. The new approach both significantly outperforms conventional approaches and reduces the creative burden on the user to design the layout of the substrate, thereby opening formerly prohibitive multimodal problems to neuroevolution.}, notes = {Also known as \cite{2463459} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Rieffel:2013:GECCO, author = {John Rieffel}, title = {Heterochronic scaling of developmental durations in evolved soft robots}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {743--750}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463466}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the evolution of Generative and Developmental Systems (GDS), the choice of where along the ontogenic trajectory to stop development in order to measure fitness can have a profound effect upon the emergent solutions. After illustrating the complexities of ontogenic fitness trajectories, we introduce a GDS encoding without an a priori fixed developmental duration, which instead slowly increases the duration over the course of evolution. Applied to a soft robotic locomotion task, we demonstrate how this approach can not only retain the well known advantages of developmental encodings, but also be more efficient and arrive at more parsimonious solutions than approaches with static developmental time frames.}, notes = {Also known as \cite{2463466} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Samuelsen:2013:GECCO, author = {Eivind Samuelsen and Kyrre Glette and Jim Torresen}, title = {A hox gene inspired generative approach to evolving robot morphology}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {751--758}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463464}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an approach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimising a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.}, notes = {Also known as \cite{2463464} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{vandenBerg:2013:GECCO, author = {Thomas G. van den Berg and Shimon Whiteson}, title = {Critical factors in the performance of hyperNEAT}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {759--766}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463460}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {HyperNEAT is a popular indirect encoding method for evolutionary computation that has performed well on a number of benchmark tasks. This paper presents a series of experiments designed to examine the critical factors for its success. First, we determine the fewest hidden nodes a genotypic network needs to solve several of these tasks. Our results show that all of these tasks are easy: they can be solved with at most one hidden node and require generating only trivial regular patterns. Then, we examine how HyperNEAT performs when the tasks are made harder. Our results show that HyperNEAT's performance decays quickly: it fails to solve all variants of these tasks that require more complex solutions. Next, we examine the hypothesis that fracture in the problem space, known to be challenging for regular NEAT, is even more so for HyperNEAT. Our results suggest that quite complex networks are needed to cope with fracture and HyperNEAT can have difficulty discovering them. Finally, we connect these results to previous experiments showing that HyperNEAT's performance decreases on irregular tasks. Our results suggest irregularity is an extreme form of fracture and that HyperNEAT's limitations are thus more severe than those experiments suggested.}, notes = {Also known as \cite{2463460} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wilson:2013:GECCO, author = {Dennis Wilson and Emmanuel Awa and Sylvain Cussat-Blanc and Kalyan Veeramachaneni and Una-May O'Reilly}, title = {On learning to generate wind farm layouts}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {767--774}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463462}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Optimising a wind farm layout is a very complex problem that involves many local and global constraints such as inter-turbine wind interference or terrain peculiarities. Existing methods are either inefficient or, when efficient, take days or weeks to execute. Solutions are contextually sensitive to the specific values of the problem variables; when one value is modified, the algorithm has to be re-run from scratch. This paper proposes the use of a developmental model to generate farm layouts. Controlled by a gene regulatory network, virtual cells have to populate a simulated environment that represents the wind farm. When the cells' behaviour is learnt, this approach has the advantage that it is re-usable in different contexts; the same initial cell is responsive to a variety of environments and the layout generation takes few minutes instead of days.}, notes = {Also known as \cite{2463462} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Doerr:2013:GECCO, author = {Benjamin Doerr and Carola Doerr and Franziska Ebel}, title = {Lessons from the black-box: fast crossover-based genetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {781--788}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463480}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The recently active research area of black-box complexity revealed that for many optimisation problems the best possible black-box optimisation algorithm is significantly faster than all known evolutionary approaches. While it is not to be expected that a general-purpose heuristic competes with a problem-tailored algorithm, it still makes sense to look for the reasons for this discrepancy. In this work, we exhibit one possible reason---most optimal black-box algorithms profit also from solutions that are inferior to the previous-best one, whereas evolutionary approaches guided by the 'survival of the fittest' paradigm often ignore such solutions. Trying to overcome this shortcoming, we design a simple genetic algorithm that first creates lambda offspring from a single parent by mutation with a mutation probability that is k times larger than the usual one. From the best of these offspring (which often is worse than the parent) and the parent itself, we generate further offspring via a uniform crossover operator that takes bits from the winner offspring with probability 1/k only. A rigorous runtime analysis proves that our new algorithm for suitable parameter choices on the OneMax test function class is asymptotically faster (in terms of the number of fitness evaluations) than what has been shown for mu +, lambda EAs. This is the first time that crossover is shown to give an advantage for the OneMax class that is larger than a constant factor. Using a fitness-dependent choice of k and lambda, the optimisation time can be reduced further to linear in n. Our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges. Also, a simple self-adaptive choice of these parameters gives surprisingly good results.}, notes = {Also known as \cite{2463480} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Doerr:2013:GECCOa, author = {Carola Doerr and Fran\c{c}ois-Michel De Rainville}, title = {Constructing low star discrepancy point sets with genetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {789--796}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463469}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Geometric discrepancies are standard measures to quantify the irregularity of distributions. They are an important notion in numerical integration. One of the most important discrepancy notions is the so-called star discrepancy. Roughly speaking, a point set of low star discrepancy value allows for a small approximation error in quasi-Monte Carlo integration. It is thus the most studied discrepancy notion. In this work we present a new algorithm to compute point sets of low star discrepancy. The two components of the algorithm (for the optimisation and the evaluation, respectively) are based on evolutionary principles. Our algorithm clearly outperforms existing approaches. To the best of our knowledge, it is also the first algorithm which can be adapted easily to optimise inverse star discrepancies.}, notes = {Also known as \cite{2463469} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Giessen:2013:GECCO, author = {Christian Gie\ssen}, title = {Hybridizing evolutionary algorithms with opportunistic local search}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {797--804}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463475}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There is empirical evidence that memetic algorithms (MAs) can outperform plain evolutionary algorithms (EAs). Recently the first runtime analyses have been presented proving the aforementioned conjecture rigorously by investigating Variable-Depth Search, VDS for short (Sudholt, 2008). Sudholt raised the question if there are problems where VDS performs badly. We answer this question in the affirmative in the following way. We analyse MAs with VDS, which is also known as Kernighan-Lin for the TSP, on an artificial problem and show that MAs with a simple first-improvement local search outperform VDS. Moreover, we show that the performance gap is exponential. We analyse the features leading to a failure of VDS and derive a new local search operator, coined Opportunistic Local Search, that can easily overcome regions of the search space where local optima are clustered. The power of this new operator is demonstrated on the Rastrigin function encoded for binary hypercubes. Our results provide further insight into the problem of how to prevent local search algorithms to get stuck in local optima from a theoretical perspective. The methods stem from discrete probability theory and combinatorics.}, notes = {Also known as \cite{2463475} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Hains:2013:GECCO, author = {Doug Hains and Darrell Whitley and Adele Howe and Wenxiang Chen}, title = {Hyperplane initialized local search for MAXSAT}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {805--812}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463468}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {By converting the MAXSAT problem to Walsh polynomials, we can efficiently and exactly compute the hyperplane averages of fixed order k. We use this fact to construct initial solutions based on variable configurations that maximise the sampling of hyperplanes with good average evaluations. The Walsh coefficients can also be used to implement a constant time neighbourhood update which is integral to a fast next descent local search for MAXSAT (and for all bounded pseudo-Boolean optimisation problems.) We evaluate the effect of initialising local search with hyperplane averages on both the first local optima found by the search and the final solutions found after a fixed number of bit flips. Hyperplane initialisation not only provides better evaluations, but also finds local optima closer to the globally optimal solution in fewer bit flips than search initialised with random solutions. A next descent search initialised with hyperplane averages is able to outperform several state-of-the art stochastic local search algorithms on both random and industrial instances of MAXSAT.}, notes = {Also known as \cite{2463468} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Manso:2013:GECCO, author = {Ant\'{o}nio Manuel Rodrigues Manso and Lu\'{\i}s Miguel Parreira Correia}, title = {A multiset genetic algorithm for the optimization of deceptive problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {813--820}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463471}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MuGA is an evolutionary algorithm (EA) that represents populations as multisets, instead of the conventional collection. Such representation can be explored to adapt genetic operators in order to increase performance in difficult problems. In this paper we present an adaptation of the mutation operator, multiset wave mutation (MWM), that explores the multiset representation to apply different mutation ratios to the same chromosome, and an adaptation of the replacement operator, multiset decimation replacement (MDR) that preserves multiset representation in the main population and helps MuGA to solve hard deceptive problems. Results obtained in different deceptive functions show that pairing both operators is a robust approach with a high success ratio in most of the problems.}, notes = {Also known as \cite{2463471} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Martins:2013:GECCO, author = {Jean Paulo Martins and Alexandre Claudio Botazzo Delbem}, title = {The influence of linkage-learning in the linkage-tree GA when solving multidimensional knapsack problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {821--828}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463476}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Linkage Learning (LL) is an important issue concerning the development of more effective genetic algorithms (GA). It is from the identification of strongly dependent variables that crossover can be effective and an efficient search can be implemented. In the last decade many algorithms have confirmed the beneficial influence of LL when solving nearly decomposable problems. As it is a well-known fact from the no free-lunch theorem, LL can not be the best tool for all optimisation problems, therefore, methods to identify those problems which could be efficiently solved by LL have become necessary. This paper investigates that nearly-decomposable problems present characteristic linkage-trees, therefore, those trees can be used as reference to infer whether or not some black-box optimisation problem is a good candidate to be solved by LL. In this context, we consider the linkage-tree model from the Linkage-Tree GA (LTGA) and use the silhouette measure to expose some problems' characteristics. The silhouette fingerprints (SF) are defined for overlapping deceptive trap functions and compared with the SFs obtained for Multidimensional Knapsack Problems (MKP). The comparison allowed us to conclude that MKPs do not present evident linkages. This result was confirmed by experiments comparing the performance of the LTGA and the Randomised LTGA, in which both algorithms had very similar results.}, notes = {Also known as \cite{2463476} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Merelo-Guervos:2013:GECCO, author = {Juan Juli\'{a}n Merelo-Guerv\'{o}s and Pedro Castillo and Antonio M. Mora Garc\'{\i}a and Anna I. Esparcia-Alc\'{a}zar}, title = {Improving evolutionary solutions to the game of mastermind using an entropy-based scoring method}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {829--836}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463473}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Solving the MasterMind puzzle, that is, finding out a hidden combination by using hints that tell you how close some strings are to that one is a combinatorial optimisation problem that becomes increasingly difficult with string size and the number of symbols used in it. Since it does not have an exact solution, heuristic methods have been traditionally used to solve it; these methods scored each combination using a heuristic function that depends on comparing all possible solutions with each other. In this paper we first optimise the implementation of previous evolutionary methods used for the game of mastermind, obtaining up to a 40percent speed improvement over them. Then we study the behaviour of an entropy-based score, which has previously been used but not checked exhaustively and compared with previous solutions. The combination of these two strategies obtain solutions to the game of Mastermind that are competitive, and in some cases beat, the best solutions obtained so far. All data and programs have also been published under an open source license.}, notes = {Also known as \cite{2463473} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Oliveto:2013:GECCO, author = {Pietro S. Oliveto and Christine Zarges}, title = {Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {837--844}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463478}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary dynamic optimisation has become one of the most active research areas in evolutionary computation. We consider the BALANCE function for which the poor performance of the (1+1) EA at low frequencies of change has been shown in the literature. We analyse the impact of populations and diversity mechanisms towards the robustness of evolutionary algorithms with respect to frequencies of change. We rigorously prove that for each population size mu, there exists a sufficiently low frequency of change such that the (mu+1) EA without diversity requires expected exponential time. Furthermore we prove that a crowding as well as a genotype diversity mechanism do not help the (mu+1) EA. On the positive side we prove that, independent of the frequency of change, a fitness-diversity mechanism turns the runtime from exponential to polynomial. Finally, we show how a careful use of fitness-sharing together with a crowding mechanism is effective already with a population of size 2.}, notes = {Also known as \cite{2463478} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{:2013:GECCO, author = {Paix\, {a}o, Tiago and Barton, Nick}, title = {A variance decomposition approach to the analysis of genetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {845--852}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463470}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Prediction of the evolutionary process is a long standing problem both in the theory of evolutionary biology and evolutionary computation (EC). It has long been realised that heritable variation is crucial to both the response to selection and the success of genetic algorithms. However, not all variation contributes in the same way to the response. Quantitative genetics has developed a large body of work trying to estimate and understand how different components of the variance in fitness in the population contribute to the response to selection. We illustrate how to apply some concepts of quantitative genetics to the analysis of genetic algorithms. In particular, we derive estimates for the short term prediction of the response to selection and we use variance decomposition to gain insight on local aspects of the landscape. Finally, we propose a new population based genetic algorithm that uses these methods to improve its operation.}, notes = {Also known as \cite{2463470} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Sadowski:2013:GECCO, author = {Krzysztof L. Sadowski and Peter A.N. Bosman and Dirk Thierens}, title = {On the usefulness of linkage processing for solving MAX-SAT}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {853--860}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463474}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Mixing of partial solutions is a key mechanism used for creating new solutions in many Genetic Algorithms (GAs). However, this mixing can be disruptive and generate improved solutions inefficiently. Exploring a problem's structure can help in establishing less disruptive operators, leading to more efficient mixing. One way of using a problem's structure is to consider variable linkage information. Once a proper linkage model for a problem is obtained, mixing becomes more efficient. This paper focuses on exploring different methods of building family of subsets (FOS) linkage models, which are then used with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) to solve MAX-SAT problems. Individual algorithms from the GOMEA family are distinguished by how the FOS linkage models are constructed. The Linkage Tree Genetic Algorithm (LTGA) is a GOMEA instance which learns the linkage between problem variables by building a linkage tree in every generation. In this paper, we introduce SAT-GOMEA. This algorithm uses a predetermined FOS linkage model based on the SAT-problem's definition. Both algorithms use linkage information. We show that because of this information they are capable of performing significantly better than other algorithms from the GOMEA family which do not explore linkage. In a black-box (BBO) setting, LTGA performs well. We further study the use of linkage models outside of the typical BBO approach by examining the behaviour of LTGA and the problem-specific SAT-GOMEA in a white-box setting, where more of the problem information is known. We show that with this white-box optimisation (WBO) approach, exploring linkage information can still be beneficial. We further compare the performance of these algorithms with a selection of non-GOMEA based algorithms. From the BBO perspective, we compare LTGA with the well-known hBOA. In the WBO setting, many very efficient problem-specific local search (LS) algorithm exist. We specifically consider Walksat and GSAT and show that combining LS with LTGA or SAT-GOMEA increases their performance.}, notes = {Also known as \cite{2463474} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Shabash:2013:GECCO, author = {Boris Shabash and Kay C. Wiese}, title = {pEvoSAT: a novel permutation based genetic algorithm for solving the boolean satisfiability problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {861--868}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463479}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we introduce pEvoSAT, a permutation based Genetic Algorithm (GA), designed to solve the Boolean satisfiability (SAT) problem when it is presented in the conjunctive normal form (CNF). The use of permutation based representation allows the algorithm to take advantage of domain specific knowledge such as unit propagation, and pruning. In this paper, we explore and characterise the behaviour of our algorithm. This paper also presents the comparison of pEvoSAT to GASAT, a leading implementation of GAs for the solving of CNF instances.}, notes = {Also known as \cite{2463479} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{:2013:GECCOa, author = {Sim\, {o}es, Anabela and Costa, Ernesto}, title = {Extended virtual loser genetic algorithm for the dynamic traveling salesman problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {869--876}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463472}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The use of memory-based Evolutionary Algorithms (EAs) for dynamic optimisation problems (DOPs) has proved to be efficient, namely when past environments reappear later. Memory EAs using associative approaches store the best solution and additional information about the environment. In this paper we propose a new algorithm called Extended Virtual Loser Genetic Algorithm (eVLGA) to deal with the Dynamic Travelling Salesman Problem (DTSP). In this algorithm, a matrix called extended Virtual Loser (eVL) is created and updated during the evolutionary process. This matrix contains information that reflects how much the worst individuals differ from the best, working as environmental information, which can be used to avoid past errors when new individuals are created. The matrix is stored into memory along with the current best individual of the population and, when a change is detected, this information is retrieved from memory and used to create new individuals that replace the worst of the population. eVL is also used to create immigrants that are tested in eVLGA and in other standard algorithms. The performance of the investigated eVLGAs is tested in different instances of the Dynamic Travelling Salesman Problem and compared with different types of EAs. The statistical results based on the experiments show the efficiency, robustness and adaptability of the different versions of eVLGA.}, notes = {Also known as \cite{2463472} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Thierens:2013:GECCO, author = {Dirk Thierens and Peter A.N. Bosman}, title = {Hierarchical problem solving with the linkage tree genetic algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {877--884}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463477}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hierarchical problems represent an important class of nearly decomposable problems. The concept of near decomposability is central to the study of complex systems. When little a priori information is available, a black box problem solver is needed to optimise these hierarchical problems. The solver should be able to learn linkage information, and to preserve and test partial solutions at different levels in the hierarchy. Two well known benchmark functions, shuffled Hierarchical If-And-Only-If (HIFF) and shuffled Hierarchical Trap (HTRAP) functions, exemplify the challenges posed by hierarchical problems. Standard genetic algorithms are unable to solve these problems, and specific methods, like SEAM and hBOA, have been designed to address them. In this paper, we investigate how the recently developed Linkage Tree Genetic Algorithm (LTGA) performs on these hierarchical problems. We compare LTGA with SEAM and hBOA on HIFF and HTRAP functions. Results show that, although LTGA is a simple algorithm compared to SEAM and hBOA, it nevertheless is a very efficient, reliable, and scalable algorithm for solving the randomly shuffled versions of HIFF and HTRAP, two hard, hierarchical problems.}, notes = {Also known as \cite{2463477} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Atwater:2013:GECCO, author = {Aaron Atwater and Malcolm I. Heywood}, title = {Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {885--892}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463489}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterised by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterised by non-stationary stepwise changes.}, notes = {Also known as \cite{2463489} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bailey:2013:GECCO, author = {Alexander Bailey and Beatrice Ombuki-Berman and Mario Ventresca}, title = {Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {893--900}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463498}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The pathways that relay sensory information within the brain form a network of connections, the precise organisation of which is unknown. Communities of neurons can be discerned within this tangled structure, with inhomogeneously distributed connections existing between cortical areas. Classification and modelling of these networks has led to advancements in the identification of unhealthy or injured brains, however, the current models used are known to have major deficiencies. Specifically, the community structure of the cortex is not accounted for in existing algorithms, and it is unclear how to properly design a more representative graph model. It has recently been demonstrated that genetic programming may be useful for inferring accurate graph models, although no study to date has investigated the ability to replicate community structure. In this paper we propose the first GP system for the automatic inference of algorithms capable of generating, to a high accuracy, networks with community structure. We use a common cat cortex data set to highlight the efficacy of our approach. Our experiments clearly show that the inferred graph model generates a more representative network than those currently used in scientific literature.}, notes = {Also known as \cite{2463498} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bartovs:2013:GECCO, author = {Tom\'{a}\v{s} Barto\v{s} and Tom\'{a}\v{s} Skopal and Juraj Mo\v{s}ko}, title = {Efficient indexing of similarity models with inequality symbolic regression}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {901--908}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463487}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The increasing amount of available unstructured content introduced a new concept of searching for information, the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequality symbolic regression principle and show how it helps the GP-SIMDEX Framework to find appropriate results that in most cases outperform the best-known indexing methods.}, notes = {Also known as \cite{2463487} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Dick:2013:GECCOa, author = {Grant Dick}, title = {An effective parse tree representation for tartarus}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {909--916}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463497}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent work in genetic programming (GP) has highlighted the need for stronger benchmark problems. For benchmarking planning scenarios, the artificial ant problem is often used. With a limited number of test cases, this problem is often fairly simple to solve. A more complex planning problem is Tartarus, but as of yet no standard representation for Tartarus exists for GP. This paper examines an existing parse tree representation for Tartarus, and identifies weaknesses in the way in which it manipulates environmental information. Through this analysis, an alternative representation is proposed for Tartarus that shares many similarities with those already used in GP for planning problems. Empirical analysis suggests that the proposed representation has qualities that make it a suitable benchmark problem.}, notes = {Also known as \cite{2463497} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Fu:2013:GECCO, author = {Wenlong Fu and Mark Johnston and Mengjie Zhang}, title = {Genetic programming for edge detection using multivariate density}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {917--924}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463485}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The combination of local features in edge detection can generally improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. Multivariate density is a generalisation of the one-dimensional (univariate) distribution to higher dimensions. In order to effectively construct composite features with multivariate density, a Genetic Programming (GP) system is proposed to evolve Bayesian-based programs. An evolved Bayesian-based program estimates the relevant multivariate density to construct a composite feature. The results of the experiments show that the GP system constructs high-level combined features which substantially improve the detection performance.}, notes = {Also known as \cite{2463485} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Gaudesi:2013:GECCO, author = {Marco Gaudesi and Giovanni Squillero and Alberto Tonda}, title = {An efficient distance metric for linear genetic programming}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {925--932}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463495}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Defining a distance measure over the individuals in the population of an Evolutionary Algorithm can be exploited for several applications, ranging from diversity preservation to balancing exploration and exploitation. When individuals are encoded as strings of bits or sets of real values, computing the distance between any two can be a straightforward process; when individuals are represented as trees or linear graphs, however, quite often the user must resort to phenotype-level problem-specific distance metrics. This paper presents a generic genotype-level distance metric for Linear Genetic Programming: the information contained by an individual is represented as a set of symbols, using n-grams to capture significant recurring structures inside the genome. The difference in information between two individuals is evaluated resorting to a symmetric difference. Experimental evaluations show that the proposed metric has a strong correlation with phenotype-level problem-specific distance measures in two problems where individuals represent string of bits and Assembly-language programs, respectively.}, notes = {Also known as \cite{2463495} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Goldman:2013:GECCO, author = {Brian W. Goldman and William F. Punch}, title = {Length bias and search limitations in cartesian genetic programming}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {933--940}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463482}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we examine how Cartesian Genetic Programming's (CGP's) method for encoding directed acyclic graphs (DAGs) and its mutation operator bias the effective length of individuals as well as the distribution of inactive nodes in the genome. We investigate these biases experimentally using two CGP variants as comparisons: Reorder, a method for shuffling node ordering without effecting individual evaluation, and DAG, a method for removing the concept of node position. Experiments were performed on four problems tailored to highlight potential search limitations, with further testing on the 3-bit multiplier problem. Unlike previous work, our experiments show that CGP has an innate parsimony pressure that makes it very difficult to evolve individuals with a high percentage of active nodes. This bias is particularly prevalent as the length of an individual increases. Furthermore, these problems are compounded by CGP's positional biases which can make some problems effectively unsolvable. Both Reorder and DAG appear to avoid these problems and outperform Normal CGP on preliminary benchmark testing. Finally, these new techniques require more reasonable genome sizes than those suggested in current CGP, with some evidence that solutions are also more terse.}, notes = {Also known as \cite{2463482} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Krawiec:2013:GECCO, author = {Krzysztof Krawiec and Tomasz Pawlak}, title = {Approximating geometric crossover by semantic backpropagation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {941--948}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463483}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel crossover operator for tree-based genetic programming, that produces approximately geometric offspring. We empirically analyse certain aspects of geometry of crossover operators and verify performance of the new operator on both, training and test fitness cases coming from set of symbolic regression benchmarks. The operator shows superior performance and higher probability of producing geometric offspring than tree-swapping crossover and other semantic-aware control methods.}, notes = {Also known as \cite{2463483} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Krawiec:2013:GECCOa, author = {Krzysztof Krawiec and Jerry Swan}, title = {Pattern-guided genetic programming}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {949--956}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463496}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Online progress in search and optimisation is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and Boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented.}, notes = {Also known as \cite{2463496} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Leitao:2013:GECCO, author = {Antonio Leitao and Penousal Machado}, title = {Self-adaptive mate choice for cluster geometry optimization}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {957--964}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463494}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Sexual Selection through Mate Choice has, over the past few decades, attracted the attention of researchers from various fields. They have gathered numerous supporting evidence, establishing Mate Choice as a major driving force of evolution, capable of shaping complex traits and behaviours. Despite its wide acceptance and relevance across various research fields, the impact of Mate Choice in Evolutionary Computation is still far from understood, both regarding performance and behaviour. In this study we describe a nature-inspired self-adaptive mate choice model, relying on a Genetic Programming representation tailored for the optimisation of Morse clusters, a relevant and widely accepted problem for benchmarking new algorithms, which provides a set of hard test instances. The model is coupled with a state-of-the-art hybrid steady-state approach and both its performance and behaviour are assessed with a particular interest on the replacement strategy's acceptance rate and diversity handling.}, notes = {Also known as \cite{2463494} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lopes:2013:GECCO, author = {Rui L. Lopes and Ernesto Costa}, title = {Genetic programming with genetic regulatory networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {965--972}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463488}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms (EA) approach differently from nature the genotype-phenotype relationship, and this view is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, we combine two of those alternatives, demonstrating experimentally how the resulting model can deal with complex problems, including those that have multiple outputs. The efficacy and efficiency of this variant are tested experimentally using two benchmark problems that show how we can evolve a controller or an artificial artist.}, notes = {Also known as \cite{2463488} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lopes:2013:GECCOa, author = {Rui L. Lopes and Ernesto Costa}, title = {GEARNet: grammatical evolution with artificial regulatory networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {973--980}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463490}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Central Dogma of Biology states that genes made proteins that made us. This principle has been revised in order to incorporate the role played by a multitude of regulatory mechanisms that are fundamental in both the processes of inheritance and development. Evolutionary Computation algorithms are inspired by the theories of evolution and development, but most of the computational models proposed so far rely on a simple genotype to phenotype mapping. During the last years some researchers advocate the need to explore computationally the new biological understanding and have proposed different gene expression models to be incorporated in the algorithms.Two examples are the Artificial Regulatory Network (ARN) model, first proposed by Wolfgang Banzhaf, and the Grammatical Evolution (GE) model, introduced by Michael O'Neill and Conor Ryan. In this paper, we show how a modified version of the ARN can be combined with the GE approach, in the context of automatic program generation. More precisely, we rely on the ARN to control the gene expression process ending in an ordered set of proteins, and on the GE to build, guided by a grammar, a computational structure from that set. As a proof of concept we apply the hybrid model to two benchmark problems and show that it is effective in solving them.}, notes = {Also known as \cite{2463490} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Meier:2013:GECCO, author = {Andreas Meier and Mark Gonter and Rudolf Kruse}, title = {Accelerating convergence in cartesian genetic programming by using a new genetic operator}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {981--988}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463481}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. In this paper, we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centres of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the Cartesian Genetic Programming algorithm and show that on our examples convergence is accelerated by over 50percent on average. We finish this paper with giving hints about parametrisation of the forking operator for other problems.}, notes = {Also known as \cite{2463481} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Moraglio:2013:GECCO, author = {Alberto Moraglio and Andrea Mambrini}, title = {Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {989--996}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463492}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP) that searches the semantic space of functions/programs. The fitness landscape seen by GSGP is always, for any domain and for any problem, unimodal with a linear slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. Very recent work proposed a runtime analysis of mutation-based GSGP on the class of all Boolean functions. We present a runtime analysis of mutation-based GSGP on the class of all regression problems with generic basis functions (encompassing e.g., polynomial regression and trigonometric regression).}, notes = {Also known as \cite{2463492} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Thorhauer:2013:GECCO, author = {Ann Thorhauer and Franz Rothlauf}, title = {Structural difficulty in grammatical evolution versus genetic programming}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {997--1004}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463491}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming (GP) has problems with structural difficulty as it is unable to search effectively for solutions requiring very full or very narrow trees. As a result of structural difficulty, GP has a bias towards narrow trees which means it searches effectively for solutions requiring narrow trees. This paper focuses on the structural difficulty of grammatical evolution (GE). In contrast to GP, GE works on variable-length binary strings and uses a grammar in Backus-Naur Form (BNF) to map linear genotypes to phenotype trees. The paper studies whether and how GE is affected by structural difficulty. For the analysis, we perform random walks through the search space and compare the structure of the visited solutions. In addition, we compare the performance of GE and GP for the Lid problem. Results show that GE representation is biased, this means it has problems with structural difficulty. For binary trees, GE has a bias towards narrow and deep structures; thus GE outperforms standard GP if optimal solutions are composed of very narrow and deep structures. In contrast, problems where optimal solutions require more dense trees are easier to solve for GP than for GE.}, notes = {Also known as \cite{2463491} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Turner:2013:GECCO, author = {Andrew James Turner and Julian Francis Miller}, title = {Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1005--1012}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463484}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Neuroevolution, the application of evolutionary algorithms to artificial neural networks (ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a graph-based form of Genetic Programming which can easily represent ANNs. Cartesian Genetic Programming encoded ANNs (CGPANNs) can evolve every aspect of an ANN: weights, topology, arity and node transfer functions. This makes CGPANNs very suited to situations where appropriate configurations are not known in advance. The effectiveness of CGPANNs is compared with a large number of previous methods on three benchmark problems. The results show that CGPANNs perform as well as or better than many other approaches. We also discuss the strength and weaknesses of each of the three benchmarks.}, notes = {Also known as \cite{2463484} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wieloch:2013:GECCO, author = {Bartosz Wieloch and Krzysztof Krawiec}, title = {Running programs backwards: instruction inversion for effective search in semantic spaces}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1013--1020}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463493}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The instructions used for solving typical genetic programming tasks have strong mathematical properties. In this study, we leverage one of such properties: invertibility. A search operator is proposed that performs an approximate reverse execution of program fragments, trying to determine in this way the desired semantics (partial outcome) at intermediate stages of program execution. The desired semantics determined in this way guides the choice of a subprogram that replaces the old program fragment. An extensive computational experiment on 20 symbolic regression and Boolean domain problems leads to statistically significant evidence that the proposed Random Desired Operator outperforms all typical combinations of conventional mutation and crossover operators.}, notes = {Also known as \cite{2463493} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Worm:2013:GECCO, author = {Tony Worm and Kenneth Chiu}, title = {Prioritized grammar enumeration: symbolic regression by dynamic programming}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1021--1028}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463486}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce Prioritised Grammar Enumeration (PGE), a deterministic Symbolic Regression (SR) algorithm using dynamic programming techniques. PGE maintains the tree-based representation and Pareto non-dominated sorting from Genetic Programming (GP), but replaces genetic operators and random number use with grammar production rules and systematic choices. PGE uses non-linear regression and abstract parameters to fit the coefficients of an equation, effectively separating the exploration for form, from the optimisation of a form. Memoisation enables PGE to evaluate each point of the search space only once, and a Pareto Priority Queue provides direction to the search. Sorting and simplification algorithms are used to transform candidate expressions into a canonical form, reducing the size of the search space. Our results show that PGE performs well on 22 benchmarks from the SR literature, returning exact formulae in many cases. As a deterministic algorithm, PGE offers reliability and reproducibility of results, a key aspect to any system used by scientists at large. We believe PGE is a capable SR implementation, following an alternative perspective we hope leads the community to new ideas.}, notes = {Also known as \cite{2463486} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Aler:2013:GECCO, author = {Ricardo Aler and Julia Handl and Joshua D. Knowles}, title = {Comparing multi-objective and threshold-moving ROC curve generation for a prototype-based classifier}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1029--1036}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463504}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all possible operating conditions, i.e., for all preferences regarding the tradeoff between false positives and false negatives. The generation of a ROC curve generally involves the training of a single classifier for a given set of operating conditions, with the subsequent use of threshold-moving to obtain a complete ROC curve. Recent work has shown that the generation of ROC curves may also be formulated as a multi-objective optimisation problem in ROC space: the goals to be minimised are the false positive and false negative rates. This technique also produces a single ROC curve, but the curve may derive from operating points for a number of different classifiers. This paper aims to provide an empirical comparison of the performance of both of the above approaches, for the specific case of prototype-based classifiers. Results on synthetic and real domains shows a performance advantage for the multi-objective approach.}, notes = {Also known as \cite{2463504} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Berka:2013:GECCO, author = {Tobias Berka and Helmut A. Mayer}, title = {Evolving artificial neural networks for nonlinear feature construction}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1037--1044}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463502}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We use neuroevolution to construct nonlinear transformation functions for feature construction that map points in the original feature space to augmented pattern vectors and improve the performance of generic classifiers. Our research demonstrates that we can apply evolutionary algorithms to both adapt the weights of a fully connected standard multi-layer perceptron (MLP), and optimise the topology of a generalised multi-layer perceptron (GMLP). The evaluation of the MLPs on four commonly used data sets shows an improvement in classification accuracy ranging from 4 to 13 percentage points over the performance on the original pattern set. The GMLPs obtain a slightly better accuracy and conserve 14percent to 54percent of all neurons and between 40percent and 89percent of all connections compared to the standard MLP.}, notes = {Also known as \cite{2463502} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Iqbal:2013:GECCO, author = {Muhammad Iqbal and Will N. Browne and Mengjie Zhang}, title = {Extending learning classifier system with cyclic graphs for scalability on complex, large-scale boolean problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1045--1052}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463500}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary computational techniques have had limited capabilities in solving large-scale problems, due to the large search space demanding large memory and much longer training time. Recently work has begun on autonomously reusing learnt building blocks of knowledge to scale from low dimensional problems to large-scale ones. An XCS-based classifier system has been shown to be scalable, through the addition of tree-like code fragments, to a limit beyond standard learning classifier systems. Self-modifying Cartesian genetic programming (SMCGP) can provide general solutions to a number of problems, but the obtained solutions for large-scale problems are not easily interpretable. A limitation in both techniques is the lack of a cyclic representation, which is inherent in finite state machines. Hence this work introduces a state-machine based encoding scheme into scalable XCS, for the first time, in an attempt to develop a general scalable classifier system producing easily interpretable classifier rules. The proposed system has been tested on four different Boolean problem domains, i.e. even-parity, majority-on, carry, and multiplexer problems. The proposed approach outperformed standard XCS in three of the four problem domains. In addition, the evolved machines provide general solutions to the even-parity and carry problems that are easily interpretable as compared with the solutions obtained using SMCGP.}, notes = {Also known as \cite{2463500} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kamath:2013:GECCO, author = {Uday Kamath and Carlotta Domeniconi and Kenneth A. De Jong}, title = {An analysis of a spatial EA parallel boosting algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1053--1060}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463503}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The scalability of machine learning (ML) algorithms has become a key issue as the size of training datasets continues to increase. To address this issue in a reasonably general way, a parallel boosting algorithm has been developed that combines concepts from spatially structured evolutionary algorithms (SSEAs) and ML boosting techniques. To get more insight into the algorithm, a proper theoretical and empirical analysis is required. This paper is a first step in that direction. First, it establishes the connection between this algorithm and well known density estimation and mixture model approaches used by the machine learning community. The paper then analyses the algorithm in terms of various theoretical and empirical properties such as convergence to large margins, scalability effects on accuracy and speed, robustness to noise, and connections to support vector machines in terms of instances converged to.}, notes = {Also known as \cite{2463503} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Koutnik:2013:GECCO, author = {Jan Koutn\'{\i}k and Giuseppe Cuccu and J\"{u}rgen Schmidhuber and Faustino Gomez}, title = {Evolving large-scale neural networks for vision-based reinforcement learning}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1061--1068}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463509}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The idea of using evolutionary computation to train artificial neural networks, or neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 years. However, as RL tasks become more challenging, the networks required become larger, as do their genomes. But, scaling NE to large nets (i.e. tens of thousands of weights) is infeasible using direct encodings that map genes one-to-one to network components. In this paper, we scale-up our compressed network encoding where network weight matrices are represented indirectly as a set of Fourier-type coefficients, to tasks that require very-large networks due to the high-dimensionality of their input space. The approach is demonstrated successfully on two reinforcement learning tasks in which the control networks receive visual input: (1) a vision-based version of the octopus control task requiring networks with over 3 thousand weights, and (2) a version of the TORCS driving game where networks with over 1 million weights are evolved to drive a car around a track using video images from the driver's perspective.}, notes = {Also known as \cite{2463509} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kovacs:2013:GECCO, author = {Tim Kovacs and Robin Tindale}, title = {Analysis of the niche genetic algorithm in learning classifier systems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1069--1076}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465803}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning Classifier Systems (LCS) evolve IF-THEN rules for classification and control tasks. The earliest Michigan-style LCS used a panmictic Genetic Algorithm (GA) (in which all rules compete for selection) but newer ones tend to use a niche GA (in which only a certain subset of rules compete for selection at any one time). The niche GA was thought to be advantageous in all learning tasks, but recent research suggests it has difficulties when the rules composing the solution overlap. Furthermore, the niche GA's effects are implicit, making it difficult study, and fixed, which prevents tuning its performance. Given these issues, we set out on a long-term project to reevaluate the niche GA. This work is our starting point and in it we address the implicit and unquantified effects of the niche GA by building a mathematical model of the probability of rule selection. This model reveals a number of insights into the components of rule fitness, particularly the bonus for rule generality and penalty for overlaps, both previously unquantified. These theoretical results are our primary contribution. However, to demonstrate one way to apply this theory, we then introduce a new variant of the UCS algorithm, which uses a hybrid panmictic/niche GA. Preliminary results suggest, unexpectedly, that the niche GA may have even more drawbacks than previously thought.}, notes = {Also known as \cite{2465803} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kowaliw:2013:GECCO, author = {Taras Kowaliw and Wolfgang Banzhaf and Ren\'{e} Doursat}, title = {Networks of transform-based evolvable features for object recognition}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1077--1084}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463507}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose an evolutionary feature creator (EFC) to explore a non-linear and offline method for generating features in image recognition tasks. Our model aims at extracting low-level features automatically when provided with an arbitrary image database. In this work, we are concerned with the addition of algorithmic depth to a genetic programming (GP) system, suggesting that it will improve the capacity for solving problems that require high-level, hierarchical reasoning. For this we introduce a network superstructure that co-evolves with our low-level GP representations. Two approaches are described: the first uses our previously used 'shallow' GP system, the second presents a new 'deep' GP system that involves this network superstructure. We evaluate these models against a benchmark object recognition database. Results show that the deep structure outperforms the shallow one in generating features that support classification, and does so without requiring significant additional computational time. Further, high accuracy is achieved on the standard ETH-80 classification task, also outperforming many existing specialised techniques. We conclude that our EFC is capable of data-driven extraction of useful features from an object recognition database.}, notes = {Also known as \cite{2463507} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Nakata:2013:GECCO, author = {Masaya Nakata and Pier Luca Lanzi and Keiki Takadama}, title = {Selection strategy for XCS with adaptive action mapping}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1085--1092}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463508}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {XCS with Adaptive Action Mapping (XCSAM) evolves solutions focused on classifiers that advocate the best action in every state. Accordingly, XCSAM usually evolves more compact solutions than XCS which, in contrast, works toward solutions representing complete state-action mappings. Experimental results have however shown that, in some problems, XCSAM may produce bigger populations than XCS. In this paper, we extend XCSAM with a novel selection strategy to reduce, even further, the size of the solutions XCSAM produces. The proposed strategy selects the parent classifiers based both on their fitness values (like XCS) and on the effect they have on the adaptive map. We present experimental results showing that XCSAM with the new selection strategy can evolve more compact solutions than XCS which, at the same time, are also maximally general and maximally accurate.}, notes = {Also known as \cite{2463508} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Naredo:2013:GECCO, author = {Enrique Naredo and Leonardo Trujillo}, title = {Searching for novel clustering programs}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1093--1100}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463505}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an explicit objective function. Instead, NS focuses selective pressure on the search for novel solutions. NS has produced intriguing results in specialised domains, but has not been applied in most machine learning areas. The key component of NS is that each individual is described by the behaviour it exhibits, and this description is used to determine how novel each individual is with respect to what the search has produced thus far. However, describing individuals in behavioural space is not trivial, and care must be taken to properly define a descriptor for a particular domain. This paper applies NS to a mainstream pattern analysis area: data clustering. To do so, a descriptor of clustering performance is proposed and tested on several problems, and compared with two control methods, Fuzzy C-means and K-means. Results show that NS can effectively be applied to data clustering in some circumstances. NS performance is quite poor on simple or easy problems, achieving basically random performance. Conversely, as the problems get harder NS performs better, and outperforming the control methods. It seems that the search space exploration induced by NS is fully exploited only when generating good solutions is more challenging.}, notes = {Also known as \cite{2463505} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Tay:2013:GECCO, author = {Darwin Tay and Chueh Loo Poh and Richard Kitney}, title = {An evolutionary data-conscious artificial immune recognition system}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1101--1108}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463499}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial Immune Recognition System (AIRS) algorithm offers a promising methodology for data classification. It is an immune-inspired supervised learning algorithm that works efficiently and has shown comparable performance with respect to other classifier algorithms. For this reason, it has received escalating interests in recent years. However, the full potential of the algorithm was yet unleashed. We proposed a novel algorithm called the evolutionary data-conscious AIRS (EDC-AIRS) algorithm that accentuates and capitalises on 3 additional immune mechanisms observed from the natural immune system. These mechanisms are associated to the phenomena exhibited by the antibodies in response to the concentration, location and type of foreign antigens. Bio-mimicking these observations empower EDC-AIRS algorithm with the ability to robustly adapt to the different density, distribution and characteristics exhibited by each data class. This provides competitive advantages for the algorithm to better characterise and learn the underlying pattern of the data. Experiments on four widely used benchmarking datasets demonstrated promising results, outperforming several state-of-the-art classification algorithms evaluated. This signifies the importance of integrating these immune mechanisms as part of the learning process.}, notes = {Also known as \cite{2463499} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Vargas:2013:GECCO, author = {Danilo V. Vargas and Hirotaka Takano and Junichi Murata}, title = {Self organizing classifiers and niched fitness}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1109--1116}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463501}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalisation problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organising Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organises itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature.}, notes = {Also known as \cite{2463501} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Veeramachaneni:2013:GECCO, author = {Kalyan Veeramachaneni and Owen Derby and Dylan Sherry and Una-May O'Reilly}, title = {Learning regression ensembles with genetic programming at scale}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1117--1124}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463506}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we examine the challenge of producing ensembles of regression models for large datasets. We generate numerous regression models by concurrently executing multiple independent instances of a genetic programming learner. Each instance may be configured with different parameters and a different subset of the training data. Several strategies for fusing predictions from multiple regression models are compared. To overcome the small memory size of each instance, we challenge our framework to learn from small subsets of training data and yet produce a prediction of competitive quality after fusion. This decreases the running time of learning which produces models of good quality in a timely fashion. Finally, we examine the quality of fused predictions over the progress of the computation.}, notes = {Also known as \cite{2463506} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Brys:2013:GECCO, author = {Tim Brys and Madalina M. Drugan and Peter A.N. Bosman and Martine De Cock and Ann Now\'{e}}, title = {Solving satisfiability in fuzzy logics by mixing CMA-ES}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1125--1132}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463510}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Satisfiability in propositional logic is well researched and many approaches to checking and solving exist. In infinite-valued or fuzzy logics, however, there have only recently been attempts at developing methods for solving satisfiability. In this paper, we propose new benchmark problems and analyse the function landscape of different problem classes, focusing our analysis on plateaus. Based on this study, we develop Mixing CMA-ES (M-CMA-ES), an extension to CMA-ES that is well suited to solving problems with many large plateaus. We empirically show the relation between certain function landscape properties and M-CMA-ES performance.}, notes = {Also known as \cite{2463510} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chou:2013:GECCO, author = {Chih-Yuan Chou and Tian-Li Yu}, title = {Using representative strategies for finding nash equilibria}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1133--1140}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463511}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Since the existence of at least one mixed Nash equilibrium (NE) for any game was proved by Nash, finding NE has been an important issue in the field of game theory. However, polynomial-time algorithms for such task have not yet been discovered, and one of the difficulties is the infinite search space. In this paper, we define the so-called e-representative strategy to reduce the search space. In general, the equilibria on these representative strategies are not the original equilibria but approximations.To find such approximate equilibria, we then propose a two-level method, which firstly uses co-evolutionary algorithms to co-evolve the representative strategies for each player and then the approximate equilibria.The computational time can be controlled by the parameters of the co-evolutionary algorithms. Empirical results show that our method finds the approximate NE in a reasonable time. Finally, the definitions developed in this paper help define the co-evolvability of NE.}, notes = {Also known as \cite{2463511} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Jaskowski:2013:GECCO, author = {Wojciech Ja\'{s}kowski and Pawe\l Liskowski and Marcin Szubert and Krzysztof Krawiec}, title = {Improving coevolution by random sampling}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1141--1148}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463512}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent developments cast doubts on the effectiveness of coevolutionary learning in interactive domains. A simple evolution with fitness evaluation based on games with random strategies has been found to generalise better than competitive coevolution. In an attempt to investigate this phenomenon, we analyse the utility of random opponents for one and two-population competitive coevolution applied to learning strategies for the game of Othello. We show that if coevolution uses two-population setup and engages also random opponents, it is capable of producing equally good strategies as evolution with random sampling for the expected utility performance measure. To investigate the differences between analysed methods, we introduce performance profile, a tool that measures the player's performance against opponents of various strength. The profiles reveal that evolution with random sampling produces players coping well with mediocre opponents, but playing relatively poorly against stronger ones. This finding explains why in the round-robin tournament, evolution with random sampling is one of the worst methods from all those considered in this study.}, notes = {Also known as \cite{2463512} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Szubert:2013:GECCO, author = {Marcin Szubert and Wojciech Ja\'{s}kowski and Pawe\l Liskowski and Krzysztof Krawiec}, title = {Shaping fitness function for evolutionary learning of game strategies}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1149--1156}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463513}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In evolutionary learning of game-playing strategies, fitness evaluation is based on playing games with certain opponents. In this paper we investigate how the performance of these opponents and the way they are chosen influence the efficiency of learning. For this purpose we introduce a simple method for shaping the fitness function by sampling the opponents from a biased performance distribution. We compare the shaped function with existing fitness evaluation approaches that sample the opponents from an unbiased performance distribution or from a coevolving population. In an extensive computational experiment we employ these methods to learn Othello strategies and assess both the absolute and relative performance of the elaborated players. The results demonstrate the superiority of the shaping approach, and can be explained by means of performance profiles, an analytical tool that evaluate the evolved strategies using a range of variably skilled opponents.}, notes = {Also known as \cite{2463513} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lanza-Gutierrez:2013:GECCO, author = {Jose M. Lanza-Gutierrez and Juan A. Gomez-Pulido and Miguel A. Vega-Rodriguez and Juan M. Sanchez-Perez}, title = {A parallel evolutionary approach to solve the relay node placement problem in wireless sensor networks}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1157--1164}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463517}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {At this time, Wireless Sensor Networks (WSNs) are widely used in many fields. This kind of network has some attractive features that have promoted their use, such as the absence of wires and the use of low-cost devices. However, WSNs also have important shortcomings that affect some features like energy cost and quality of service. In this paper, we optimise traditional static WSNs (a set of sensors and a sink node) by means of adding routers to simultaneously optimise a couple of important factors: energy consumption and average coverage. This multiobjective optimisation problem was solved in a previous work using two genetic algorithms (NSGA-II and SPEA2) which had an important limitation: the computing time was very high and then, to address complex instances was difficult. In this paper, both algorithms are parallelised using OpenMP in order to reduce the computing time, and a more realistic data set is included. The results obtained are analysed in depth from both multiobjective and parallel viewpoints. A Quite good efficiency is obtained with a wide range of processing cores, observing that NSGA-II provides the best results in small and medium instances, but in the largest ones the behaviour of both algorithms is similar.}, notes = {Also known as \cite{2463517} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lepping:2013:GECCO, author = {Joachim Lepping and Panayotis Mertikopoulos and Denis Trystram}, title = {Accelerating population-based search heuristics by adaptive resource allocation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1165--1172}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463514}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We investigate a dynamic, adaptive resource allocation scheme with the aim of accelerating the convergence of multi-start population-based search heuristics (PSHs) running on multiple parallel processors. Given that each initialisation of a PSH performs differently over time, we develop an exponential learning scheme which allocates computational resources (processors) to each variant in an online manner, based on the performance level attained by each initialisation. For the well-known example of (mu+lambda)-evolution strategies, we show that the time required to reach the target quality level of a given optimisation problem is significantly reduced and that the use of the parallel system is likewise optimised. Our learning approach is easily implementable with currently available batch management systems and provides notable performance improvements without modifying the employed PSH, so it is very well-suited to improve the performance of PSHs in large-scale parallel computing environments.}, notes = {Also known as \cite{2463514} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Martens:2013:GECCO, author = {Marcus M\"{a}rtens and Dario Izzo}, title = {The asynchronous island model and NSGA-II: study of a new migration operator and its performance}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1173--1180}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463516}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents an implementation of the asynchronous island model suitable for multi-objective evolutionary optimisation on heterogeneous and large-scale computing platforms. The migration of individuals is regulated by the crowding comparison operator applied to the originating population during selection and to the receiving population augmented by all migrants during replacement. Experiments using this method combined with NSGA-II show its scalability up to 128 islands and its robustness. Furthermore, the proposed parallelisation technique consistently outperforms a multi-start and a random migration approach in terms of convergence speed, while maintaining a comparable population diversity. Applied to a real-world problem of interplanetary trajectory design, we find solutions dominating an actual NASA/ESA mission proposal for a tour from Earth to Jupiter, in a fraction of the computational time that would be needed on a single CPU.}, notes = {Also known as \cite{2463516} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Mirsoleimani:2013:GECCO, author = {Sayyed Ali Mirsoleimani and Ali Karami and Farshad Khunjush}, title = {A parallel memetic algorithm on GPU to solve the task scheduling problem in heterogeneous environments}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1181--1188}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463518}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hybrid metaheuristics have shown their capabilities to solve NP-hard problems. However, they exhibit significantly higher execution times in comparison to deterministic approaches. Parallel techniques are usually leveraged to overcome the execution time bottleneck for various metaheuristics. Recently, GPUs have emerged as general purpose parallel processors and have been harnessed to reduce the execution time of these algorithms. In this work, we propose a novel parallel memetic algorithm which is fully offloaded onto GPUs. In addition, we propose an adaptive sorting strategy in order to achieve maximum possible speedups for discrete optimisation problems on GPUs. In order to show the efficacy of our algorithm, a task scheduling problem for heterogeneous environments is chosen as a case study. The output of this problem can have a tangible impact on overall performance of parallel heterogeneous platforms. The achieved results of our approach are promising and show up to 696x speedup in comparison to the sequential approach for various versions of this problem. Moreover, the effects of key parameters of memetic algorithms in terms of execution time and solution quality are investigated.}, notes = {Also known as \cite{2463518} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Melab:2013:GECCO, author = {Nouredine Melab and Th\'{e} Van Luong and Karima Boufaras and El-Ghazali Talbi}, title = {ParadisEO-MO-GPU: a framework for parallel GPU-based local search metaheuristics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1189--1196}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465804}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a pioneering framework called ParadisEO-MO-GPU for the reusable design and implementation of parallel local search metaheuristics (S-Metaheuristics)on Graphics Processing Units (GPU). We revisit the ParadisEO-MO software framework to allow its use on GPU accelerators focusing on the parallel iteration-level model, the major parallel model for S-Metaheuristics. It consists in the parallel exploration of the neighbourhood of a problem solution. The challenge is on the one hand to rethink the design and implementation of this model optimising the data transfer between the CPU and the GPU. On the other hand, the objective is to make the GPU as transparent as possible for the user minimising his or her involvement in its management. In this paper, we propose solutions to this challenge as an extension of the ParadisEO framework. The first release of the new GPU-based ParadisEO framework has been experimented on the permuted perceptron problem. The preliminary results are convincing, both in terms of flexibility and easiness of reuse at implementation, and in terms of efficiency at execution on GPU.}, notes = {Also known as \cite{2465804} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Shao:2013:GECCO, author = {Chung-Yu Shao and Tian-Li Yu}, title = {Speeding up model building for ECGA on CUDA platform}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1197--1204}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463515}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parallelisation is a straightforward approach to enhance the efficiency for evolutionary computation due to its inherently parallel nature. Since NVIDIA released the compute unified device architecture (CUDA), graphic processing units have enabled lots of scalable parallel programs in a wide range of fields. However, parallelisation of model building for EDAs is rarely studied. In this paper, we propose two implementations on CUDA to speed up the model building in the extended compact genetic algorithm (ECGA). The first implementation is algorithmically identical to original ECGA. Aiming at a greater speed boost, the second implementation modifies the model building. It slightly decreases the accuracy of models in exchange for more speedup. Empirically, the first implementation achieves a speedup of roughly 359 to the baseline on 500-bit trap problem with order 5, and the second implementation achieves a speedup of roughly 506 to the baseline on the same problem. Finally, both of our implementations scale up to 9,800-bit trap problem with order~5 on one single Tesla C2050 GPU card.}, notes = {Also known as \cite{2463515} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{:2013:GECCOb, author = {Abr\, {a}o, Pedro de Lima and Wanner, Elizabeth Fialho and Almeida, Paulo Eduardo Maciel de}, title = {A novel movable partitions approach with neural networks and evolutionary algorithms for solving the hydroelectric unit commitment problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1205--1212}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463523}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a method based on Neural Networks and Evolutionary Algorithms to solve the Hydroelectric Unit Commitment Problem. A Neural Network is used to model the production function and a novel approach based on movable partitions is proposed, which makes it easier to model the desired power output equality constraint in the optimisation modelling. Three evolutionary algorithms are tested in order to find optimised operation points: differential evolution DE/best/1/bin, a balanced version of DE and Particle Swarm Optimisation algorithm (PSO). The results show that the proposed method is effective in terms of water consumption, reaching in some cases more than 1percent of economy whether compared to the traditional commitment strategy.}, notes = {Also known as \cite{2463523} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bate:2013:GECCO, author = {Iain Bate and Mark Fairbairn}, title = {Searching for the minimum failures that can cause a hazard in a wireless sensor network}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1213--1220}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463520}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Wireless Sensor Networks (WSN) are now being used in a range of applications, many of which are critical systems, e.g. monitoring assisted living facilities or for fire detection systems which is the example used in this paper. For critical systems it is important to be able to determine the minimum number of failures that can cause a hazard to occur. This is normally a manual, human intensive, task. This paper presents a novel application of search to both the WSN and safety domains; searching for combinations of failures that can cause a hazard and then reducing these to the minimum possible using a combination of automated search and manual refinement. Due to the size and complexity of the search problem, a parallel search algorithm is designed that runs on available compute resources with the results being processed using R.}, notes = {Also known as \cite{2463520} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Brochero:2013:GECCO, author = {Darwin Brochero and Christian Gagn\'{e} and Fran\c{c}ois Anctil}, title = {Evolutionary multiobjective optimization for selecting members of an ensemble streamflow forecasting model}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1221--1228}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463538}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We are proposing to use the Nondominated Sorting Genetic Algorithm II (NSGA-II) for optimising a hydrological forecasting model of 800 simultaneous stream flow predictors. The optimisation is based on the selection of the best 48 predictors from the 800 that jointly define the 'best' ensemble in terms of two probabilistic criteria. Results showed that the difficulties in simplifying the ensembles mainly originate from the preservation of the system reliability. We conclude that Pareto fronts generated with NSGA-II allow the development of a decision process based explicitly on the trade-off between different probabilistic properties. In other words, evolutionary multiobjective optimisation offers more flexibility to the operational hydrologists than a priori methods that produce only one selection.}, notes = {Also known as \cite{2463538} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Chaaraoui:2013:GECCO, author = {Alexandros Andre Chaaraoui and Francisco Fl\'{o}rez-Revuelta}, title = {Human action recognition optimization based on evolutionary feature subset selection}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1229--1236}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463529}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Human action recognition constitutes a core component of advanced human behaviour analysis. The detection and recognition of basic human motion enables to analyse and understand human activities, and to react proactively providing different kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimisation for human action recognition is proposed. The resulting recognition rate and computational cost are significantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection. The introduced feature is computed using only the contour points of human silhouettes. These are spatially aligned based on a radial scheme. This definition shows to be proficient for feature subset selection, since different parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.}, notes = {Also known as \cite{2463529} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Clemente:2013:GECCO, author = {Eddie Clemente and Francisco Ch\'{a}vez and Le\'{o}n Dozal and Francisco Fern\'{a}ndez de Vega and Gustavo Olague}, title = {Self-adjusting focus of attention by means of GP for improving a laser point detection system}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1237--1244}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463530}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces the application of a new GP based Focus of Attention technique capable of improving the accuracy level when using a Laser Pointer as an interactive device. Laser Pointers have been previously employed in combination with environment control systems as interaction devices, allowing users to send orders to devices. Accurate detection of laser spots is required for sending correct orders; moreover, false offs must be eradicated, thus preventing devices to autonomously activate/deactivate when orders have not been sent by users. The idea here is to apply a self-adjusting process to a GP based algorithm capable of focusing the attention of a visual recognition system on a narrow area of an image, where laser spots will be then located. Images are taken by video cameras working on users' environment. The results show that the new approach improves significantly the accuracy level when laser spots are present, users sending orders while maintains the extremely low values of false offs provided by previous techniques.}, notes = {Also known as \cite{2463530} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Das:2013:GECCO, author = {Swagatam Das and Rohan Mukherjee and Rupam Kundu and Thanos Vasilakos}, title = {Multi-user detection in multi-carrier CDMA wireless broadband system using a binary adaptive differential evolution algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1245--1252}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463543}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-Carrier Code Division Multiple Access (MC-CDMA) is an emerging wireless communication technology that incorporates the advantages of Orthogonal Frequency Division Multiplexing (OFDM) into the original Code Division Multiple Access (CDMA) technique. But it suffers from the inherent defect called Multiple Access Interference (MAI) due to inappropriate cross-correlation possessed by the different user codes. To reduce MAI, the multi-user detection (MUD) technique has already been proposed in which MAI is treated as noise. Due to high computational cost incorporated by the optimal MUD detector with increasing number of users, researchers are looking for sub-optimal MUD solutions. This paper proposes a binary adaptive Differential Evolution algorithm with a novel crossover strategy (MBDE_pBX) for multi-user detection in a synchronous MC-CDMA system. Since MUD detection in MC-CDMA systems is a problem in binary domain, a binary encoding rule is introduced which converts a binary domain problem of any number of dimensions into a 4-dimensional continuous domain problem. The simulation results show that this new binary Differential Evolution variant can achieve superior bit error rate (BER) performance within much lower optimum solution detection time outperforming its competitors as well as achieving 99.62percent reduction in computational complexity as compared to the MUD scheme using exhaustive search.}, notes = {Also known as \cite{2463543} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{DeLorenzo:2013:GECCO, author = {Andrea De Lorenzo and Eric Medvet and Alberto Bartoli}, title = {Automatic string replace by examples}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1253--1260}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463532}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Search-and-replace is a text processing task which may be largely automated with regular expressions: the user must describe with a specific formal language the regions to be modified (search pattern) and the corresponding desired changes (replacement expression). Writing and tuning the required expressions requires high familiarity with the corresponding formalism and is typically a lengthy, error-prone process. In this paper we propose a tool based on Genetic Programming (GP) for generating automatically both the search pattern and the replacement expression based only on examples. The user merely provides examples of the input text along with the desired output text and does not need any knowledge about the regular expression formalism nor about GP. We are not aware of any similar proposal. We experimentally evaluated our proposal on 4 different search-and-replace tasks operating on real-world datasets and found good results, which suggests that the approach may indeed be practically viable.}, notes = {Also known as \cite{2463532} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Gao:2013:GECCO, author = {Kaizhou Gao and Ponnuthurai Nagaratnam Suganthan and Tay Jin Chua and Tian Xiang Cai and Chin Soon Chong}, title = {Hybrid discrete harmony search algorithm for scheduling re-processing problem in remanufacturing}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1261--1268}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463526}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a hybrid discrete harmony search algorithm for solving the re-processing scheduling problem in pump re-manufacturing. The process of pump remanufacturing and the scheduling problem of re-processing for pump sub-assembly are modelled. An experience based strategy is proposed for solving the unpredictability of subassembly re-processing time in remanufacturing. Hybrid discrete harmony search algorithm and local search are employed for scheduling re-processing of pump subassembly. The objectives of pump subassembly re-processing scheduling are minimisation of the maximum completion time (makespan), and the mean of earliness and tardiness (E/T). These objectives are considered individually as well as together as a multi-objective problem. Computational experiments are carried out using real data from a pump remanufacturing enterprise. Computational results show that the objectives makespan and E/T can be optimised and the resulting schedules can be used in practice.}, notes = {Also known as \cite{2463526} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Hanif:2013:GECCO, author = {Ayub Hanif and Robert Elliott Smith}, title = {Stochastic volatility modeling with computational intelligence particle filters}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1269--1276}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463519}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Stochastic volatility estimation is an important task for correctly pricing derivatives in mathematical finance. Such derivatives are used by varying types of market participant as either hedging tools or for bespoke market exposure. We evaluate our adaptive path particle filter, a recombinatory evolutionary algorithm based on the generation gap concept from evolutionary computation, for stochastic volatility estimation of three real financial asset time series. We calibrate the Heston stochastic volatility model employing a Markov-chain Monte Carlo, enabling us to understand the latent stochastic volatility process and parameters. In our experiments we find the adaptive path particle filter to be superior to the standard sequential importance resampling particle filter, the Markov-chain Monte Carlo particle filter and the particle learning particle filter. We present a detailed analysis of the results and suggest directions for future research.}, notes = {Also known as \cite{2463519} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Hegels:2013:GECCO, author = {Daniel Hegels and Heinrich M\"{u}ller}, title = {Evolutionary path generation for reduction of thermal variations in thermal spray coating}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1277--1284}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463521}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Thermal spraying is a production process which consists of spraying hot material onto a workpiece surface in order to form a coating of a desired thickness. This paper describes a path generation algorithm for industrial robot-based thermal spraying which generates the desired coating as well as keeps the thermal variation on the object surface during the process low. The problem is formulated as a discrete optimisation problem which includes the quality of the particle coating and the physics of heat induction, heat diffusion and cooling of the surface. The optimisation problem is solved by an Evolutionary Algorithm. By specific mutation operators, self-adaptation, and dropping the concept of generations, an improvement of the quality of the results of over 25percent compared to standard operations is achieved. The evolutionary results overall outperform the solutions generated by the often-used strategy of direction-parallel paths.}, notes = {Also known as \cite{2463521} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Hogan:2013:GECCO, author = {Damien Hogan and Tom Arbuckle and Conor Ryan}, title = {Estimating MLC NAND flash endurance: a genetic programming based symbolic regression application}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1285--1292}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463537}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {NAND Flash memory is a multi-billion dollar industry which is projected to continue to show significant growth until at least 2017. Devices such as smart-phones, tablets and Solid State Drives use NAND Flash since it has numerous advantages over Hard Disk Drives including better performance, lower power consumption, and lower weight. However, storage locations within Flash devices have a limited working lifetime, as they slowly degrade through use, eventually becoming unreliable and failing. The number of times a location can be programmed is termed its endurance, and can vary significantly, even between locations within the same device. There is currently no technique available to predict endurance, resulting in manufacturers placing extremely conservative specifications on their Flash devices. We perform symbolic regression using Genetic Programming to estimate the endurance of storage locations, based only on the duration of program and erase operations recorded from them. We show that the quality of estimations for a device can be refined and improved as the device continues to be used, and investigate a number of different approaches to deal with the significant variations in the endurance of storage locations. Results show this technique's huge potential for real-world application.}, notes = {Also known as \cite{2463537} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Iscen:2013:GECCO, author = {Atil Iscen and Adrian Agogino and Vytas SunSpiral and Kagan Tumer}, title = {Controlling tensegrity robots through evolution}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1293--1300}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463525}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralised evolutionary algorithm performs 400percent better than a hand-coded solution, while the multiagent evolution performs 800percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.}, notes = {Also known as \cite{2463525} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Izzo:2013:GECCO, author = {Dario Izzo and Sim\, {o}es, Lu\'{\i}s F. and M\"{a}rtens, Marcus and de Croon, Guido C.H.E. and Heritier, Aurelie and Yam, Chit Hong}, title = {Search for a grand tour of the jupiter galilean moons}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1301--1308}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463524}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We make use of self-adaptation in a Differential Evolution algorithm and of the asynchronous island model to design a complex interplanetary trajectory touring the Galilean Jupiter moons (Io, Europa, Ganymede and Callisto) using the multiple gravity assist technique. Such a problem was recently the subject of an international competition organised by the Jet Propulsion Laboratory (NASA) and won by a trajectory designed by aerospace experts and reaching the final score of 311/324. We apply our method to the very same problem finding new surprising designs and orbital strategies and a score of up to 316/324.}, notes = {Also known as \cite{2463524} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Johns:2013:GECCO, author = {Matthew Barrie Johns and Edward Keedwell and Dragan Savic}, title = {Pipe smoothing genetic algorithm for least cost water distribution network design}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1309--1316}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463533}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes the development of a Pipe Smoothing Genetic Algorithm (PSGA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we propose a pipe smoothing based approach to the creation and mutation of chromosomes which uses engineering expertise with the view to increasing the performance of the algorithm compared to a standard genetic algorithm. Both PSGA and the standard genetic algorithm were tested on benchmark water distribution networks from the literature. In all cases PSGA achieves higher optimality in fewer solution evaluations than the standard genetic algorithm.}, notes = {Also known as \cite{2463533} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kazakova:2013:GECCO, author = {Vera A. Kazakova and Annie S. Wu and Talat S. Rahman}, title = {Cluster energy optimizing genetic algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1317--1324}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463536}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nanoclusters are small clumps of atoms of one or several materials. A cluster possesses a unique set of material properties depending on its configuration (i.e. the number of atoms, their types, and their exact relative positioning). Finding and subsequently testing these configurations is of great interest to physicists in search of new advantageous material properties. To facilitate the discovery of ideal cluster configurations, we propose the Cluster Energy Optimising GA (CEO-GA), which combines the strengths of Johnston's BCGA [18], Pereira's H-C&S crossover [25], and two new mutation operators: Local Spherical and Center of Mass Spherical. The advantage of CEO-GA is its ability to evolve optimally stable clusters (those with lowest potential energy) without relying on local optimisation methods, as do other commonly used cluster evolving GAs, such as BCGA.}, notes = {Also known as \cite{2463536} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kiperwasser:2013:GECCO, author = {Eliyahu Kiperwasser and Omid David and Nathan S. Netanyahu}, title = {A hybrid genetic approach for stereo matching}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1325--1332}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463542}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present a genetic algorithm (GA)-based approach for the stereo matching problem. More precisely, the approach presented is a combination of a simple dynamic programming algorithm, commonly used for stereo matching, with a practical GA-based optimisation scheme. The performance of our scheme was evaluated on standard test data of the Middlebury benchmark. Specifically, the number of incorrect disparities on these data decreases by approximately 20percent in comparison to the original approach (without the use of a GA).}, notes = {Also known as \cite{2463542} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lattarulo:2013:GECCO, author = {Valerio Lattarulo and Pranay Seshadri and Geoffrey T. Parks}, title = {Optimization of a supersonic airfoil using the multi-objective alliance algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1333--1340}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463531}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A baseline NACA0012 two-dimensional (2D) air-foil is optimised for supersonic flight conditions using a recently introduced optimisation algorithm: the multi-objective alliance algorithm (MOAA). The efficacy of the algorithm is demonstrated through comparisons with NSGA-II for 300, 600 and 1000 function evaluations. Through epsilon/hypervolume indicators and the Mann-Whitney statistical test, we show that MOAA outperforms NSGA-II on this problem.}, notes = {Also known as \cite{2463531} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Loginov:2013:GECCO, author = {Alexander Loginov and Malcolm I. Heywood}, title = {On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1341--1348}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463522}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most research into frameworks for evolving trading agents emphasise aspects associated with the evolution of technical indicators and decision trees / rules. One of the factors that drives the development of such frameworks is the non-stationary, streaming nature of the task. However, it is the heuristics used to interface the evolutionary framework to the streaming data which potentially have most impact on the quality of the resulting trading agents. We demonstrate that including a validation partition has a significant impact on determining the overall success of the trading agents. Moreover, rather than conduct evolution on a continuous basis, only retraining when changes in trading quality are detected also yields significant advantages. Neither of these heuristics are widely recognised by research in evolving trading agent frameworks, although both are relatively easy to add to current frameworks. Benchmarking over a 3 year period of the EURUSD foreign exchange supports these findings.}, notes = {Also known as \cite{2463522} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Nielsen:2013:GECCO, author = {Sune S. Nielsen and Gregoire Danoy and Pascal Bouvry}, title = {Vehicular mobility model optimization using cooperative coevolutionary genetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1349--1356}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463539}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A key factor for accurate vehicular ad hoc networks (VANET) simulation is the quality of its underlying mobility model. VehILux is a recent vehicular mobility model that generates traces using traffic volume counts and real-world map data. This model uses probabilistic attraction points which values require optimisation to provide realistic traces. Previous sensitivity analysis and application of genetic algorithms (GAs) on the Luxembourg problem instance have outlined this model's limitations. In this article, we first propose an extension of the model using a higher number of auto-generated attraction points. Then its decomposition on the Luxembourg instance using geographical information is proposed as a way to break epistatic links and hence make its optimisation using cooperative coevolutionary genetic algorithms (CCGAs) more efficient. Experimental results demonstrate the significant realism increase brought by both the VehILux model enhancements and the CCGA compared to the generational and cellular GAs.}, notes = {Also known as \cite{2463539} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Shen:2013:GECCO, author = {Yan Shen and Sarang Khim and Won Jun Sung and Sungjin Hong and Phill Kyu Rhee}, title = {Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1357--1364}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463527}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents an evolutionary and adaptive framework for efficient visual tracking based on a hybrid POMDP formulation. The main focus is to guarantee visual tracking performance under varying environments without strongly-controlled situations or high-cost devices. The performance optimisation is formulated as a dynamic adaptation of the system control parameters, i.e., threshold and adjusting parameters in a visual tracking algorithm, based on the hybrid of offline and online POMDPs. The hybrid POMDP allows the agent to construct world-belief models under uncertain environments in offline, and focus on the optimisation of the system control parameters over the current world model in real-time. Since the visual tracking should satisfy strict real-time constraints, we restrict our attention to simpler and faster approaches instead of exploring the belief space of each world model directly. The hybrid POMDP is thus solved by an evolutionary adaptive framework employing the GA (Genetic Algorithm) and real-time Q-learning approaches in the optimally reachable genotype and phenotype spaces, respectively. Experiments were carried out extensively in the area of eye tracking using videos of various structures and qualities, and yielded very encouraging results. The framework can achieve an optimal performance by balancing the tracking accuracy and real-time constraints.}, notes = {Also known as \cite{2463527} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Stathakis:2013:GECCO, author = {Apostolos Stathakis and Gr\'{e}goire Danoy and Julien Schleich and Pascal Bouvry and Gianluigi Morelli}, title = {Minimising longest path length in communication satellite payloads via metaheuristics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1365--1372}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463535}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The size and complexity of communication satellite payloads have been increasing very quickly over the last years and their configuration / reconfiguration have become very difficult problems. In this work, we propose to compare the efficiency of three well-known metaheuristic methods to solve an initial configuration problem, which objective is to minimise the length of the longest channel path. Experiments are conducted on real-world problem instances with realistic operational constraints (e.g., a maximum computation time of 10 minutes) and Wilcoxon test is used to determine with statistical confidence what technique is more suitable and what are its limitations. The results of this work will serve as an initial step in our research to design hybrid approaches to push even further the solving capabilities, i.e., tackling bigger payloads and more channels to activate.}, notes = {Also known as \cite{2463535} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Stolfi:2013:GECCO, author = {Daniel H. Stolfi and Enrique Alba}, title = {Red Swarm: smart mobility in cities with EAS}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1373--1380}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463540}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.}, notes = {Also known as \cite{2463540} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Tran:2013:GECCO, author = {Raymond Tran and Junhua Wu and Christopher Denison and Thomas Ackling and Markus Wagner and Frank Neumann}, title = {Fast and effective multi-objective optimisation of wind turbine placement}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1381--1388}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463541}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The single-objective yield optimisation of wind turbine placements on a given area of land is already a challenging optimisation problem. In this article, we tackle the multi-objective variant of this problem: we are taking into account the wake effects that are produced by the different turbines on the wind farm, while optimising the energy yield, the necessary area, and the cable length needed to connect all turbines. One key step contribution in order to make the optimisation computationally feasible is that we employ problem-specific variation operators. Furthermore, we use a recently presented caching-technique to speed-up the computation time needed to assess a given wind farm layout. The resulting approach allows the multi-objective optimisation of large real-world scenarios within a single night on a standard computer.}, notes = {Also known as \cite{2463541} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Ugolotti:2013:GECCO, author = {Roberto Ugolotti and Stefano Cagnoni}, title = {Differential evolution based human body pose estimation from point clouds}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1389--1396}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463528}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a method to estimate the body pose of a human from the point cloud obtained from a depth sensor. It uses Differential Evolution to find the best match between a candidate pose, represented by an instance of a 42-parameter articulated model of a human, and the point cloud. The results, compared to other four state-of-the art methods on a publicly available dataset, show that the method has good ability to estimate the pose of a person and to track him in video sequences. The entire method, from Differential Evolution to fitness computation, is run on nVIDIA GPUs. Thanks to its massively parallel implementation in CUDA-C, it produces pose estimates in real time.}, notes = {Also known as \cite{2463528} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{vanWilligen:2013:GECCO, author = {Willem van Willigen and Evert Haasdijk and Leon Kester}, title = {A multi-objective approach to evolving platooning strategies in intelligent transportation systems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1397--1404}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463534}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves high-level controllers for such intelligent vehicles. The algorithm yields a set of solutions that each embody their own prioritisation of various user requirements such as speed, comfort or fuel economy. This contrasts with the current practice in researching such controllers, where user preferences are summarised in a single number that the controller development process then optimises. Proof-of-concept experiments show that evolved controllers substantially outperform a widely used human behavioural model. We show that it is possible to evolve a set of vehicle controllers that correspond with different prioritisations of user preferences, giving the driver, on the road, the power to decide which preferences to emphasise.}, notes = {Also known as \cite{2463534} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{ZapotecasMartinez:2013:GECCO, author = {Sa\'{u}l Zapotecas Mart\'{\i}nez and Carlos A. Coello Coello}, title = {MOEA/D assisted by rbf networks for expensive multi-objective optimization problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1405--1412}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465805}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The development of multi-objective evolutionary algorithms assisted by surrogate models has increased in the last few years. However, in real-world applications, the high modality and dimensionality that functions have, often causes problems to such models. In fact, if the Pareto optimal set of a multi-objective optimisation problem is located in a search space in which the surrogate model is not able to shape the corresponding region, the search could be misinformed and thus converge to wrong regions. Because of this, a considerable amount of research has focused on improving the prediction of the surrogate models by adding the new solutions to the training set and retraining the model. However, when the size of the training set increases, the training complexity can significantly increase. In this paper, we present a surrogate model which maintains the size of the training set, and in which the prediction of the function is improved by using radial basis function networks in a cooperative way. Preliminary results indicate that our proposed approach can produce good quality results when it is restricted to performing only 200, 1,000 and 5,000 fitness function evaluations. Our proposed approach is validated using a set of standard test problems and an air-foil design problem.}, notes = {Also known as \cite{2465805} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Barros:2013:GECCO, author = {Rodrigo C. Barros and M\'{a}rcio P. Basgalupp and Ricardo Cerri and Tiago S. da Silva and Andr\'{e} C.P.L.F. de Carvalho}, title = {A grammatical evolution approach for software effort estimation}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1413--1420}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463546}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.}, notes = {Also known as \cite{2463546} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bergmann:2013:GECCO, author = {Karel P. Bergmann and J\"{o}rg Denzinger}, title = {Testing of precision agricultural networks for adversary-induced problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1421--1428}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463544}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present incremental adaptive corrective learning as a method to test ad-hoc wireless network protocols and applications. This learning method allows for the evolution of complex, variable-length, cooperative behaviour patterns for adversarial agents acting in such networks. We used the method to test precision agriculture sensor networks for vulnerabilities which could be exploited by attackers to significantly increase power consumption within the network. Our technique was able to find behaviours which increased power consumption by at least a factor of 3.6 for a node in each of the tested scenarios.}, notes = {Also known as \cite{2463544} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Bozkurt:2013:GECCO, author = {Mustafa Bozkurt}, title = {Cost-aware pareto optimal test suite minimisation for service-centric systems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1429--1436}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463551}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Runtime testing cost caused by service invocations is considered as one of the major limitations in Service-centric System Testing (ScST). Unfortunately, most of the existing work cannot achieve cost reduction at runtime as they perform offline testing. In this paper, we introduce a novel cost-aware Pareto optimal test suite minimisation approach for ScST aimed at reducing runtime testing cost. In experimental analysis, the proposed approach achieved reductions between 69percent and 98.6percent in monetary cost of service invocations while retaining test suite coverage. The results also provided evidence for the effectiveness of the selected algorithm HNSGA-II over the two commonly used algorithms: Greedy and NSGA-II.}, notes = {Also known as \cite{2463551} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Fraser:2013:GECCO, author = {Gordon Fraser and Andrea Arcuri and Phil McMinn}, title = {Test suite generation with memetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1437--1444}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463548}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Algorithms have been successfully applied to the generation of unit tests for classes, and are well suited to create complex objects through sequences of method calls. However, because the neighbourhood in the search space for method sequences is huge, even supposedly simple optimisations on primitive variables (e.g., numbers and strings) can be ineffective or unsuccessful. To overcome this problem, we extend the global search applied in the EVOSUITE test generation tool with local search on the individual statements of method sequences. In contrast to previous work on local search, we also consider complex data types including strings and arrays. A rigorous experimental methodology has been applied to properly evaluate these new local search operators. In our experiments on a set of open source classes of different kinds (e.g., numerical applications and text processing), the resulting test data generation technique increased branch coverage by up to 32percent on average over the normal Genetic Algorithm.}, notes = {Also known as \cite{2463548} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kempka:2013:GECCO, author = {Joseph Kempka and Phil McMinn and Dirk Sudholt}, title = {A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1445--1452}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463549}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Alternating Variable Method (AVM) has been shown to be a surprisingly effective and efficient means of generating branch-covering inputs for procedural programs. However, there has been little work that has sought to analyse the technique and further improve its performance. This paper proposes two new local searches that may be used in conjunction with the AVM, Geometric and Lattice Search. A theoretical runtime analysis shows that under certain conditions, the use of these searches is proved to outperform the original AVM. These theoretical results are confirmed by an empirical study with four programs, which shows that increases of speed of over 50percent are possible in practice.}, notes = {Also known as \cite{2463549} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Kessentini:2013:GECCO, author = {Marouane Kessentini and Wafa Werda and Philip Langer and Manuel Wimmer}, title = {Search-based model merging}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1453--1460}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463553}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In Model-Driven Engineering (MDE) adequate means for collaborative modelling among multiple team members is crucial for large projects. To this end, several approaches exist to identify the operations applied in parallel, to detect conflicts among them, as well as to construct a merged model by incorporating all non-conflicting operations. Conflicts often denote situations where the application of one operation disables the applicability of another operation. Whether one operation disables the other, however, often depends on their application order. To obtain a merged model that maximises the combined effect of all parallel operations, we propose an automated approach for finding the optimal merging sequence that maximises the number of successfully applied operations. Therefore, we adapted and used a heuristic search algorithm to explore the huge search space of all possible operation sequences. The validation results on merging various versions of real-world models confirm that our approach finds operation sequences that successfully incorporate a high number of conflicting operations, which are otherwise not reflected in the merge by current approaches.}, notes = {Also known as \cite{2463553} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Ouni:2013:GECCO, author = {Ali Ouni and Marouane Kessentini and Houari Sahraoui and Mohamed Salah Hamdi}, title = {The use of development history in software refactoring using a multi-objective evolutionary algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1461--1468}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463554}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the widely used techniques for evolving software systems is refactoring, a maintenance activity that improves design structure while preserving the external behaviour. Exploring past maintenance and development history can be an effective way of finding refactoring opportunities. Code elements which undergo changes in the past, at approximately the same time, bear a good probability for being semantically related. Moreover, these elements that experienced a huge number of refactoring in the past have a good chance for refactoring in the future. In addition, the development history can be used to propose new refactoring solutions in similar contexts. In this paper, we propose a multi-objective optimisation-based approach to find the best sequence of refactorings that minimises the number of bad-smells, and maximises the use of development history and semantic coherence. To this end, we use the non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between these three objectives. We report the results of our experiments using different large open source projects.}, notes = {Also known as \cite{2463554} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{:2013:GECCOc, author = {Paix\, {a}o, Matheus and Souza, Jerffeson}, title = {A scenario-based robust model for the next release problem}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1469--1476}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463547}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The next release problem is a significant task in the iterative and incremental software development model, involving the selection of a set of requirements to be included in the next software release. Given the dynamic environment in which modern software development occurs, the uncertainties related to the input variables considered in this problem should be taken into account. In this context, this paper proposes a novel formulation to the next release problem based on scenarios and considering the robust optimisation framework, which enables the production of robust solutions. In order to measure the 'price of robustness,' several experiments were designed and executed over artificial and real-world instances. All experimental results are consistent to show that the penalisation with regard to solution quality due to robustness is relatively small, which qualifies the proposed model to be applied even in large-scale real-world software projects.}, notes = {Also known as \cite{2463547} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Poulding:2013:GECCO, author = {Simon Poulding and Robert Alexander and John A. Clark and Mark J. Hadley}, title = {The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1477--1484}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463550}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The effectiveness of probabilistic structural testing depends on the characteristics of the probability distribution from which test inputs are sampled at random. Metaheuristic search has been shown to be a practical method of optimising the characteristics of such distributions. However, the applicability of the existing search-based algorithm is limited by the requirement that the software's inputs must be a fixed number of numeric values. In this paper we relax this limitation by means of a new representation for the probability distribution. The representation is based on stochastic context-free grammars but incorporates two novel extensions: conditional production weights and the aggregation of terminal symbols representing numeric values. We demonstrate that an algorithm which combines the new representation with hill-climbing search is able to efficiently derive probability distributions suitable for testing software with structurally-complex input domains.}, notes = {Also known as \cite{2463550} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Smith:2013:GECCO, author = {Jim Smith and Christopher L. Simons}, title = {A comparison of two memetic algorithms for software class modelling}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1485--1492}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463552}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent research has demonstrated that the problem of class modelling within early cycle object orientated software engineering can be successfully tackled by posing it as a search problem to be tackled with meta-heuristics. This 'Search Based Software Engineering' approach has been illustrated using both Evolutionary Algorithms and Ant Colony Optimisation to perform the underlying search. Each has been shown to display strengths and weaknesses, both in terms of how easily 'standard' algorithms can be applied to the domain, and of optimisation performance. This paper extends that work by considering the effect of incorporating Local Search. Specifically we examine the hypothesis that within a memetic framework the choice of global search heuristic does not significantly affect search performance, freeing the decision to be made on other more subjective factors. Results show that in fact the use of local search is not always beneficial to the Ant Colony Algorithm, whereas for the Evolutionary Algorithm with order based recombination it is highly effective at improving both the quality and speed of optimisation. Across a range of parameter settings ACO found its best solutions earlier than EAs, but those solutions were of lower quality than those found by EAs. For both algorithms we demonstrated that the number of constraints present, which relates to the number of classes created, has a far bigger impact on solution quality and time than the size of the problem in terms of numbers of attributes and methods.}, notes = {Also known as \cite{2463552} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Wang:2013:GECCOb, author = {Shuai Wang and Shaukat Ali and Arnaud Gotlieb}, title = {Minimizing test suites in software product lines using weight-based genetic algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1493--1500}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463545}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Test minimisation techniques aim at identifying and eliminating redundant test cases from test suites in order to reduce the total number of test cases to execute, thereby improving the efficiency of testing. In the context of software product line, we can save effort and cost in the selection and minimisation of test cases for testing a specific product by modelling the product line. However, minimising the test suite for a product requires addressing two potential issues: 1) the minimised test suite may not cover all test requirements compared with the original suite; 2) the minimised test suite may have less fault revealing capability than the original suite. In this paper, we apply weight-based Genetic Algorithms (GAs) to minimise the test suite for testing a product, while preserving fault detection capability and testing coverage of the original test suite. The challenge behind is to define an appropriate fitness function, which is able to preserve the coverage of complex testing criteria (e.g., Combinatorial Interaction Testing criterion). Based on the defined fitness function, we have empirically evaluated three different weight-based GAs on an industrial case study provided by Cisco Systems, Inc. Norway. We also presented our results of applying the three weight-based GAs on five existing case studies from the literature. Based on these case studies, we conclude that among the three weight-based GAs, Random-Weighted GA (RWGA) achieved significantly better performance than the other ones.}, notes = {Also known as \cite{2463545} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Aleti:2013:GECCO, author = {Aldeida Aleti and Irene Moser}, title = {Entropy-based adaptive range parameter control for evolutionary algorithms}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1501--1508}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463560}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms are equipped with a range of adjustable parameters, such as crossover and mutation rates which significantly influence the performance of the algorithm. Practitioners usually do not have the knowledge and time to investigate the ideal parameter values before the optimisation process. Furthermore, different parameter values may be optimal for different problems, and even problem instances. In this work, we present a parameter control method which adjusts parameter values during the optimisation process using the algorithm's performance as feedback. The approach is particularly effective with continuous parameter intervals, which are adapted dynamically. Successful parameter ranges are identified using an entropy-based clusterer, a method which outperforms state-of-the-art parameter control algorithms.}, notes = {Also known as \cite{2463560} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Candan:2013:GECCO, author = {Caner Candan and Adrien Go\"{e}ffon and Fr\'{e}d\'{e}ric Lardeux and Fr\'{e}d\'{e}ric Saubion}, title = {Non stationary operator selection with island models}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1509--1516}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463559}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The purpose of adaptive operator selection is to choose dynamically the most suitable variation operator of an evolutionary algorithm at each iteration of the search process. These variation operators are applied on individuals of a population which evolves, according to an evolutionary process, in order to find an optimal solution. Of course the efficiency of an operator may change during the search and therefore its application should be precisely controlled. In this paper, we use dynamic island models as operator selection mechanisms. A sub-population is associated to each operators and individuals are allowed to migrate from one sub-population to another one. In order to evaluate the performance of this adaptive selection mechanism, we propose an abstract operator representation using fitness improvement distributions that allow us to define non stationary operators with mutual interactions. Our purpose is to show that the adaptive selection is able to identify not only good operators but also suitable sequences of operators.}, notes = {Also known as \cite{2463559} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{DeRainville:2013:GECCO, author = {Fran\c{c}ois-Michel De Rainville and Mich\`{e}le Sebag and Christian Gagn\'{e} and Marc Schoenauer and Denis Laurendeau}, title = {Sustainable cooperative coevolution with a multi-armed bandit}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1517--1524}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463556}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.}, notes = {Also known as \cite{2463556} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lourenco:2013:GECCO, author = {Nuno Lourenco and Francisco Baptista Pereira and Ernesto Costa}, title = {The importance of the learning conditions in hyper-heuristics}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1525--1532}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463558}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms are problem solvers inspired by nature. The effectiveness of these methods on a specific task usually depends on a non trivial manual crafting of their main components and settings. Hyper-Heuristics is a recent area of research that aims to overcome this limitation by advocating the automation of the optimisation algorithm design task. In this paper, we describe a Grammatical Evolution framework to automatically design evolutionary algorithms to solve the knapsack problem. We focus our attention on the evaluation of solutions that are iteratively generated by the Hyper-Heuristic. When learning optimisation strategies, the hyper-method must evaluate promising candidates by executing them. However, running an evolutionary algorithm is an expensive task and the computational budget assigned to the evaluation of solutions must be limited. We present a detailed study that analyses the effect of the learning conditions on the optimisation strategies evolved by the Hyper-Heuristic framework. Results show that the computational budget allocation impacts the structure and quality of the learnt architectures. We also present experimental results showing that the best learnt strategies are competitive with state-of-the-art hand designed algorithms in unseen instances of the knapsack problem.}, notes = {Also known as \cite{2463558} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Luke:2013:GECCO, author = {Sean Luke and AKM Khaled Ahsan Talukder}, title = {Is the meta-EA a viable optimization method?}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1533--1540}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465806}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Meta-evolutionary algorithms have long been proposed as an approach to automatically discover good parameter settings to use in later optimisation runs. In this paper we instead ask whether a meta-evolutionary algorithm makes sense as an optimiser in its own right. That is, we're not interested in the resulting parameter settings, but only in the final result. As it so happens, this use of meta-EAs make sense in the context of large numbers of parallel runs, particularly in massive distributed scenarios. A primary issue facing meta-EAs is the stochastic nature of the meta-level fitness function. We consider whether this poses a challenge to establishing a gradient in the meta-level search space, and to what degree multiple tests are helpful in smoothing the noise. We discuss the nature of the meta-level search space and its impact on local optima, then examine the degree to which exploitation can be applied. We find that meta-EAs perform well as optimisers, and very surprisingly that they do best with only a single test. More exploitation appears to reduce performance, but only slightly.}, notes = {Also known as \cite{2465806} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Reehuis:2013:GECCO, author = {Edgar Reehuis and Markus Olhofer and Michael Emmerich and Bernhard Sendhoff and Thomas B\"{a}ck}, title = {Novelty and interestingness measures for design-space exploration}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1541--1548}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463557}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Measures of novelty and interestingness are frequently encountered in the context of developmental robotics, being derived from human psychology. This work addresses these measures from the viewpoint of enhancing design-space exploration in black-box optimisation. We provide a unifying notational and naming scheme with the intent of facilitating comparison, implementation, and application in the domain of design optimisation. Initial analysis shows a promising interestingness measure for being tried on real-world design problems.}, notes = {Also known as \cite{2463557} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Sim:2013:GECCO, author = {Kevin Sim and Emma Hart}, title = {Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1549--1556}, keywords = {genetic algorithms, genetic programming}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463555}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Novel deterministic heuristics are generated using Single Node Genetic Programming for application to the One Dimensional Bin Packing Problem. First a single deterministic heuristic was evolved that minimised the total number of bins used when applied to a set of 685 training instances. Following this, a set of heuristics were evolved using a form of cooperative co-evolution that collectively minimise the number of bins used across the same set of problems. Results on an unseen test set comprising a further 685 problem instances show that the single evolved heuristic outperforms existing deterministic heuristics described in the literature. The collection of heuristics evolved by cooperative co-evolution outperforms any of the single heuristics, including the newly generated ones.}, notes = {Also known as \cite{2463555} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Yuan:2013:GECCOa, author = {Zhi Yuan and Thomas St\"{u}tzle and Marco A. Montes de Oca and Hoong Chuin Lau and Mauro Birattari}, title = {An analysis of post-selection in automatic configuration}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1557--1564}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463562}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Automated algorithm configuration methods have proved to be instrumental in deriving high-performing algorithms and such methods are increasingly often used to configure evolutionary algorithms. One major challenge in devising automatic algorithm configuration techniques is to handle the inherent stochasticity in the configuration problems. This article analyses a post-selection mechanism that can also be used for this task. The central idea of the post-selection mechanism is to generate in a first phase a set of high-quality candidate algorithm configurations and then to select in a second phase from this candidate set the (statistically) best configuration. Our analysis of this mechanism indicates its high potential and suggests that it may be helpful to improve automatic algorithm configuration methods.}, notes = {Also known as \cite{2463562} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Zhang:2013:GECCO, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, title = {S-Race: a multi-objective racing algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1565--1572}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463561}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm's Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.}, notes = {Also known as \cite{2463561} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Barton:2013:GECCO, author = {Nick Barton and Paix\, {a}o, Tiago}, title = {Can quantitative and population genetics help us understand evolutionary computation?}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1573--1580}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463568}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Even though both population and quantitative genetics, and evolutionary computation, deal with the same questions, they have developed largely independently of each other. I review key results from each field, emphasising those that apply independently of the (usually unknown) relation between genotype and phenotype. The infinitesimal model provides a simple framework for predicting the response of complex traits to selection, which in biology has proved remarkably successful. This allows one to choose the schedule of population sizes and selection intensities that will maximise the response to selection, given that the total number of individuals realised, C = sum_t N_t, is constrained. This argument shows that for an additive trait (i.e., determined by the sum of effects of the genes), the optimum population size and the maximum possible response (i.e., the total change in trait mean) are both proportional to sqrt(C).}, notes = {Also known as \cite{2463568} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Doerr:2013:GECCOb, author = {Benjamin Doerr and Thomas Jansen and Carsten Witt and Christine Zarges}, title = {A method to derive fixed budget results from expected optimisation times}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1581--1588}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463565}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {At last year's GECCO a novel perspective for theoretical performance analysis of evolutionary algorithms and other randomised search heuristics was introduced that concentrates on the expected function value after a pre-defined number of steps, called budget. This is significantly different from the common perspective where the expected optimisation time is analysed. While there is a huge body of work and a large collection of tools for the analysis of the expected optimisation time the new fixed budget perspective introduces new analytical challenges. Here it is shown how results on the expected optimisation time that are strengthened by deviation bounds can be systematically turned into fixed budget results. We demonstrate our approach by considering the (1+1) EA on LeadingOnes and significantly improving previous results. We prove that deviating from the expected time by an additive term of omega(n^(3/2)) happens only with probability o(1). This is turned into tight bounds on the function value using the inverse function. We use three, increasingly strong or general approaches to proving the deviation bounds, namely via Chebyshev's inequality, via Chernoff bounds for geometric random variables, and via variable drift analysis.}, notes = {Also known as \cite{2463565} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Doerr:2013:GECCOc, author = {Benjamin Doerr and Marvin K\"{u}nnemann}, title = {How the (1+{\$\lambda\$}) evolutionary algorithm optimizes linear functions}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1589--1596}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463569}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We analyse how the (1+lambda) evolutionary algorithm (EA) optimises linear pseudo-Boolean functions. We prove that it finds the optimum of any linear function within an expected number of O(1/lambda n log n+n) iterations. We also show that this bound is sharp for some functions, e.g., the binary value function. Hence unlike for the(1+1) EA, for the (1+lambda) EA different linear functions may have run-times of different asymptotic order. The proof of our upper bound heavily relies on a number of classic and recent drift analysis methods. In particular, we show how to analyse a process displaying different types of drifts in different phases. Our work corrects a wrongfully claimed better asymptotic runtime in an earlier work~\cite{He10}.}, notes = {Also known as \cite{2463569} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Horn:2013:GECCO, author = {Jeffrey Horn}, title = {NP-completeness and the coevolution of exact set covers}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1597--1604}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2465807}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent success with a simple type of coevolution, resource defined fitness sharing (RFS), involving only pairwise interactions among species, has inspired some static analysis of the species interaction matrix. Under the assumption of equilibrium (w.r.t. selection), the matrix yields a set of linear equations. If there exists a subset of species that exactly cover the resources, then its characteristic population vector is a solution to the equilibrium equations. And if the matrix is non-singular, a solution to the equilibrium equations specifies an exact cover of the resources. This polynomial-time reduction of exact cover problems to linear equations is used in this paper to transform certain exact cover NP-complete problems to certain linear equation NP-complete problems: 0-1 Integer Programming, Minimum Weight Positive Solution to Linear Equations. While most of these problems are known to be in NP-complete, our new proof technique introduces a practical, polynomial-time heuristic algorithm for solving large instances of them.}, notes = {Also known as \cite{2465807} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Lissovoi:2013:GECCO, author = {Andrei Lissovoi and Carsten Witt}, title = {Runtime analysis of ant colony optimization on dynamic shortest path problems}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1605--1612}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463567}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A simple ACO algorithm called lambda-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\lambda$ ants per vertex helps in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic shortest-path problems which make the optimum difficult to track are discussed. Experiments illustrate theoretical findings and conjectures.}, notes = {Also known as \cite{2463567} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Nguyen:2013:GECCO, author = {Anh Quang Nguyen and Andrew M. Sutton and Frank Neumann}, title = {Population size matters: rigorous runtime results for maximizing the hypervolume indicator}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1613--1620}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463564}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Using the hypervolume indicator to guide the search of evolutionary multi-objective algorithms has become very popular in recent years. We contribute to the theoretical understanding of these algorithms by carrying out rigorous runtime analyses. We consider multi-objective variants of the problems OneMax and LeadingOnes called OMM and LOTZ, respectively, and investigate hypervolume-based algorithms with population sizes that do not allow coverage of the entire Pareto front. Our results show that LOTZ is easier to optimise than OMM for hypervolume-based evolutionary multi-objective algorithms which is contrary to the results on their single-objective variants and the well-studied (1+1)~EA.}, notes = {Also known as \cite{2463564} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Oliveto:2013:GECCOa, author = {Pietro S. Oliveto and Carsten Witt}, title = {Improved runtime analysis of the simple genetic algorithm}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1621--1628}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463566}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A runtime analysis of the Simple Genetic Algorithm (SGA) for the OneMax problem has recently been presented proving that the algorithm requires exponential time with overwhelming probability. This paper presents an improved analysis which overcomes some limitations of our previous one. Firstly, the new result holds for population sizes up to mu = n1/4-epsilon which is an improvement up to a power of 2 larger. Secondly, we present a technique to bound the diversity of the population that does not require a bound on its bandwidth. Apart from allowing a stronger result, we believe this is a major improvement towards the reusability of the techniques in future systematic analyses of GAs. Finally, we consider the more natural SGA using selection with replacement rather than without replacement although the results hold for both algorithmic versions. Experiments are presented to explore the limits of the new and previous mathematical techniques.}, notes = {Also known as \cite{2463566} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @inproceedings{Schmitt:2013:GECCO, author = {Manuel Schmitt and Rolf Wanka}, title = {Particle swarm optimization almost surely finds local optima}, booktitle = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = {2013}, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, isbn13 = {978-1-4503-1963-8}, pages = {1629--1636}, keywords = {}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2463372.2463563}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle swarm optimisation (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimisation problems. Although this technique is widely used, up to now only some partial aspects of the method have been formally investigated. In particular, while it is well-studied how to let the swarm converge to a single point in the search space, no general theoretical statements about this point or on the best position any particle has found have been known. For a very general class of objective functions, we provide for the first time results about the quality of the solution found. We show that a slightly adapted PSO almost surely finds a local optimum by investigating the newly defined potential of the swarm. The potential drops when the swarm approaches the point of convergence, but increases if the swarm remains close to a point that is not a local optimum, meaning that the swarm charges potential and continues its movement.}, notes = {Also known as \cite{2463563} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, } @proceedings(Blum:2013:GECCO, title = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference}, year = 2013, editor = {Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi}, address = {Amsterdam, The Netherlands}, publisher_address = {New York, NY, USA}, month = {6-10 July}, organisation = {SIGEVO}, keywords = {genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory}, ISBN13 = {978-1-4503-1963-8}, notes = {GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)}, )