%Extend editor list 6 Aug 2014 %urls added 8 Aug 2013 %processed by gecco2013_toc.awk $Revision: 1.39 $ ARGC=4 Thu Aug 01 20:28:11 BST 2013 %1 gecco2013comp_toc.txt %2 gecco2013comp.bib %3 gecco2013comp.bib %WBL 28 Jul 2020 remove {} in abstract for gecco_errors.txt from Paul Ortyl Jul 12, 2020 %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Biswas:2013:GECCOcomp, author = {Subhodip Biswas and Souvik Kundu and Swagatam Das and Athanasios Vasilakos}, title = {Information sharing in bee colony for detecting multiple niches in non-stationary environments}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1--2}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464588}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an information sharing model of artificial bee colony for locating multiple peaks in dynamic environments. The concept of niching is implemented by using a hybridised approach that combines a modified variant of the fitness sharing technique with a speciation based response to the changing environment. The informative dynamic niching bee colony algorithm helps to synchronise the employer and onlooker forager swarms by synergizing the local information with a modified perturbation strategy. This main crux of our algorithm is its independency of problem dependent control parameter, like niche radius, and the absence of any hard-partitioning clustering technique that leads to high computational burden. Our framework aims at bringing about a simple, robust approach that can be applied to a variety of problems. Experimental investigation is undertaken pertaining to the competitive performance of our algorithm with the existing techniques in order to highlight the significance of our work.}, notes = {Also known as \cite{2464588} Distributed at GECCO-2013.}, } @inproceedings{Lin:2013:GECCOcomp, author = {Ying Lin and Jun Zhang}, title = {Ant colony optimization with adaptive heuristics design}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {3--4}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464587}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Heuristics design, including definitions of heuristic information and parameter settings that control the impact of heuristic information, has significant influence on the performance of ant colony optimisation (ACO) algorithms. However, in complex real-world problems, it is difficult or even impossible to find one heuristics design that suits all problem instances. Besides, static heuristics design biases ACO to search certain areas of the solution space constantly, which makes ACO less explorative and increases the risk of prematurity. This paper proposes a heuristics design adaptation scheme (HDAS) for addressing the above problems in ACO. With HDAS, each ant defines a profile of heuristics design to guide its solution construction procedure. Such profiles are adaptively adjusted towards the most suitable heuristic design according to the search experience of ants. The ACO with HDAS (HDA-ACO) is validated on a set of benchmarks of flexible job-shop scheduling problems (FJSP). Experimental results show that the HDA-ACO outperforms the original ACO.}, notes = {Also known as \cite{2464587} Distributed at GECCO-2013.}, } @inproceedings{Ludwig:2013:GECCOcomp, author = {Simone A. Ludwig}, title = {Towards a repulsive and adaptive particle swarm optimization algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {5--6}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464584}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a Repulsive Adaptive PSO (RAPSO) variant that adaptively optimises the velocity weights of every particle at every iteration. RAPSO optimizes the velocity weights during every outer PSO iteration, and optimizes the solution of the problem in an inner PSO iteration. We compare RAPSO to Global Best PSO (GBPSO) on nine benchmark problems, and the results show that RAPSO out-performs GBPSO on difficult optimisation problems.}, notes = {Also known as \cite{2464584} Distributed at GECCO-2013.}, } @inproceedings{Magatrao:2013:GECCOcomp, author = {Dattatraya Magatrao and Shameek Ghosh and Jayaraman Valadi and Patrick Siarry}, title = {Simultaneous gene selection and cancer classification using a hybrid group search optimizer}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {7--8}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464579}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Constructing classifier models for gene expression datasets using informative features enhances prediction performance of concerned models. Here, we propose a hybrid Group Search based feature selection (GSO-FS) algorithm which can select relevant gene subsets that can optimally predict cancerous tissue samples. Our experimental results show that the GSO-FS algorithm in combination with SVM classifier performs quite well.}, notes = {Also known as \cite{2464579} Distributed at GECCO-2013.}, } @inproceedings{Malan:2013:GECCOcomp, author = {Katherine M. Malan and Andries P. Engelbrecht}, title = {Steep gradients as a predictor of PSO failure}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {9--10}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464582}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There are many features of optimisation problems that can influence the difficulty for search algorithms. This paper investigates the steepness of gradients in a fitness landscape as an additional feature that can be linked to difficulty for particle swarm optimisation (PSO) algorithms. The performances of different variations of PSO algorithms on a range of benchmark problems are considered against average estimations of gradients based on random walks. Results show that all variations of PSO failed to solve problems with estimated steep gradients in higher dimensions.}, notes = {Also known as \cite{2464582} Distributed at GECCO-2013.}, } @inproceedings{Fernandes:2013:GECCOcomp, author = {Carlos M. Fernandes and Juan Juli\'{a}n Merelo and Juan L.J. Laredo and Carlos Cotta and Agostinho C. Rosa}, title = {Partially connected topologies for particle swarm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {11--12}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464586}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The effects of dynamic and partially connected 2-dimensional topologies on the particle swarm are studied. The particles are positioned on 2-dimensional grids of nodes, where they move according to a simple rule. The von Neumann neighbourhood is used to decide which particles influence each individual. Structures with growing size are tested on a classical benchmark. The partially connected grids with von Neumann neighborhood structure perform more consistently than other strategies.}, notes = {Also known as \cite{2464586} Distributed at GECCO-2013.}, } @inproceedings{Nallaperuma:2013:GECCOcomp, author = {Samadhi Nallaperuma and Markus Wagner and Frank Neumann}, title = {Ant colony optimisation and the traveling salesperson problem: hardness, features and parameter settings}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {13--14}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464581}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Our study on ant colony optimisation (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.}, notes = {Also known as \cite{2464581} Distributed at GECCO-2013.}, } @inproceedings{Salama:2013:GECCOcomp, author = {Khalid M. Salama and Alex A. Freitas}, title = {Evaluating the use of different measure functions in the predictive quality of ABC-miner}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {15--16}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464580}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimisation (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naive-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 4 different classification measures on 15 benchmark datasets.}, notes = {Also known as \cite{2464580} Distributed at GECCO-2013.}, } @inproceedings{Schmitt:2013:GECCOcomp, author = {Manuel Schmitt and Rolf Wanka}, title = {Particles prefer walking along the axes: experimental insights into the behavior of a particle swarm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {17--18}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464583}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We study the frequently observed phenomenon of stagnation in the context of particle swarm optimisation (PSO). We show that in certain situations the particle swarm is likely to move almost parallel to one of the axes, which may cause stagnation. We provide an experimentally supported explanation in terms of a potential of the swarm and are therefore able to adapt the PSO algorithm slightly such that this weakness can be avoided.}, notes = {Also known as \cite{2464583} Distributed at GECCO-2013.}, } @inproceedings{Yin:2013:GECCOcomp, author = {Liang Yin and Xiao-Min Hu and Jun Zhang}, title = {Space-based initialization strategy for particle swarm optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {19--20}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464585}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Particle Swarm Optimisation (PSO) is a population-based stochastic optimisation algorithm that has been applied to various scientific and engineering problems. Despite its fast convergence speed, the original PSO is easy to fall into local optima when solving multi-modal functions. To address this problem, we present a novel initialisation strategy, namely Space-based Initialisation Strategy (SIS), to help PSO avoid local optima. We embed SIS into the standard PSO and form a novel PSO variant named SIS-PSO. The performance of SIS-PSO is validated by 13 benchmark functions and the experimental results demonstrate that the SIS enables PSO to achieve faster convergence speed and higher solution accuracy especially in multi-modal problems.}, notes = {Also known as \cite{2464585} Distributed at GECCO-2013.}, } @inproceedings{Clark:2013:GECCOcomp, author = {Anthony J. Clark and Philip K. McKinley}, title = {Evolutionary optimization of robotic fish control and morphology}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {21--22}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464593}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The nonlinear dynamics of an aquatic environment make robotic fish behaviour difficult to predict and subsequently difficult to optimise. In this paper, we present a method for optimising robotic fish propulsion through the evolution of control patterns and caudal fin flexibility. Evolved solutions are evaluated in a physics-based simulation environment. Control signals are generated with both simple sinusoids and neural oscillators. This study demonstrates how evolutionary algorithms can be used to handle the complex interactions among material properties, physical form, and control patterns in an aquatic environment.}, notes = {Also known as \cite{2464593} Distributed at GECCO-2013.}, } @inproceedings{Clune:2013:GECCOcomp, author = {Jeff Clune and Jean-Baptiste Mouret and Hod Lipson}, title = {Summary of "the evolutionary origins of modularity"}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {23--24}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464596}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A long-standing, open question in biology is how populations are capable of rapidly adapting to novel environments, a trait called evolvability. A major contributor to evolvability is the fact that many biological entities are modular, especially the many biological processes and structures that can be modelled as networks, such as metabolic pathways, gene regulation, protein interactions, and animal brains. Networks are modular if they contain highly connected clusters of nodes that are sparsely connected to nodes in other clusters [4, 2]. Despite its importance and decades of research, there is no agreement on why modularity evolves [4]. Intuitively, modular systems seem more adaptable, a lesson well-known to human engineers, because it is easier to rewire a modular network with functional subunits than an entangled, monolithic network [1]. However, because this evolvability only provides a selective advantage over the long-term, such selection is at best indirect and may not be strong enough to explain the level of modularity in the natural world [4]. Modularity is likely caused by multiple forces acting to various degrees in different contexts [4], and a comprehensive understanding of the evolutionary origins of modularity involves identifying those multiple forces and their relative contributions. The leading hypothesis is that modularity mainly emerges due to rapidly changing environments that have common subproblems, but different overall problems [1]. It is unknown how much natural modularity MVG can explain, however, because it unclear if biological environments change modularly, and whether they change at a high enough frequency for this force to play a significant role. We investigate an alternate hypothesis that has been suggested, but heretofore untested, which is that modularity evolves not because it conveys evolvability, but as a byproduct from selection to reduce connection costs in a network [3].}, notes = {Also known as \cite{2464596} Distributed at GECCO-2013.}, } @inproceedings{Clune:2013:GECCOcompa, author = {Jeff Clune and Dusan Misevic and Charles Ofria and Richard E. Lenski and Santiago Elena and Rafael Sanju\'{a}n}, title = {Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {25--26}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464597}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximises adaptation will be some intermediate value. This abstract summarises a previous publication in which we used Avida, a well-studied artificial life platform, to investigate the ability of natural selection to adjust and optimise mutation rates. Our initial experiments occurred in a previously studied environment with a complex fitness landscape (Lenski et al. Nature, 423, 2003) where Avidians were rewarded for performing any of nine logic tasks. We assessed the optimal mutation rate by empirically determining which unchanging mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve and we evaluated their proximity to the optimum. Although we chose conditions favourable for mutation rate optimisation (asexual organisms not yet adapted to a new environment), the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings (Fig. 1). We suggested that the reason mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified 'counting ones' landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates (Fig. 2, top row). In contrast, once moderate fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost (Fig. 2, bottom two rows). Additional experiments revealed that lowering the rate at which mutation rates evolved did not preclude the evolution of suboptimal mutation rates (see original manuscript). We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation because of the short-term costs of traversing fitness valleys. This finding has important implications for evolutionary research in both biological and computational realms.}, notes = {Also known as \cite{2464597} Distributed at GECCO-2013.}, } @inproceedings{Grappiolo:2013:GECCOcomp, author = {Corrado Grappiolo and Julian Togelius and Georgios N. Yannakakis}, title = {Using reinforcement learning and artificial evolution for the detection of group identities in complex adaptive artificial societies}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {27--28}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464589}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a computational framework capable of inferring the existence of groups, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial Societies (CAAS). Our modelling framework infers the group identities by following two steps: first, it aims to learn the on-going levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learnt cooperation values, to partition the agents into groups. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum Game, show that a cooperation learning phase, based on Reinforcement Learning, can provide highly promising results for minimising the mismatch between the existing and the inferred groups, for two different society sizes under investigation.}, notes = {Also known as \cite{2464589} Distributed at GECCO-2013.}, } @inproceedings{Jeanson:2013:GECCOcomp, author = {Francis Jeanson and Anthony White}, title = {Dynamic memory for robot control via delay neural networks}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {29--30}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464590}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a procedure to decode spatio-temporal spiking patterns in delay coincidence detection networks with stable limit cycles. We apply this to control a simulated e-puck robot to solve the t-maze memory task. This work shows that dynamic memory modules formed by coincidence detection neurones with transmission delays can be effectively coupled to produce adaptive behaviours.}, notes = {Also known as \cite{2464590} Distributed at GECCO-2013.}, } @inproceedings{Koos:2013:GECCOcomp, author = {Sylvain Koos and Antoine Cully and Jean-Baptiste Mouret}, title = {High resilience in robotics with a multi-objective evolutionary algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {31--32}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464591}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2464591} Distributed at GECCO-2013.}, } @inproceedings{Montanier:2013:GECCOcomp, author = {Jean-Marc Montanier and Nicolas Bredeche}, title = {Evolution of altruism: spatial dispersion and consumption strategies}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {33--34}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464594}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The work presented here is concerned with the evolution of altruistic behaviour in a population of agents subject to an open-ended evolutionary process. In this context, it is well known that genotypic relatedness plays a key role with respect to the level of altruism that can be observed. Such relatedness may be enforced through particular selection mechanism (e.g. kin-recognition) as well as particular dispersion strategies (e.g. low dispersion favours local interactions). This paper presents results on the importance of the evolution of particular dispersion strategies whenever consumption strategies are enforced. A key result from this paper is that whenever altruism is difficult to display when consuming food (i.e. being unable to share while eating), higher dispersion behaviour are evolved, which is a counter intuitive result at first sight.}, notes = {Also known as \cite{2464594} Distributed at GECCO-2013.}, } @inproceedings{Oliveira:2013:GECCOcomp, author = {Miguel Oliveira and Pedro Silva and Cristina Santos and Lino Costa}, title = {Sensitivity analysis of a crawl gait multi-objective optimization system}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {35--36}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464592}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes the analysis of a stable crawl gait multi-objective optimisation system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. In order to optimize the crawl gait, a multi-objective problem, an optimization system based on NSGA-II allows to find a set of non-dominated solutions that correspond to different motor solutions. The experimental results highlight the effectiveness of this multi-objective approach.}, notes = {Also known as \cite{2464592} Distributed at GECCO-2013.}, } @inproceedings{Trueba:2013:GECCOcomp, author = {Pedro Trueba and Abraham Prieto and Francisco Bellas}, title = {Distributed embodied evolution for collective tasks: parametric analysis of a canonical algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {37--38}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464595}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper deals with the formal parametric analysis of an evolutionary paradigm inspired in natural evolution, the distributed Embodied Evolution (EE). The main drawback of this approach is in the high parametric sensitivity it presents, being in addition highly task-dependent. With the aim of understanding the parameter relevance in different fitness landscapes, in this work we present a canonical version of a distributed EE algorithm, propose a set of intrinsic parameters that define its response and analyse them in eight collective problem landscapes.}, notes = {Also known as \cite{2464595} Distributed at GECCO-2013.}, } @inproceedings{Bautista:2013:GECCOcomp, author = {Eddy J. Bautista and Ranjan Srivastava}, title = {Leveraging ensemble information of evolving populations in genetic algorithms to identify incomplete metabolic pathways}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {39--40}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464601}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genome-scale metabolic models are powerful tools in helping to understand the metabolism of living organisms. They can be applied to the biomedical and biotechnological arenas. The use of such models enables fundamental understanding of metabolism and identification of drug targets in pathogenic microorganisms. They also facilitate metabolic engineering of recombinant organisms to make products useful to society. These mathematical models of metabolism are created based upon the genome annotation of the organism of interest. However, development of high quality versions of these models is non-trivial due to incomplete knowledge regarding gene function, as well as errors in genome annotations. Models developed under such circumstances display metabolic inconsistency and are mathematically infeasible. Genetic algorithms may be used to resolve these inconsistencies. In the process, it is possible to take advantage of the ensemble information inherent to the evolving population to gain additional biologically relevant insight. Specifically, it is possible to identify the most pathological metabolic inconsistencies in an organism, facilitating experimental design and hypothesis development.}, notes = {Also known as \cite{2464601} Distributed at GECCO-2013.}, } @inproceedings{Blazej:2013:GECCOcomp, author = {Pawe\l B\la\.{z}ej and Pawe\l Mackiewicz and Stanis\law Cebrat and Ma\lgorzata Wa\'{n}czyk}, title = {Using evolutionary algorithms in finding of optimized nucleotide substitution matrices}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {41--42}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464598}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2464598} Distributed at GECCO-2013.}, } @inproceedings{Ibragimov:2013:GECCOcomp, author = {Rashid Ibragimov and Jan Martens and Jiong Guo and Jan Baumbach}, title = {NABEECO: biological network alignment with bee colony optimization algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {43--44}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464600}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Motivation: A growing number of biological networks of ever increasing sizes are becoming available nowadays, making the ability to solve Network Alignment of primer importance. However, computationally the problem is hard for data sets of real-world sizes. Results: we developed NABEECO, a novel and robust Network Alignment heuristic based on Bee Colony Optimisation. We use the so-called Graph Edit Distance (GED) as optimization criterion, which is defined as the minimal amount of edge and node modifications necessary to transform one graph into another. We compare NABEECO on a set of protein-protein interaction networks to the current state of the art tool for biological networks, MI-GRAAL. Conclusion: We present the first Bee Colony Optimization algorithm for biological Network Alignment. NABEECO, in contrast to many other tools, can be applied to all kinds of networks and allows incorporating prior knowledge about node/edge similarity, though this is not required a priori. NABEECO together with a more detailed description and all data sets used are publicly available at http://nabeeco.mpi-inf.mpg.de.}, notes = {Also known as \cite{2464600} Distributed at GECCO-2013.}, } @inproceedings{Sobczynski:2013:GECCOcomp, author = {Maciej Sobczy\'{n}ski and Pawe\l Mackiewicz}, title = {Analysis of relationship between amino acid composition of proteins and environmental features of microorganisms using evolutionary algorithm and self-organizing maps}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {45--46}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464599}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2464599} Distributed at GECCO-2013.}, } @inproceedings{Ballinger:2013:GECCOcomp, author = {Christopher Ballinger and Sushil Louis}, title = {Comparing coevolution, genetic algorithms, and hill-climbers for finding real-time strategy game plans}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {47--48}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464604}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper evaluates a coevolutionary genetic algorithm's performance at generating competitive strategies in the initial stages of real-time strategy games. Specifically, we evaluate coevolution's performance against an exhaustive search of all possible build orders. Three hand coded strategies outside this exhaustive list provide a quantitative baseline for comparison with other strategy search algorithms. Earlier work had shown that a bit-setting hill-climber only finds the best strategies six percent of the time but takes significantly less time compared to a genetic algorithm that routinely finds the best strategies. Our results here show that coevolved strategies win or tie against hill-climber and genetic algorithm strategies eighty percent of the time but routinely lose to the three hand coded baselines. This work informs our research on improving coevolutionary approaches to real-time strategy game player design.}, notes = {Also known as \cite{2464604} Distributed at GECCO-2013.}, } @inproceedings{Bown:2013:GECCOcomp, author = {Oliver Bown and Rob Saunders}, title = {Multi-feature visualisations of phenotypic behaviour for creative interactive evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {49--50}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464602}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A visualisation method is presented for interactive evolution of interactive software objects, in which multiple outputs of the system are used to construct a two-dimensional shape in a feature space. The method allows multiple phenotypes to be overlaid allowing for quick feedback on the different properties of phenotypes. The properties of the resulting visualisations are discussed.}, notes = {Also known as \cite{2464602} Distributed at GECCO-2013.}, } @inproceedings{Tavares:2013:GECCOcomp, author = {Tiago F. Tavares and Alan Godoy}, title = {Sonification of population behavior in particle swarm optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {51--52}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464603}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {High-dimensional optimisation problems may be addressed using population based meta-heuristics, whose statistical properties may indicate important characteristics of the optimisation process. There is a great number of these properties, which means their joint visualisation may become impractical. We developed a method for sonically displaying characteristics of population dynamics in particle swarm optimization processes as sound scape parameters. This process allows jointly analysing several dimensions of the population's dynamics. Moreover, design decisions aimed at generating aesthetically appealing sound scapes, which allows the proposed system to be used as an automated music composition environment.}, notes = {Also known as \cite{2464603} Distributed at GECCO-2013.}, } @inproceedings{Gardner:2013:GECCOcomp, author = {Marc-Andr\'{e} Gardner and Christian Gagn\'{e} and Marc Parizeau}, title = {Estimation of distribution algorithm based on hidden Markov models for combinatorial optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {53--54}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464606}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a well-known graphical model useful for modelling populations of variable-length sequences of discrete values. Surprisingly, HMMs have not yet been used as distribution estimators for an EDA, even though it is a very powerful tool especially designed for modelling sequences. We thus propose a new method, called HMM-EDA, implementing this idea. Preliminary comparative results on two classical combinatorial optimisation problems show that HMM-EDA is indeed a promising approach for problems that have sequential representations.}, notes = {Also known as \cite{2464606} Distributed at GECCO-2013.}, } @inproceedings{Helmi:2013:GECCOcomp, author = {B. Hoda Helmi and Adel T. Rahamani and Martin Pelikan}, title = {Factor graph based factorization distribution algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {55--56}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464605}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose an optimisation algorithm in which a factor graph is used to encode the underlying distribution of the problem. The factor graph is learnt using symmetric non-negative matrix factorisation (SNMF) approach and bivariate statistics. Based on experimental and theoretical discussions, the proposed approach is capable of solving the optimisation problems in polynomial time with polynomial number of evaluations.}, notes = {Also known as \cite{2464605} Distributed at GECCO-2013.}, } @inproceedings{AlamAnik:2013:GECCOcomp, author = {Md. Tanvir Alam Anik and Saif Ahmed and Md. Monirul Islam}, title = {Fitness tracking based evolutionary programming: a novel approach for function optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {57--58}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464609}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to achieve a satisfactory optimisation performance by evolutionary programming (EP), it is necessary to ensure proper balance between exploration and exploitation. It is obvious that one single mutation operator is not the answer. Moreover, early loss of genetic diversity causes premature trapping around locally optimal points of the fitness landscape. This paper presents a fitness tracking based evolutionary programming (FTEP) algorithm incorporating a fitness tracking scheme to find the locally trapped individuals and treat them in a different way so that they are able to improve their performance. FTEP also incorporates several mutation operators in one algorithm and employs a self-adaptive strategy to gradually self-adapt the mutation operators in order to apply an appropriate mutation operator on the individual based on its need. A test-suite of 25 functions has been used to evaluate the performance of FTEP.}, notes = {Also known as \cite{2464609} Distributed at GECCO-2013.}, } @inproceedings{Li:2013:GECCOcomp, author = {Menglin Li and Colm O'Riordan}, title = {Analysis of generalised tit-for-tat strategies in evolutionary spatial n-player prisoner dilemmas}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {59--60}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464607}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work explores the evolution of a population of generalised tit-for-tat (TFT) strategies playing an N-player prisoner's dilemma on a regular lattice. We show that the generalised TFT can be robust to invasion by defectors in most cases. However, interestingly, the TFT strategies which are highly tolerant perform worse than totally cooperative strategies. Furthermore, although, the TFT strategies cannot guarantee the promotion of cooperation, the less tolerant TFT strategies obtain a stable and higher level of cooperation against defectors and populations containing both defectors and cooperators.}, notes = {Also known as \cite{2464607} Distributed at GECCO-2013.}, } @inproceedings{Sun:2013:GECCOcomp, author = {Yi Sun and Tom Schaul and Faustino Gomez and J\"{u}rgen Schmidhuber}, title = {A linear time natural evolution strategy for non-separable functions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {61--62}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464608}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES (R1-NES), which uses a low-rank approximation of the search distribution covariance matrix. The algorithm allows computation of the natural gradient with cost linear in the dimensionality of the parameter space, and excels in solving high-dimensional non-separable problems.}, notes = {Also known as \cite{2464608} Distributed at GECCO-2013.}, } @inproceedings{Amorim:2013:GECCOcomp, author = {Rainer Amorim and Bruno Dias and Rosiane de Freitas and Eduardo Uchoa}, title = {A hybrid genetic algorithm with local search approach for E/T scheduling problems on identical parallel machines}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {63--64}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464616}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work considers scheduling problems on single and parallel machines with arbitrary processing times and independent jobs, to minimise the sum of earliness-tardiness penalties. A Genetic Algorithm with a smart local search approach is presented, a 2-opt neighbourhood-based with GPI moves and tie-breaking criteria, in a single sequence representation for single and multi-machine instances. Computational experiments are performed on Tanaka's instances for single machine, achieving all optimal solutions obtained by an IP exact method, for 40, 50, and 100 jobs. Moreover, our method is also suitable for dealing with multi-machine instances, achieving good solutions in a reasonable execution time, for 40, 50, and 100 jobs, with 2, 4, and 10 machines.}, notes = {Also known as \cite{2464616} Distributed at GECCO-2013.}, } @inproceedings{Asta:2013:GECCOcomp, author = {Shahriar Asta and Ender \"{O}zcan and Andrew J. Parkes}, title = {Dimension reduction in the search for online bin packing policies}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {65--66}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464620}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In on-line bin-packing problems, a policy must be found for assigning items, according their size, immediately upon their arrival to bins with known initial capacities. In previous work of Ozcan and Parkes (GECCO 2011), a policy was represented as a 2-dimensional matrix (array) and good matrices were then evolved using a genetic algorithm (GA). Here, we consider a form of dimensional reduction in which variables in the matrix are grouped into elements taken from one-dimensional vectors. We find that with the right form of grouping, the GA then typically finds such vector policies significantly more quickly, and yet suffers little loss of overall quality.}, notes = {Also known as \cite{2464620} Distributed at GECCO-2013.}, } @inproceedings{Gheorghita:2013:GECCOcomp, author = {Marius Gheorghita and Irene Moser and Aldeida Aleti}, title = {Characterising fitness landscapes using predictive local search}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {67--68}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464618}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neighbourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.}, notes = {Also known as \cite{2464618} Distributed at GECCO-2013.}, } @inproceedings{Guzman:2013:GECCOcomp, author = {Mar\'{\i}a Alejandra Guzm\'{a}n and Gustavo Alfredo Bula}, title = {Bio-inspired and evolutionary algorithms applied to a bi-objective network design problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {69--70}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464615}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Logistics network design is one of the principal parts of strategic decisions in the planning and control of production systems. It deals with determining the warehouses locations and the definition of product flow between facilities and clients. This work is focused in finding an approximation of the Pareto-optimal front for two conflicting objective functions in logistic networks design: minimise costs and maximise coverage. Since the establishing of which warehouses must be opened constitute a combinatorial optimisation problem, two metaheuristic techniques, namely Improved Strength Pareto Evolutionary Algorithm - SPEA2 and a novel binary version of Bacterial Chemotaxis Multi-objective Optimisation Algorithm - BCMOA, were applied. With the aim of finding the optimal flow between clients and warehouses, network flow algorithms were also used. The performances of the above techniques were evaluated by comparative analysis of the results obtained in the solution of eight randomly generated problems by means of the dominated hypervolume metric (S-metric). The hybrid methodology here developed to solve the logistics network design problem - which combines metaheuristic techniques with a network flow algorithms - showed to be competitive regarding the Pareto Optimal Front approximation, and also displayed high efficiency in execution times.}, notes = {Also known as \cite{2464615} Distributed at GECCO-2013.}, } @inproceedings{Ma:2013:GECCOcomp, author = {Yun-yang Ma and Yue-jiao Gong and Wei-neng Chen and Jun Zhang}, title = {A set-based locally informed discrete particle swarm optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {71--72}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464614}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposed an efficient discrete PSO algorithm. Following the general process of the recently proposed locally informed particle swarm (LIPS), the velocity update of each particle in the proposed algorithm depends on the p-bests of its nearest neighbours. However, in order to achieve optimisation in discrete space, the related arithmetic operators and the concept of 'distance' in LIPS are redefined based on set theory. Thus, the proposed algorithm is termed Set-based LIPS (S-LIPS). Moreover, a reset scheme is embedded in S-LIPS to further improve population diversity in S-LIPS. By using the locally informed update mechanism and the reset scheme, the proposed algorithm is able to have both high convergence speed and good global search ability. S-LIPS is compared with a set-based comprehensive learning PSO on TSP benchmark instances. The experimental result shows that S-LIPS is a very promising algorithm for solving discrete problems, especially in the case where the scale of the problem is large.}, notes = {Also known as \cite{2464614} Distributed at GECCO-2013.}, } @inproceedings{Oliveira:2013:GECCOcompa, author = {Eunice Oliveira and Carlos Henggeler Antunes and \'{A}lvaro Gomes}, title = {A hybrid evolutionary simulated annealing algorithm with incorporation of preferences}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {73--74}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464619}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a hybrid evolutionary simulated annealing approach devoted to multi-objective models, in which preferences are elicited and exploited using the principles of the outranking-based ELECTRE TRI method. Preferences are used to guide the search process including deciding about the exploitation of new neighbour solutions. The overall approach has been used in a case study to provide decision support in identifying suitable load control actions in electrical networks.}, notes = {Also known as \cite{2464619} Distributed at GECCO-2013.}, } @inproceedings{Shil:2013:GECCOcomp, author = {Shubhashis Kumar Shil and Malek Mouhoub and Samira Sadaoui}, title = {An approach to solve winner determination in combinatorial reverse auctions using genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {75--76}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464611}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nowadays, winner determination problem is one of the main challenges in the domain of real-time applications such as combinatorial reverse auctions. To determine the winner(s) in combinatorial reverse auctions, in our previous work, we have proposed a Genetic Algorithm (GA)-based method and have demonstrated its superiority in terms of processing time and procurement cost. One of the main drawbacks of traditional GA-based solutions is their inconsistency in different runs. In this paper, we perform a statistical-based experiment that reveals that our proposed method is not affected by the inconsistency issue. In addition, we show two other features of our GA-based method: (1) the quality of the solution improves over generations, and (2) the any-time behaviour.}, notes = {Also known as \cite{2464611} Distributed at GECCO-2013.}, } @inproceedings{Silva:2013:GECCOcomp, author = {Andre Luis Silva and Jaime Arturo Ram\'{\i}rez and Felipe Campelo}, title = {A statistical study of discrete differential evolution approaches for the capacitated vehicle routing problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {77--78}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464613}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We examine the performance of four discrete differential evolution (DE) algorithms for the solution of capacitated vehicle routing problems (CVRPs). Twenty seven test instances are employed in the experimental analysis, with comparisons of final solution quality and time to convergence. The results indicate that two approaches presented significantly better results, but that all algorithms are still lacking in their ability to converge to the vicinity of the global optimum.}, notes = {Also known as \cite{2464613} Distributed at GECCO-2013.}, } @inproceedings{Silva:2013:GECCOcompa, author = {Ricardo Martins de Abreu Silva and Mauricio G.C. Resende and Panos M. Pardalos and Joao L. Faco}, title = {Biased random-key genetic algorithm for linearly-constrained global optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {79--80}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464617}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimisation problems subject to linear constraints. Experimental results illustrate its effectiveness on the g01 and g14 problems from CEC2006 benchmark [5].}, notes = {Also known as \cite{2464617} Distributed at GECCO-2013.}, } @inproceedings{Wang:2013:GECCOcomp, author = {Wenbo Wang and Xiaolin Chang and Jiqiang Liu and Bin Wang}, title = {Simulated annealing based resource allocation for cloud data centers}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {81--82}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464610}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Network virtualisation technology can be applied to provide the performance guarantee by creating a virtual network for running the user application. One of the most important issues in the network virtualization is the virtual network embedding (VNE) problem, which deals with the efficient allocation of physical resources in the cloud data centre to virtual networks. In this paper, we investigate the ability of simulated annealing technique in handling the VNE problem. The simulation results show that SA technique outperforms both genetic algorithm and particles swarm optimisation techniques in handling the cost-aware VNE problem.}, notes = {Also known as \cite{2464610} Distributed at GECCO-2013.}, } @inproceedings{Yuen:2013:GECCOcomp, author = {Shiu Yin Yuen and Xin Zhang}, title = {On composing an (evolutionary) algorithm portfolio}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {83--84}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464612}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a general methodology to automatically compose a good portfolio from a set of selected EAs. As a single EA is a degenerate portfolio, our method also provides an answer to when a portfolio of two or more EAs are beneficial. Our method has the nice property of being parameter-less; it does not introduce extra parameters. Hence there is no need for parameter control, which is well known to be a thorny research issue. To illustrate our idea, we show how a portfolio that is constructed by considering five state of the art EAs as candidates is automatically constructed from ten CEC 2005 benchmark functions. It is found that the resulting portfolio enjoys excellent, and equally importantly, stable ranking. Thus the new portfolio algorithm has the property of being a robust algorithm, which is a highly desirable property in practical applications.}, notes = {Also known as \cite{2464612} Distributed at GECCO-2013.}, } @inproceedings{Aguirre:2013:GECCOcomp, author = {Hernan Aguirre and Arnaud Liefooghe and Sebastien Verel and Kiyoshi Tanaka}, title = {Population size and scalability in the aeseh evolutionary many-objective algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {85--86}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466798}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work studies the effects of population size on performance scalability of the Adaptive ’¦Å-Sampling and ’¦Å-Hood evolutionary many-objective algorithm.}, notes = {Also known as \cite{2466798} Distributed at GECCO-2013.}, } @inproceedings{Chichakly:2013:GECCOcomp, author = {Karim J. Chichakly and Margaret J. Eppstein}, title = {Improving uniformity of solution spacing in biobjective evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {87--88}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464628}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We introduce a new synergistic combination of features, some of which have previously been used individually but not together, to improve uniformity of spacing in evolved non-dominated sets, especially in biobjective problems. On five standard biobjective benchmark tests, these features are shown to enhance performance in distinct and complementary ways.}, notes = {Also known as \cite{2464628} Distributed at GECCO-2013.}, } @inproceedings{Goulart:2013:GECCOcomp, author = {Fillipe Goulart and Lucas S. Batista and Felipe Campelo}, title = {Influence of relaxed dominance criteria in multiobjective evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {89--90}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464629}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work explores the influence of three different dominance criteria, namely the Pareto-, epsilon-, and cone epsilon-dominance, on the performance of multiobjective evolutionary algorithms. The approaches are incorporated into two different algorithms, which are then applied to the solution of twelve benchmark problems from the ZDT and DTLZ families. The final results of the algorithms are compared in terms of cardinality, convergence, and diversity of solutions using a statistical methodology designed to indicate whether any of the criteria provides significantly better results over the whole test set. The results obtained suggest that the cone epsilon-approach is an interesting alternative for finding well-distributed fronts without the loss of efficient solutions usually presented by the epsilon-dominance.}, notes = {Also known as \cite{2464629} Distributed at GECCO-2013.}, } @inproceedings{Kirkland:2013:GECCOcomp, author = {Oliver Kirkland and Beatriz de la Iglesia}, title = {MOEA for clustering: comparison of mutation operators}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {91--92}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464623}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Clustering is an important task in data mining. However, there are numerous conflicting measurements of what a good clustering solution is. Therefore, clustering is a task that is suitable for a Multi-Objective Evolutionary Algorithm. Mutation operators for these algorithms can be designed to explore a diverse range of solutions or focus upon individual solution quality. We propose using a hybrid technique that generates a wide range of solutions and then improves them with respect to the data. We create an experimental set-up to assess mutation operators with respect to Pareto front quality. Using this set-up we find that mutation operators that mutate solutions with respect to the data perform better but hybrid mutation techniques show promise.}, notes = {Also known as \cite{2464623} Distributed at GECCO-2013.}, } @inproceedings{Moalic:2013:GECCOcomp, author = {Laurent Moalic and Sid Lamrous and Alexandre Caminada}, title = {A memetic algorithm for multiobjective problems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {93--94}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464627}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this article, we present a method combining a genetic approach with a local search for multiobjective problems. The performance of the proposed algorithm is illustrated by experimental results based on a real problem with three objectives. The problem is issued from electric car-sharing service with a car manufacturer partner. Compared to the Multiobjective Pareto Local Search (PLS) well known in the scientific literature, the proposed model aims to improve: the solutions quality and the set diversity.}, notes = {Also known as \cite{2464627} Distributed at GECCO-2013.}, } @inproceedings{Narukawa:2013:GECCOcomp, author = {Kaname Narukawa}, title = {Finding a diverse set of decision variables in evolutionary many-objective optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {95--96}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464621}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we modify an evolutionary many-objective optimisation algorithm so that it can find a diverse set of solutions in the decision variable space. The modification is based on considering the Euclidean distance in the decision variable space. The effect of our modification is examined by using benchmark test problems. From computational experiments, we can say that a diverse set of solutions in the decision variable space is searched by the modification.}, notes = {Also known as \cite{2464621} Distributed at GECCO-2013.}, } @inproceedings{Pilat:2013:GECCOcomp, author = {Martin Pilat and Roman Neruda}, title = {Multiobjectivization for classifier parameter tuning}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {97--98}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464626}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a multiobjective approach to the parameter tuning of RBF networks and multilayer perceptrons. The approach works by adding two new objectives -- maximisation of kappa statistic and minimisation of root mean square error -- to the originally single-objective problem of minimizing the classification error of the model. We show the performance of the multiobjectivization approach on five datasets.}, notes = {Also known as \cite{2464626} Distributed at GECCO-2013.}, } @inproceedings{Suciu:2013:GECCOcomp, author = {Mihai Suciu and Marcel Cremene and Dumitru Dumitrescu}, title = {Exploring some scalarization techniques for EMOAs}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {99--100}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464625}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When solving a multi-objective problem Pareto based evolutionary algorithms are the preferred choice. They are able to find a good approximation of the Pareto front and assure good diversity. But Pareto dominance scales badly with the number of objectives. Decomposition based algorithms represent a good choice for many-objective problems, their performance is not affected in such a severe way because they solve multiple one-objective problems. The preferred methods for scalarizing all objectives into one single objective are weighted sum and weighted Tchebycheff. With some modifications to the Tchebycheff approach some drawbacks, such as obtaining weak Pareto optimal solutions, can be avoided. We study the augmented, modified Tchebycheff and Lp decomposition techniques as an alternative. Numerical results on test problems indicate an in crease in performance over weighted sum and weighted Tchebycheff when applied to many-objective optimisation problems.}, notes = {Also known as \cite{2464625} Distributed at GECCO-2013.}, } @inproceedings{Wang:2013:GECCOcompa, author = {Rui Wang and Robin C. Purshouse and Peter J. Fleming}, title = {Preference-inspired co-evolutionary algorithm using weights for many-objective optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {101--102}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464622}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Decomposition based approaches are known to perform well on many-objective problems when a suitable set of weights is provided. However, providing a suitable set of weights a priori is difficult. This study proposes a novel algorithm: preference-inspired co-evolutionary algorithm using weights (PICEA-w), which co-evolves a set of weights with the usual population of candidate solutions during the search process. The co-evolution enables suitable sets of weights to be constructed along the optimisation process, thus guiding the candidate solutions toward the Pareto optimal front. Experimental results show PICEA-w performs better than algorithms embedded with random or uniform weights.}, notes = {Also known as \cite{2464622} Distributed at GECCO-2013.}, } @inproceedings{Younas:2013:GECCOcomp, author = {Irfan Younas and Farzad Kamrani and Rassul Ayani}, title = {Optimization of assignment of tasks to teams using multi-objective metaheuristics}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {103--104}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464624}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A highly interesting but not thoroughly addressed optimisation problem is a variation of the Assignment Problem (AP) where tasks are assigned to groups of collaborating agents (teams). In this paper, we address this class of AP as a bi-objective optimisation problem, in which the cost is minimised and the quality is maximised. To solve the model, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). We conduct several experiments on problems with varying sizes to compare the NSGA-II and SPEA2 algorithms.}, notes = {Also known as \cite{2464624} Distributed at GECCO-2013.}, } @inproceedings{Beal:2013:GECCOcomp, author = {Jacob Beal and Aaron Adler and Hala Mostafa}, title = {Mixed geometric-topological representation for electromechanical design}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {105--106}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464632}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Avoiding unintended representational commitments is a key challenge in generative design. We have developed a mixed geometric-topological representation based on CW-complexes, which represents structure and geometric constraints such that commitments regarding position and layout are late-binding and resolve only during the evaluation of a design instance. Complicated designs can be elaborated into a full representation using a small number of biologically-inspired developmental operators. We illustrate the new representation with a number of examples of electromechanical design.}, notes = {Also known as \cite{2464632} Distributed at GECCO-2013.}, } @inproceedings{Fontana:2013:GECCOcomp, author = {Alessandro Fontana and Borys Wrobel}, title = {Morphogen-based self-generation of evolving artificial multicellular structures with millions of cells}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {107--108}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464631}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Epigenetic Tracking is a model of Artificial Embryology whose central feature is separation of cells into normal cells and driver cells. Drivers are much fewer in number than normal cells and orchestrate developmental events. This separation allows for generation of structures with much more complexity and much higher number of cells (in the order of millions) than in other Artificial Embryology models. We introduce in this paper a new mechanism for the generation of driver cells, based on diffusing morphogens. We show that this change preserves the evolvability of very large complex structures, provided that the density of the drivers is sufficiently high. We than draw the outline of the future work that will build on this mechanism towards evolution of structures robust to damage and developmental noise in our system.}, notes = {Also known as \cite{2464631} Distributed at GECCO-2013.}, } @inproceedings{Morgan:2013:GECCOcomp, author = {Alyssa Morgan and Daniel Coore}, title = {Extending the growing point language to self-organise patterns in three dimensions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {109--110}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466799}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Growing Point Language (GPL) is used to engineer the emergent behaviour of an amorphous computer. GPL patterns are topologically one-dimensional objects, regardless of the dimension of the space in which the system exists. A crude length measure in GPL means that GPL patterns also have a geometric character to them. One of the constructs defined in GPL (diatropisim), directs a growing point to propagate tangentially to the level curve of a spatial distribution called a pheromone. In 2-dimensions, tangent spaces are 1-dimensional and therefore diatropism is reasonably well defined. However, in 3-dimensions (and higher) diatropism is no longer confined to 1-dimension, which means that some programs whose behaviour was well understood in 2-dimensional systems, become less so in higher dimensions. We argue that the predictability of the geometric properties of a GPL program in 3-dimensions can be completely recovered. We support this argument with the presentation of a program that given a centre point, a direction, and a radius will generate a circular path in the plane containing the centre, that is normal to the given direction. We provide quantitative data from a single run to illustrate how well the geometric objectives can be achieved.}, notes = {Also known as \cite{2466799} Distributed at GECCO-2013.}, } @inproceedings{Townsend:2013:GECCOcomp, author = {Joe Townsend and Ed Keedwell and Antony Galton}, title = {Artificial development of connections in SHRUTI networks using a multi objective genetic algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {111--112}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464630}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {SHRUTI is a model of how first-order logic can be represented and reasoned upon using a network of spiking neurons in an attempt to model the brain's ability to perform reasoning. This paper extends the biological plausibility of the SHRUTI model by presenting a genotype representation of connections in a SHRUTI network using indirect encoding and showing that networks represented in this way can be generated by an evolutionary process.}, notes = {Also known as \cite{2464630} Distributed at GECCO-2013.}, } @inproceedings{Asafuddoula:2013:GECCOcomp, author = {Md Asafuddoula and Tapabrata Ray and Ruhul Sarker}, title = {An efficient constraint handling approach for optimization problems with limited feasibility and computationally expensive constraint evaluations}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {113--114}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464633}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Existing optimisation approaches adopt a full evaluation policy, i.e. all the constraints corresponding to a solution are evaluated throughout the course of search. Furthermore, a common sequence of constraint evaluation is used for all the solutions. In this paper, we introduce a scheme of constraint handling, wherein every solution is assigned a random sequence of constraints and the evaluation process is aborted whenever a constraint is violated. The solutions are sorted based on two measures i.e. the number of satisfied constraints and the violation measure. The number of satisfied constraints takes a precedence over the amount of violation. We illustrate the performance of the proposed scheme and compare it with other state-of-the-art constraint handling methods within a framework of differential evolution. The results are compared using gseries test functions for inequality constraints. The results clearly highlight the potential savings offered by the proposed method}, notes = {Also known as \cite{2464633} Distributed at GECCO-2013.}, } @inproceedings{Craven:2013:GECCOcomp, author = {Matthew J. Craven and Henri C. Jimbo}, title = {An EA for portfolio selection over multiple investment periods with exponential transaction costs}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {115--116}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464639}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We study a matrix representation for an EA attack on the CCPOP with transaction costs. The representation is based on portfolio sequences which change over the investment lifetime in response to asset price changes. We show the approach is effective and that EA performance is directly related to asset price correlation. We compare the EA with a matrix hill climber and show some common results of vector representations do not hold for a matrix one, potentially providing a step forward in performance of such algorithms.}, notes = {Also known as \cite{2464639} Distributed at GECCO-2013.}, } @inproceedings{Darabos:2013:GECCOcomp, author = {Christian Darabos and Craig O. Mackenzie and Marco Tomassini and Mario Giacobini and Jason H. Moore}, title = {Coevolution of rules and topology in cellular automata}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {117--118}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464638}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Coevolution is nature's response to highly complex and rapidly changing conditions. Biological systems are able to have multiple traits evolving concurrently to adapt to their environment. For many years, evolutionary algorithms have been successfully used on cellular automata (CA) to produce good update functions. The resulting CAs are, however, much slower and more sensitive to perturbations than CAs with an evolved topology and fixed uniform update rule. Unfortunately, these are not nearly as performant, and suffer from scaling up the number of cells. We propose a hybrid paradigm that simultaneously coevolves the supporting network and the update functions of CAs. The resulting systems combine the high performance of the update evolution and the robustness properties and speed of the topology evolution CAs. Coevolution in CAs a viable trade off between the two single trait evolutions.}, notes = {Also known as \cite{2464638} Distributed at GECCO-2013.}, } @inproceedings{Doerr:2013:GECCOcomp, author = {Benjamin Doerr and Paul Fischer and Astrid Hilbert and Carsten Witt}, title = {Evolutionary algorithms for the detection of structural breaks in time series: extended abstract}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {119--120}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464635}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behaviour of the time series changes. Typically, no solid background knowledge of the time series under consideration is available. Therefore, a black-box optimisation approach is our method of choice for detecting structural breaks. We describe a evolutionary algorithm framework which easily adapts to a large number of statistical settings. The experiments on artificial and real-world time series show that the algorithm detects break points with high precision and is computationally very efficient. A reference implementation is available at the following address: http://www2.imm.dtu.dk/~pafi/SBX/launch.html}}, notes = {Also known as \cite{2464635} Distributed at GECCO-2013.}, } @inproceedings{Huang:2013:GECCOcomp, author = {Xiao-ma Huang and Yue-jiao Gong and Jun Zhang}, title = {A novel pheromone-based evolutionary algorithm for solving degree-constrained minimum spanning tree problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {121--122}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464636}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The degree-constrained minimum spanning tree problem (dc-MSTP) is crucial in the design of networks and it is proved to be NP-hard. The recently developed evolutionary algorithm using node-depth-degree representation (EANDD) has successfully enabled the dc-MSTP solvable by generating new spanning trees in average time complexity , which is the fastest in the literature. However, as the generic operation of EANDD is to change two edges that are randomly selected from the entire tree, the efficiency of EANDD still has potential to be further improved. In this paper, we propose a novel pheromone-based tree modification method (PTMM) to improve the efficiency of EANDD. For each edge, a pheromone value is defined based on the historical contribution of the edge to the fitness of the spanning tree. Then, PTMM considers the pheromone value on each edge as a desirability measure for selecting the edge to construct the spanning tree. In this way, the more promising edge is more likely to be selected and therefore the efficiency of the tree modification operation in EANDD can be improved. The effectiveness and efficiency of PTMM is demonstrated on a set of benchmark instances in comparison with the original EANDD.}, notes = {Also known as \cite{2464636} Distributed at GECCO-2013.}, } @inproceedings{Li:2013:GECCOcompa, author = {Yuan-Long Li and Zhi-Hui Zhan and Jun Zhang}, title = {Differential evolution enhanced with evolution path vector}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {123--124}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464637}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to combine the advantages of distributed model (DM) and centralised model (CM) offspring generation models, this paper proposes to use the differential evolution (DE) algorithm as the base population reproduction method and enhance its DM scheme with one of the key CM features, which is the covariance matrix adaptation (CMA) used in CMA-ES. In this way, an enhanced DE population reproduction scheme with evolution path (DE/EP) is developed. The proposed DE/EP scheme is kept almost as simple as the original DE but works better due to the advantages of the CMA feature.}, notes = {Also known as \cite{2464637} Distributed at GECCO-2013.}, } @inproceedings{Nazmul:2013:GECCOcomp, author = {Rumana Nazmul and Madhu Chetty}, title = {A priority based parental selection method for genetic algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {125--126}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464641}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Selection is an important and critical aspect in evolutionary computation. This paper presents a novel parental selection technique that includes the advantages of both the deterministic and the stochastic selection techniques and helps to reduce the loss of diversity by distributing the reproduction opportunity among all the members of the population. Moreover, the proposed selection strategy promotes the concept of non-random mating by clustering the population into groups according to the fitness values and then by persuading the mating between individuals from different groups based on performance determined dynamically over the evolution. Computational results using widely used benchmark functions show significant improvements in the convergence characteristics of the proposed selection method over two well-known selection techniques.}, notes = {Also known as \cite{2464641} Distributed at GECCO-2013.}, } @inproceedings{Oliwa:2013:GECCOcomp, author = {Tomasz Oliwa and Khaled Rasheed}, title = {An overlapping variable linkage benchmark suite}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {127--128}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464642}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a novel benchmarking suite for global real-valued evolutionary optimisation is presented. It combines some merits of the Real-Parameter Black-Box Optimization Benchmarking (BBOP), the Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimisation (LSGO) and one of its extensions benchmarks in regards to fitness functions consisting of overlapping sub-problems of varying size with a varying degree of overlapping, a previously not fully investigated scenario.}, notes = {Also known as \cite{2464642} Distributed at GECCO-2013.}, } @inproceedings{Skaruz:2013:GECCOcomp, author = {Jaroslaw Skaruz and Artur Niewiadomski and Wojciech Penczek}, title = {Automated abstract planning with use of genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {129--130}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464640}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper presents a new approach based on nature inspired algorithms to an abstract planning problem, which is a part of the web service composition problem. An abstract plan is defined as an equivalence class of sequences of the same service types that satisfy a user query. The objective of our genetic algorithm (GA) is to return representatives of abstract plans without generating all the equivalent sequences.}, notes = {Also known as \cite{2464640} Distributed at GECCO-2013.}, } @inproceedings{Xu:2013:GECCOcomp, author = {Zhuoran Xu and Mikko Poloj\"{a}rvi and Masahito Yamamoto and Masashi Furukawa}, title = {An attraction basin estimating genetic algorithm for multimodal optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {131--132}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464634}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Radii-based niching evolutionary algorithms are criticised for the difficulty of the proper choice of the radius parameter. Detect-multimodal method enables the identification of niches without an explicit user-defined radius parameter. Although robust, the detect-multimodal method based algorithms are computationally expensive. We propose a novel algorithm called Attraction Basin Estimating Genetic Algorithm (ABE), which estimates the radius parameter based on the detect-multimodal method and uses the estimated radius to identify niches. Our experiments demonstrate that ABE has a similar ability to solve the multimodal optimisation problem as Topological Species Conservation algorithm which is based on the detect-multimodal method, but much more efficiently.}, notes = {Also known as \cite{2464634} Distributed at GECCO-2013.}, } @inproceedings{AL-Madi:2013:GECCOcomp, author = {Nailah AL-Madi and Simone A. Ludwig}, title = {Segment-based genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {133--134}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464648}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP.}, notes = {Also known as \cite{2464648} Distributed at GECCO-2013.}, } @inproceedings{Bhowan:2013:GECCOcomp, author = {Urvesh Bhowan and Mark Johnston and Mengjie Zhang}, title = {Comparing ensemble learning approaches in genetic programming for classification with unbalanced data}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {135--136}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464643}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble.}, notes = {Also known as \cite{2464643} Distributed at GECCO-2013.}, } @inproceedings{Castelli:2013:GECCOcomp, author = {Mauro Castelli and Davide Castaldi and Leonardo Vanneschi and Ilaria Giordani and Francesco Archetti and Daniele Maccagnola}, title = {An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {137--138}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464644}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behaviour of a program once it is executed on a set of inputs, or more particularly the set of its output values on input training data (this definition has been used, among many others, for instance in [?, ?, ?, ?]). In particular, new genetic operators, called geometric semantic operators, have been proposed by Moraglio et al. [?]. They are defined s follows:}, notes = {Also known as \cite{2464644} Distributed at GECCO-2013.}, } @inproceedings{Colmenar:2013:GECCOcomp, author = {J. Manuel Colmenar and Alfredo Cuesta-Infante and Jos\'{e} L. Risco-Mart\'{\i}n and J. Ignacio Hidalgo}, title = {An evolutionary methodology for automatic design of finite state machines}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {139--140}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464645}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose an evolutionary flow for finite state machine inference through the cooperation of grammatical evolution and a genetic algorithm. This coevolution has two main advantages. First, a high-level description of the target problem is accepted by the flow, being easier and affordable for system designers. Second, the designer does not need to define a training set of input values because it is automatically generated by the genetic algorithm at run time. Our experiments on the sequence recogniser and the vending machine problems obtained the FSM solution in 99.96percent and 100percent of the optimisation runs, respectively.}, notes = {Also known as \cite{2464645} Distributed at GECCO-2013.}, } @inproceedings{Fitzgerald:2013:GECCOcomp, author = {Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan}, title = {Bootstrapping to reduce bloat and improve generalisation in genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {141--142}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464647}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Typically, the quality of a solution in Genetic Programming (GP) is represented by a score on a given training sample. However, in Machine Learning, we are most interested in estimating the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data to direct training without actually using additional data, by employing a technique called bootstrapping that repeatedly re-samples with replacement from the training data and helps estimate sensitivity of the individual in question to small variations across these re-sampled data sets. We minimise this sensitivity, as measured by the Bootstrap Standard Error, alongside the training error, in a bid to evolve models that generalise better to the unseen data. We evaluate the proposed technique on four binary classification problems and compare with a standard GP approach. The results show that for the problems undertaken, the proposed method not only generalises significantly better than standard GP while the training performance improves, but also demonstrates a strong side effect of containing the tree sizes.}, notes = {Also known as \cite{2464647} Distributed at GECCO-2013.}, } @inproceedings{Icke:2013:GECCOcomp, author = {Ilknur Icke and Joshua C. Bongard}, title = {Automatic identification of hierarchy in multivariate data}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {143--144}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464650}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Given n variables to model, symbolic regression (SR) returns a flat list of n equations. As the number of state variables to be modelled scales, it becomes increasingly difficult to interpret such a list. Here we present a symbolic regression method that detects and captures hidden hierarchy in a given system. The method returns the equations in a hierarchical dependency graph, which increases the interpretability of the results. We demonstrate two variations of this hierarchical modelling approach, and show that both consistently outperform non-hierarchical symbolic regression on a number of synthetic data sets.}, notes = {Also known as \cite{2464650} Distributed at GECCO-2013.}, } @inproceedings{Koshiyama:2013:GECCOcomp, author = {Adriano Soares Koshiyama and Douglas Mota Dias and Andr\'{e} Vargas Abs da Cruz and Marco Aur\'{e}lio Cavalcanti Pacheco}, title = {Numerical optimization by multi-gene genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {145--146}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464651}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new method for numerical optimization problems based on Multi-Gene Genetic Programming. We discuss theoretical aspects, operators, representation, and experimental results.}, notes = {Also known as \cite{2464651} Distributed at GECCO-2013.}, } @inproceedings{Miller:2013:GECCOcomp, author = {Julian F. Miller and Maktuba Mohid}, title = {Function optimization using cartesian genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {147--148}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464646}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In function optimisation one tries to find a vector of real numbers that optimises a complex multi-modal fitness function. Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not. In this paper, we show how Cartesian Genetic Programming can be readily applied to such problems. The technique can successfully find many optima in a standard suite of benchmark functions. The work opens up new avenues of research in the application of genetic programming and also offers an extensive set of highly developed benchmarks that could be used to compare the effectiveness of different GP methodologies.}, notes = {Also known as \cite{2464646} Distributed at GECCO-2013.}, } @inproceedings{Nguyen:2013:GECCOcomp, author = {Quang Uy Nguyen and Cong Doan Truong and Xuan Hoai Nguyen and Michael O'Neill}, title = {Guiding function set selection in genetic programming based on fitness landscape analysis}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {149--150}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466800}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper attempts to provide a guideline for function set selection based on fitness landscape analysis. We used two well-known techniques, autocorrelation function and information content, to analyse the fitness landscape of each function set. We tested these methods on a large number of real-valued symbolic regression problems and the experimental results showed that there is a strong relationship between autocorrelation function value and the performance of a function set. Therefore, autocorrelation function can be used as a good indicator for selecting an appropriate function set for a problem.}, notes = {Also known as \cite{2466800} Distributed at GECCO-2013.}, } @inproceedings{Tuite:2013:GECCOcomp, author = {Cliodhna Tuite and Michael O'Neill and Anthony Brabazon}, title = {Towards a dynamic benchmark for genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {151--152}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464649}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Following a recent call for a suite of benchmarks for genetic programming, we investigate the criteria for a meaningful dynamic benchmark for GP. We explore the design of a dynamic benchmark for symbolic regression, based on semantic distance between evaluated functions, where larger distances serve as a proxy for greater environmental change. We do not find convincing evidence that lower semantic distance is a good proxy for greater ease in adapting to a change. We conclude that due to fundamental characteristics of GP, it is difficult to come up with a single dynamic benchmark problem which is generally applicable.}, notes = {Also known as \cite{2464649} Distributed at GECCO-2013.}, } @inproceedings{Hemberg:2013:GECCOcomp, author = {Erik Hemberg and Kalyan Veeramachaneni and Franck Dernoncourt and Mark Wagy and Una-May O'Reilly}, title = {Imprecise selection and fitness approximation in a large-scale evolutionary rule based system for blood pressure prediction}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {153--154}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464656}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present how we have strategically allocated fitness evaluations in a large-scale rule based evolutionary system called ECStar. We describe a strategy that culls potentially weaker solutions early, then later only compete with solutions which have equivalent fitness evaluations, as they are evaluated on more fitness cases. Despite incurring some imprecision in fitness comparison, which arises from not evaluating on all the fitness cases or even the same ones, the strategy allows our system to make effective progress when the resources at its disposal are unpredictably available.}, notes = {Also known as \cite{2464656} Distributed at GECCO-2013.}, } @inproceedings{Li:2013:GECCOcompb, author = {Xianneng Li and Kotaro Hirasawa}, title = {Extended rule-based genetic network programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {155--156}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464655}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decision-making ability with a generalisation property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the if-then decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.}, notes = {Also known as \cite{2464655} Distributed at GECCO-2013.}, } @inproceedings{Marzukhi:2013:GECCOcomp, author = {Syahaneim Marzukhi and Will N. Browne and Mengjie Zhang}, title = {Adaptive artificial datasets to discover the effects of domain features for classification tasks}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {157--158}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464654}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper described an automated pattern generator to generate various synthetic data sets for classification problems, where the problem's complexity can be manipulated autonomously. The Tabu Search technique has been applied in the pattern generator to discover the best combination of domain features in order to adjust the complexity levels of the problem. Experiments confirm that the pattern generator was able to tune the problem's complexity so that it can either increase or decrease the classification performance. The novel contributions in this work enable the effect of domain features that alter classification performance, to become human readable. This work provides a new method for generating artificial datasets at various levels of difficulty where the difficulty levels can be tuned autonomously.}, notes = {Also known as \cite{2464654} Distributed at GECCO-2013.}, } @inproceedings{Rahimi:2013:GECCOcomp, author = {Sara Rahimi and Andrew R. McIntyre and Malcolm I. Heywood and Nur Zincir-Heywood}, title = {Label free change detection on streaming data with cooperative multi-objective genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {159--160}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464652}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Classification under streaming data conditions requires that the machine learning (ML) approach operate interactively with the stream content. Thus, given some initial ML classification capability, it is not possible to assume that stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labelling the stream would be extremely expensive. This work proposes an approach in which the behaviour of GP individuals is used to detect change without the use of labels. Only after detecting a change is label information requested. Benchmarking under a computer network traffic analysis scenario demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.}, notes = {Also known as \cite{2464652} Distributed at GECCO-2013.}, } @inproceedings{Vahdat:2013:GECCOcomp, author = {Ali Vahdat and Malcolm I. Heywood}, title = {Flat vs. symbiotic evolutionary subspace clusterings}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {161--162}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464653}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Subspace clustering coevolves the attribute space supporting clusters at the same time as parameterising the cluster location and combination. Typically, a 'flat' representation is pursued in which individuals describe both the property of individual clusters as well as the combination of clusters used to define the overall solution; hereafter F-ESC. Conversely, a symbiotic approach was recently proposed in which candidate clusters and the combination of clusters are coevolved from independent populations; hereafter S-ESC. In this work a common framework is pursued in order for flat and symbiotic evolutionary subspace clustering to be compared directly. We show that F-ESC might match S-ESC results for data sets with high proportions of cluster support, however, the gap between the two algorithm increases as cluster support decreases.}, notes = {Also known as \cite{2464653} Distributed at GECCO-2013.}, } @inproceedings{Chelly:2013:GECCOcomp, author = {Zeineb Chelly and Zied Elouedi}, title = {A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {163--164}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464657}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The aim of this paper is to develop a new data pre-processing method for the dendritic cell algorithm (DCA) based on Fuzzy Rough Set Theory (FRST). In this new fuzzy-rough model, the data pre-processing phase is based on the fuzzy positive region and the fuzzy dependency degree concepts. Results show that applying FRST is more convenient for the DCA data pre-processing phase yielding much better performance in terms of accuracy.}, notes = {Also known as \cite{2464657} Distributed at GECCO-2013.}, } @inproceedings{Rangel:2013:GECCOcomp, author = {Pablo Rangel and Jos\'{e} Ricardo Potier de Oliveira and Jos\'{e} Gomes de Carvalho,Jr. and Beatriz de Souza Leite Pires de Lima and Guimar\, {a}es, Solange}, title = {A fuzzy evolutionary simulation model (FESModel) for fleet combat strategies}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {165--166}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464658}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computational simulations appear as suitable solution for training military forces with a reduced operational cost. Such simulations require solutions that include models that must be close to reality. This work proposes a solution for an important part of warfare simulation: strategy. Hughes [1] explains that in Modern Warfare, the strategy is the highest level resource, because considers other integrated and non-precision variables. Using Genetic Algorithm (GA) and Fuzzy Logic (FL), this work aims to provide a combat strategy optimisation, considering: improvement of the probability to cause damage on enemy fleet and minimisation of two others variables: mission's cost and risk. The results indicate that model can be extended and incorporated into a real warfare simulation environment}, notes = {Also known as \cite{2464658} Distributed at GECCO-2013.}, } @inproceedings{Sepulveda:2013:GECCOcomp, author = {Martha Johanna Sep\'{u}lveda and Guy Gogniat and Daniel Mauricio Sep\'{u}lveda and Ricardo Pires and Wang Jiang Chau and Marius Strum}, title = {3DMIA: a multi-objective artificial immune algorithm for 3D-MPSoC multi-application 3D-NoC mapping}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {167--168}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464659}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Three dimensional Multiprocessor System-on-Chip (3D-MPSoC) are characterised by the integration of a large amount of hardware components targeting a wide range of application on a single chip. However, heating is one of the major pitfalls of the 3D-MPSoCs. Three dimensional Network-on-Chip (3D-NoC) is used as the communication structure of the 3D-MPSoC. Its main role in the system operation and performance turns critical the optimal 3D-NoC design. Mapping is one of the most critical 3D-NoC parameters, strongly influencing the 3D-MPSoC performance. In this paper we propose the use of a multi-objective immune algorithm (3DMIA), an evolutionary approach to solve the multi-application 3D-NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions constrained by the heating function. Final 3D-NoC configurations enhance up to 73percent the power and 42percent the latency when compared to the previous reported results. We also evaluate the effect on the mutation rate and population size on the convergence speed of 3DMIA. We find that the adaptive mutation rate increases the performance of 3DMIA up to 84percent when compared to static mutation rate approach.}, notes = {Also known as \cite{2464659} Distributed at GECCO-2013.}, } @inproceedings{Aljarah:2013:GECCOcomp, author = {Ibrahim Aljarah and Simone A. Ludwig}, title = {Towards a scalable intrusion detection system based on parallel PSO clustering using mapreduce}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {169--170}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464661}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The growing data traffic in large networks faces new challenges requiring efficient intrusion detection systems. The analysis of this high volume of data traffic to discover attacks has to be done very quickly. However, in order to be able to process large data, new distributed and parallel methods need to be developed. Several approaches are proposed to build intrusion systems using clustering approaches. In this paper, we introduce an intrusion detection system based on a parallel particle swarm optimisation clustering algorithm using the MapReduce framework. The proposed system is scalable in processing large data on commodity hardware.}, notes = {Also known as \cite{2464661} Distributed at GECCO-2013.}, } @inproceedings{Gibson:2013:GECCOcomp, author = {Mike J. Gibson and Ed C. Keedwell and Dragan Savi\'{c}}, title = {Understanding the efficient parallelisation of cellular automata on CPU and GPGPU hardware}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {171--172}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464660}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cellular automata, represented by a discrete set of elements are ideal candidates for parallelisation, particularly on graphics cards using GPGPU technology. This paper shows that the speedups of 50 times over CPU are possible but that the hardware is only partially responsible and the memory model is vital to exploiting the additional computational power of the GPU.}, notes = {Also known as \cite{2464660} Distributed at GECCO-2013.}, } @inproceedings{Lopes:2013:GECCOcomp, author = {Rodolfo A. Lopes and Rodrigo C. Pedrosa Silva and Felipe Campelo and Guimar\, {a}es, Frederico G.}, title = {Dynamic selection of migration flows in island model differential evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {173--174}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464662}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a new approach to the topology configuration problem in the Island Model (IM) is proposed. The mechanism proposed works with a pool of candidates for migration and the choice of immigrants is performed using the usual selection techniques of evolutionary algorithms. Computational tests on IM versions of the Differential Evolution show positive effects of the proposed approach in terms of the number of function evaluations required for convergence.}, notes = {Also known as \cite{2464662} Distributed at GECCO-2013.}, } @inproceedings{Clegg:2013:GECCOcomp, author = {Kester Clegg and Rob Alexander}, title = {The discovery and quantification of risk in high dimensional search spaces}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {175--176}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464669}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We describe a technique used by the ASHiCS project (Automating the Search for Hazards in Complex Systems) to discover high risk air traffic control (ATC) scenarios. We use a fast time ATC simulation of an en-route sector containing multiple flight paths and aircraft types, and into this we inject a serious incident (cabin pressure loss) which forces one aircraft to make an emergency descent. In order to create additional workload for the air traffic controller (ATCo), we also introduce a storm moving across the sector. We measure the associated levels of risk by analysing the simulation outputs, selecting scenarios on basis of most risk and mutating aircraft entry times to see if the search can discover variant scenarios of even greater risk. The search space is extremely large and cannot be exhaustively searched for the worst case; this is a problem for safety engineers who require a context to search results so that event probabilities can be determined. While providing context cannot demonstrate that the worst case scenario has been found over all input permutations, it can indicate the expected frequency of that result in its near neighbourhood, allowing analysts to focus on a much reduced parameter range when investigating those aircraft in conflict.}, notes = {Also known as \cite{2464669} Distributed at GECCO-2013.}, } @inproceedings{Augusto:2013:GECCOcomp, author = {Douglas A. Augusto and Heder S. Bernardino and Helio J.C. Barbosa}, title = {Improving recruitment effectiveness using genetic programming techniques}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {177--178}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464673}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques.}, notes = {Also known as \cite{2464673} Distributed at GECCO-2013.}, } @inproceedings{Canelas:2013:GECCOcomp, author = {Antonio Canelas and Rui Neves and Nuno Horta}, title = {Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {179--180}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464664}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a new Multi-Dimensional SAX-GA approach to pattern discovery using genetic algorithms (GA). The approach is capable of discovering patterns in multi-dimensional financial time series. First, the several dimensions of data are converted to a Symbolic Aggregate approXimation (SAX) representation, which is, then, feed to a GA optimisation kernel. The GA searches for profitable patterns occurring simultaneously in the multi-dimensional time series. Based on the patterns found, the GA produces more robust investment strategies, since the simultaneity of patterns on different dimensions of the data, reinforces the strength of the trading decisions implemented. The proposed approach was tested using stocks from S&P500 index, and is compared to previous reference works of SAX-GA and to the Buy & Hold (B&H) classic investment strategy.}, notes = {Also known as \cite{2464664} Distributed at GECCO-2013.}, } @inproceedings{Churchill:2013:GECCOcomp, author = {Alexander Wainwright Churchill and Phil Husbands and Andrew Philippides}, title = {Tool sequence optimisation using preferential multi-objective search}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {181--182}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464665}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents a new multi-objective approach to tool sequence optimisation in end milling applications. In this way, the process planner is presented with a selection of solutions offering a good trade-off between total machining time and total tooling costs. The majority of previous research has concentrated either on optimising tool selection or machining parameters. In the presented approach, each tool in a sequence has its most important parameter, cutting speed, simultaneously optimised creating a problem with both discrete and continuous properties. The major constraint, excess material, is included as an additional objective. The problem is solved using NSGA-II with preferential search modifications to guide solutions towards the feasible region.}, notes = {Also known as \cite{2464665} Distributed at GECCO-2013.}, } @inproceedings{Curran:2013:GECCOcomp, author = {William Curran and Adrian Agogino and Kagan Tumer}, title = {Partitioning agents and shaping their evaluation functions in air traffic problems with hard constraints}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {183--184}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464666}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hundreds of thousands of hours of delay, costing millions of dollars annually, are reported by US airports. The task of managing delay may be modelled as a multiagent congestion problem with agents who collectively impact the system. In this domain, agents are tightly coupled, and the environment can quickly change, making it difficult for agents to assess how they impact the system. We combine the noise reduction of fitness function shaping, the robustness of cooperative coevolutionary algorithms, and agent partitioning to perform hard constraint optimisation on the congestion and reduce the delay throughout the National Air Space (NAS). Our results show that an autonomous partitioning of the agents using system features leads to up to 540x speed over simple hard constraint enforcement, as well as up to a 21percent improvement in performance over a greedy scheduling solution corresponding to hundreds of hours of delay saved in a single day.}, notes = {Also known as \cite{2464666} Distributed at GECCO-2013.}, } @inproceedings{Kudikala:2013:GECCOcomp, author = {Rajesh Kudikala and Andrew R. Mills and Peter J. Fleming and Graham F. Tanner and Jonathan E. Holt}, title = {Aero engine health management system architecture design using multi-criteria optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {185--186}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464670}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A design process for system architecture design using multi-criteria optimisation is described using a case study of an aero engine health management (EHM) system. The EHM system functional operations need to be deployed in order to satisfy their operational attribute requirements within the constraints of resource limitations. Considering the large discrete search space of decision variables and many-objective functions and constraints, an evolutionary multi-objective genetic algorithm along with a progressive preference articulation (PPA) technique, is used for solving the optimisation problem. Using the PPA technique, the industrial decision maker is able to identify the most significant design constraints (hot spots) and experiment with changing goals for objectives, in order to arrive at a satisfactory non-dominated solutions that takes account of domain knowledge.}, notes = {Also known as \cite{2464670} Distributed at GECCO-2013.}, } @inproceedings{Marceau:2013:GECCOcomp, author = {Ga\'{e}tan Marceau and Pierre Sav\'{e}ant and Marc Schoenauer}, title = {Multiobjective optimization for reducing delays and congestion in air traffic management}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {187--188}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464672}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nowadays, with the increasing traffic density of the European airspace, air traffic management includes the planning and monitoring of the capacity of the network as a way to facilitate the work of the air traffic controllers. This article presents the use of an evolutionary multiobjective algorithm for optimising a schedule on the time of overflight of the way-points. The evaluation function is defined as a probabilistic model, which reports the expected delays and the expected congestion giving the schedule. We believe that the robustness of such decision-support tool can only arise from a precise modelling of the uncertainty at a trajectory level and to propagate it to the sector level, allowing us to compute the probability of congestion. This paper gives the outline of the probabilistic model and the way it is used with an evolutionary algorithm in order to optimise the schedule. The proposed approach is tested against two artificial instances where one consists of 300 aircraft and 16 sectors.}, notes = {Also known as \cite{2464672} Distributed at GECCO-2013.}, } @inproceedings{Ono:2013:GECCOcomp, author = {Satoshi Ono and Takeru Maehara and Hirokazu Sakaguchi and Daisuke Taniyama and Ryo Ikeda and Shigeru Nakayama}, title = {Self-adaptive niching differential evolution and its application to semi-fragile watermarking for two-dimensional barcodes on mobile phone screen}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {189--190}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464668}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an algorithm named Neighbourhood-and Crowding-based Self-Adaptive Differential Evolution (NCjDE) for multi-optima problems. This paper also proposes its application for semi-fragile watermarking of coloured two-dimensional (2D) barcodes with adequate semi-fragileness for copy detection, which are displayed on mobile phone screen and used for quick verification of items such as aeroplane boarding passes and coupons.}, notes = {Also known as \cite{2464668} Distributed at GECCO-2013.}, } @inproceedings{Picek:2013:GECCOcomp, author = {Stjepan Picek and Domagoj Jakobovic and Marin Golub}, title = {Evolving cryptographically sound boolean functions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {191--192}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464671}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper explores the evolution of Boolean functions for a cryptographic usage, with genetic algorithms and genetic programming. We also experiment with a new mutation operator and a new kind of initialisation process. Results obtained show that those modifications can help in obtaining better solutions. The results indicate that it is possible to obtain high quality Boolean functions with algorithms that are not tailor-made for this purpose. Additionally, among the algorithms tested, the best performance was obtained with variations of genetic programming.}, notes = {Also known as \cite{2464671} Distributed at GECCO-2013.}, } @inproceedings{Urquhart:2013:GECCOcomp, author = {Neil Urquhart and Catherine Scott and Emma Hart}, title = {Incorporating emissions models within a multi-objectivevehicle routing problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {193--194}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464663}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The vehicle routing problem with time windows (VRPTW) has previously been investigated as a multi-objective problem. In this paper estimated carbon emissions is added as an objective alongside the number of vehicles required and distance travelled. We term this new problem formulation (E)VRPTW. In order to estimate emissions we require detailed information regarding the nature of the route to be taken. As previous benchmark VRPTW problem instances do not supply such information we generate new problem instances based upon street network data from Open Street Map. Results suggest that by adding emissions as the third objective, in many cases the search may be directed to areas that allow improvement in the distance and vehicles objectives. As emissions and distance are inherently related, we do not search for Pareto fronts. Rather we attempt to find solutions that either minimise distance or minimise vehicles used. Adding the third emissions objective is shown to enable a multi-objective EA to find improved solutions in terms of minimal vehicles or minimal distance when compared to the same multi-objective EA using only two objectives.}, notes = {Also known as \cite{2464663} Distributed at GECCO-2013.}, } @inproceedings{Yuan:2013:GECCOcomp, author = {Xiaoxiao Yuan and Chuanfu Xiao and Xiyu Lv and Jing Liu}, title = {A multi-agent genetic algorithm for resource constrained project scheduling problems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {195--196}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464667}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A multi-agent genetic algorithm is proposed to solve single-mode resource constrained project scheduling problems (MAGA-RCPSPs). In MAGA-RCPSPs, an agent represents a candidate solution to the RCPSP, and all agents live in a lattice like environment, with each agent fixed on a lattice point. In the experiments, benchmark problems Patterson and J30 are used. The results show that MAGA-RCPSPs has a good performance.}, notes = {Also known as \cite{2464667} Distributed at GECCO-2013.}, } @inproceedings{Alrebeish:2013:GECCOcomp, author = {Faisal Alrebeish and Rami Bahsoon}, title = {Using portfolio theory to diversify the dynamic allocation of web services in the cloud}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {197--198}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464674}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we view the Cloud as market place for trading instances of Web Services, which can be bought or leased by web applications. Applications can buy diversity by selecting web services from multiple cloud sellers in a cloud based market. We argue that by diversifying the selection, we can improve the dependability of the application and reduce risks associated with Service Level Agreements (SLA) violations. We propose a novel dynamic adaptive search based software engineering approach, which uses portfolio theory to construct a diversify portfolio of web service instances, traded from multiple Cloud providers. The approach systematically evaluates the Quality of Service (QoS) and risks of the portfolio, compare it to the optimal traded portfolio at a given time, and dynamically decide on a new portfolio and adapt the application accordingly.}, notes = {Also known as \cite{2464674} Distributed at GECCO-2013.}, } @inproceedings{Buzhinsky:2013:GECCOcomp, author = {Igor Buzhinsky and Vladimir Ulyantsev and Fedor Tsarev and Anatoly Shalyto}, title = {Search-based construction of finite-state machines with real-valued actions: new representation model}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {199--200}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464678}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper a search-based method for constructing finite-state machines (FSMs) with continuous (real-valued) output actions is improved. A more flexible FSM representation model is presented and compared with the previous one on the problem of unmanned aircraft control.}, notes = {Also known as \cite{2464678} Distributed at GECCO-2013.}, } @inproceedings{Chitty:2013:GECCOcomp, author = {Darren M. Chitty}, title = {Multi-objective evolutionary auto-tuning for optimising algorithm speed and cache memory usage}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {201--202}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464676}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Modern CPUs are complex with hierarchical cache memory levels, vector instruction sets, instruction level parallelism and multiple processor cores. Hence, extracting the maximum performance for a given algorithm is a complex task and can require the optimisation of a number of parameters. This paper will demonstrate the use of an evolutionary approach to tune a matrix multiplication algorithm in terms of both execution speed and also cache memory usage. Moreover, it will be shown that these objectives conflict to some degree. Hence, a multi-objective evolutionary tuning approach is demonstrated that optimises for both of these objectives establishing a Pareto front of solutions.}, notes = {Also known as \cite{2464676} Distributed at GECCO-2013.}, } @inproceedings{Cody-Kenny:2013:GECCOcomp, author = {Brendan Cody-Kenny and Stephen Barrett}, title = {Self-focusing genetic programming for software optimisation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {203--204}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464681}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Approaches in the area of Search Based Software Engineering (SBSE) have seen Genetic Programming (GP) algorithms applied to the optimisation of software. While the potential of GP for this task has been demonstrated, the complexity of real-world software code bases poses a scalability problem for its serious application. To address this scalability problem, we inspect a form of GP which incorporates a mechanism to focus operators to relevant locations within a program code base. When creating offspring individuals, we introduce operator node selection bias by allocating values to nodes within an individual. Offspring values are inherited and updated when a difference in behaviour between offspring and parent is found. We argue that this approach may scale to find optimal solutions in more complex code bases under further development.}, notes = {Also known as \cite{2464681} Distributed at GECCO-2013.}, } @inproceedings{Mahouachi:2013:GECCOcomp, author = {Rim Mahouachi and Marouane Kessentini and Mel \'{O} Cinn\'{e}ide}, title = {Search-based refactoring detection}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {205--206}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464680}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose an approach to automate the detection of source code refactoring using structural information. Our approach takes as input a list of possible refactorings, a set of structural metrics and the initial and revised versions of the source code. It generates as output a sequence of detected changes in terms of refactorings. In this case, a solution is defined as the sequence of refactoring operations that minimises the metrics variation between the revised version of the software and the version yielded by the application of the refactoring sequence to the initial version of the software. We use and adapt global and local heuristic search algorithms to explore the space of possible solutions.}, notes = {Also known as \cite{2464680} Distributed at GECCO-2013.}, } @inproceedings{c:2013:GECCOcomp, author = {Mon\c{c}\, {a}o, Ana C.L. and Camilo-Jr, Celso G. and Queiroz, Leonardo T. and Rodrigues, Cassio L. and Leit\, {a}o-Jr, Pl\'{\i}nio de S\'{a} and Vincenzi, Auri M.R.}, title = {Applying genetic algorithms to data selection for SQL mutation analysis}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {207--208}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464675}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents an approach to Structured Query Language (SQL) instruction tests via Mutation Analysis that uses Evolutionary Algorithms (GA) to select data to be used in the assessment of mutants. Based on a heuristic perspective, our aim is to select an effective data set which may help detect faults in the SQL instructions of a given application. The results obtained from experiments reveal a good performance using GA metaheuristic.}, notes = {Also known as \cite{2464675} Distributed at GECCO-2013.}, } @inproceedings{Ramirez:2013:GECCOcomp, author = {Aurora Ram\'{\i}rez and Jos\'{e} Ra\'{u}l Romero and Sebasti\'{a}n Ventura}, title = {A novel component identification approach using evolutionary programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {209--210}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464679}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Component identification is a critical phase in software architecture analysis to prevent later errors and control the project time and budget. Obtaining the most appropriate architecture according to predetermined design criteria can be treated as an optimisation problem, especially since the appearance of the Search Based Software Engineering, and its combination with bio-inspired metaheuristics. In this work, an evolutionary programming (EP) algorithm is used to identify components, based on a novel and comprehensible representation of software architectures.}, notes = {Also known as \cite{2464679} Distributed at GECCO-2013.}, } @inproceedings{RealesMateo:2013:GECCOcomp, author = {Pedro Reales Mateo and Macario Polo Usaola}, title = {Automated test generation for multi-state systems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {211--212}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464677}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a genetic algorithm based on mutation testing to generate test cases for classes with multiple states. The fitness function is based on the coverability and the killability of the individuals. The paper includes a small empirical section that shows evidences of the ability of the algorithm to generate good test cases.}, notes = {Also known as \cite{2464677} Distributed at GECCO-2013.}, } @inproceedings{Aulig:2013:GECCOcomp, author = {Nikola Aulig and Markus Olhofer}, title = {Evolutionary generation of neural network update signals for the topology optimization of structures}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {213--214}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464685}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the adaptation of natural load bearing structures like bones and trees, regions subject to high physical loads accumulate structural material based on local stimuli, while it is reduced in others. This strategy can lead to efficient structures and has been modelled in the field of topology optimisation. Instead of modelling the observed strategy we target the evolutionary process, which gave rise to theses strategies. We propose to use an evolutionary process in order to find a suitable mapping from local sensory information to an update signal, based on which a structure is adapted. The target is to evolve a generalisable update signal for quality functions that can not be optimised by existing topology optimisation methods. As a first study, the update signal is represented by a feed-forward neural network model and its weights are tuned by an evolutionary strategy in order to optimize a minimum compliance structure. The resulting update signal is subsequently compared to the true compliance sensitivities and indicate that evolving a neural network update signal by optimisation is a demanding task, yet possible at least for the provided example problem.}, notes = {Also known as \cite{2464685} Distributed at GECCO-2013.}, } @inproceedings{Karafotias:2013:GECCOcomp, author = {Giorgos Karafotias and Mark Hoogendoorn and A.E. Eiben}, title = {Parameter control: strategy or luck?}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {215--216}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464686}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parameter control mechanisms in evolutionary algorithms (EAs) dynamically change the values of the EA parameters during a run. Research over the last two decades has delivered ample examples where an EA using a parameter control mechanism outperforms its static version with fixed parameter values. However, very few have investigated why such parameter control approaches perform better. In principle, it could be the case that using different parameter values alone is already sufficient and EA performance can be improved without sophisticated control strategies. This paper investigates whether very simple random variation in parameter values during an evolutionary run can already provide improvements over static values.}, notes = {Also known as \cite{2464686} Distributed at GECCO-2013.}, } @inproceedings{Luque:2013:GECCOcomp, author = {Gabriel Luque and Enrique Alba}, title = {Math oracles: a new way of designing efficient self-adaptive algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {217--218}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464683}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present a new general methodology to develop self-adaptive methods at a low computational cost. Instead of going purely ad-hoc we define several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behaviour) provided by well-known mathematical models or other prediction systems (the oracle) to build enhanced methods. We show the main steps which should be considered to include this new kind of information into any algorithm. In addition, we actually test the idea on a specific algorithm, a genetic algorithm (GA). Experiments show that our proposal is able to obtain similar, or even better results when it is compared to the traditional algorithm. We also show the benefits in terms of saving time and a lower complexity of parameter settings.}, notes = {Also known as \cite{2464683} Distributed at GECCO-2013.}, } @inproceedings{Mlejnek:2013:GECCOcomp, author = {Jarom\'{\i}r Mlejnek and Ji\v{r}\'{\i} Kubalik}, title = {Evolutionary hyperheuristic for capacitated vehicle routing problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {219--220}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464684}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper a novel constructive hyperheuristic for CVRP is proposed. This hyperheuristic, called HyperPOEMS, is based on an evolutionary-based iterative local search algorithm. Its inherent characteristics make it capable of autonomously searching a structured space of low-level domain specific heuristics for their suitable combinations that produce good solutions to particular problem instance. HyperPOEMS was tested on standard benchmarks and compared to two existing constructive hyperheuristic, HHC-VRP and EHH-VRP. The results show that HyperPOEMS outperforms both compared hyperheuristics and produces solutions competitive to solutions obtained by specialised metaheuristics designed for CVRP.}, notes = {Also known as \cite{2464684} Distributed at GECCO-2013.}, } @inproceedings{Ugolotti:2013:GECCOcomp, author = {Roberto Ugolotti and Youssef S.G. Nashed and Pablo Mesejo and Stefano Cagnoni}, title = {Algorithm configuration using GPU-based metaheuristics}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {221--222}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464682}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimization (PSO) in CUDA is used to automatically tune the parameters of PSO. The parameters were tuned over a set of 8 problems and then tested over 20 problems to assess the generalisation ability of the tuners. We compare the results obtained using such parameters with the 'standard' ones proposed in the literature and the ones obtained by state-of-the-art tuning methods (irace). The results are comparable to the ones suggested for the standard version of PSO (SPSO), and the ones obtained by irace, while the GPU implementation makes tuning time acceptable. To the best of our knowledge, this is the first time that a general purpose library of GPU-based metaheuristics is used to solve this problem, as well as being one of the few cases where DE and PSO are both used as tuners.}, notes = {Also known as \cite{2464682} Distributed at GECCO-2013.}, } @inproceedings{AsteteMorales:2013:GECCOcomp, author = {Sandra Astete Morales and Jialin Liu and Olivier Teytaud}, title = {Noisy optimization convergence rates}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {223--224}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464687}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We consider noisy optimisation problems, without the assumption of variance vanishing in the neighbourhood of the optimum. We show mathematically that evolutionary algorithms with simple rules with exponential number of resamplings lead to a log-log convergence rate (log of the distance to the optimum linear in the log of the number of resamplings), as well as with number of resamplings polynomial in the inverse step-size.}, notes = {Also known as \cite{2464687} Distributed at GECCO-2013.}, } @inproceedings{Goodman:2013:GECCOcomp, author = {Erik D. Goodman}, title = {Introduction to genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {225--246}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466737}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start at the beginning, describing first a classical genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance, described using the schema concept. It will cover some variations on the classical model, some successful applications of genetic algorithms, and advances that are making genetic algorithms more useful.}, notes = {Also known as \cite{2466737} Distributed at GECCO-2013.}, } @inproceedings{OReilly:2013:GECCOcomp, author = {Una-May O'Reilly}, title = {Genetic programming: a tutorial introduction}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {247--264}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480797}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome. Genetic programming evolves a 'program' to solve a problem rather than a single solution. This tutorial introduces the basic genetic programming framework. It explains how the powerful capability of genetic programming is derived from modular algorithmic components: executable representations such as an abstract syntax tree, variation operators that preserve syntax and explore a variable length, hierarchical solution space, appropriately chosen programming functions and fitness function specification.}, notes = {Also known as \cite{2480797} Distributed at GECCO-2013.}, } @inproceedings{Back:2013:GECCOcomp, author = {Thomas B\"{a}ck}, title = {Evolution strategies: basic introduction}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {265--292}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480798}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This tutorial gives a basic introduction to evolution strategies, a class of evolutionary algorithms. Key features such as mutation, recombination and selection operators are explained, and specifically the concept of self-adaptation of strategy parameters is introduced. All algorithmic concepts are explained to a level of detail such that an implementation of basic evolution strategies is possible. Some guidelines for utilization as well as some application examples are given.}, notes = {Also known as \cite{2480798} Distributed at GECCO-2013.}, } @inproceedings{DeJong:2013:GECCOcomp, author = {Kenneth De Jong}, title = {Evolutionary computation: a unified approach}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {293--306}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480799}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480799} Distributed at GECCO-2013.}, } @inproceedings{Brockhoff:2013:GECCOcomp, author = {Dimo Brockhoff}, title = {GECCO 2013 tutorial on evolutionary multiobjective optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {307--334}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2483908}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2483908} Distributed at GECCO-2013.}, } @inproceedings{Rothlauf:2013:GECCOcomp, author = {Franz Rothlauf}, title = {Representations for evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {335--356}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464577}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Successful and efficient use of evolutionary algorithms (EAs) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation. In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this representation.}, notes = {Also known as \cite{2464577} Distributed at GECCO-2013.}, } @inproceedings{Miikkulainen:2013:GECCOcomp, author = {Risto Miikkulainen}, title = {Evolving neural networks}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {357--376}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480800}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.}, notes = {Also known as \cite{2480800} Distributed at GECCO-2013.}, } @inproceedings{Thierens:2013:GECCOcomp, author = {Dirk Thierens and Peter A.N. Bosman}, title = {Model-based evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {377--404}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480801}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480801} Distributed at GECCO-2013.}, } @inproceedings{Wineberg:2013:GECCOcomp, author = {Mark Wineberg}, title = {Statistical analysis for evolutionary computation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {405--438}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482678}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2482678} Distributed at GECCO-2013.}, } @inproceedings{Browne:2013:GECCOcomp, author = {Will N. Browne and Ryan Urbanowicz}, title = {Learning classifier systems: introducing the user-friendly textbook}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {439--468}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2483909}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2483909} Distributed at GECCO-2013.}, } @inproceedings{Lehre:2013:GECCOcomp, author = {Per Kristian Lehre and Pietro S. Oliveto}, title = {Runtime analysis of evolutionary algorithms: basic introduction}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {469--498}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482679}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2482679} Distributed at GECCO-2013.}, } @inproceedings{Auger:2013:GECCOcomp, author = {Anne Auger and Nikolaus Hansen}, title = {Tutorial CMA-ES: evolution strategies and covariance matrix adaptation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {499--520}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2483910}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2483910} Distributed at GECCO-2013.}, } @inproceedings{CoelloCoello:2013:GECCOcomp, author = {Carlos Artemio Coello Coello}, title = {Constraint-handling techniques used with evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {521--544}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480802}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480802} Distributed at GECCO-2013.}, } @inproceedings{Whitley:2013:GECCOcomp, author = {L. Darrell Whitley and Andrew M. Sutton}, title = {Elementary landscapes: theory and applications}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {545--566}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480803}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480803} Distributed at GECCO-2013.}, } @inproceedings{Neumann:2013:GECCOcomp, author = {Frank Neumann and Carsten Witt}, title = {Bioinspired computation in combinatorial optimization: algorithms and their computational complexity}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {567--590}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466738}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2466738} Distributed at GECCO-2013.}, } @inproceedings{Verel:2013:GECCOcomp, author = {Sebastien Verel}, title = {Fitness landscapes and graphs: multimodularity, ruggedness and neutrality}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {591--616}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480804}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the most commonly-used metaphors to describe the process of heuristic search methods in solving combinatorial optimisation problems is that of a fitness landscape. The landscape metaphor appears most commonly in work related to evolutionary algorithms, however, it can be used for search in general; the search space can be regarded as a spatial structure where each point (solution) has a height (objective function value) forming a landscape surface. In this scenario, the search process would be an adaptive-walk over a landscape that can range from having many peaks of high fitness flanked by cliffs falling to profound valleys of low fitness, to being smooth, with low hills and gentle valleys. Combinatorial landscapes can be seen as graphs whose vertices are the possible configurations. If two configurations can be transformed into each other by a suitable operator move, then we can trace an edge between them. The resulting graph, with an indication of the fitness at each vertex, is a representation of the given problem fitness landscape. The study of fitness landscapes consists in analysing this graph or a relevant partition of it, with respect to the search dynamics or problem difficulty. This advanced tutorial will give an overview of the origins of the fitness landscape metaphor, and will cover alternative ways to define fitness landscapes in evolutionary computation. The two main geometries: multimodal and neutral landscapes, which correspond to two different graph partitions found in combinatorial optimisation, will be considered, as well as the dynamics of metaheuristics searching on them. A short demonstration of using paradiseo software will be made to analyse the fitness landscape in practice. Furthermore, the relationship between problem hardness and fitness landscape metrics (i.e. autocorrelation, fitness distance correlation, neutral degree, etc), and the local optima network properties, studied in recent work, will be deeply analysed. Finally, the tutorial will conclude with a brief survey of open questions and recent research directions on fitness landscapes such as multiobjective search space.}, notes = {Also known as \cite{2480804} Distributed at GECCO-2013.}, } @inproceedings{Doerr:2013:GECCOcompa, author = {Benjamin Doerr and Carola Doerr}, title = {Black-box complexity: from complexity theory to playing mastermind}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {617--640}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482680}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2482680} Distributed at GECCO-2013.}, } @inproceedings{Aguirre:2013:GECCOcompa, author = {Hern\'{a}n Aguirre}, title = {Advances on many-objective evolutionary optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {641--666}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2483911}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2483911} Distributed at GECCO-2013.}, } @inproceedings{Yang:2013:GECCOcomp, author = {Shengxiang Yang}, title = {Evolutionary computation for dynamic optimization problems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {667--682}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480805}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480805} Distributed at GECCO-2013.}, } @inproceedings{Spector:2013:GECCOcomp, author = {Lee Spector}, title = {Expressive genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {683--714}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480806}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as elucidated by formal language theory, and from the ease with which various computational structures can be produced by random code generation and by the action of genetic operators. Highly expressive languages can facilitate the evolution of programs for any computable function using, when appropriate, multiple data types, evolved subroutines, evolved control structures, evolved data structures, and evolved modular program and data architectures. In some cases expressive languages can even support the evolution of programs that express methods for their own reproduction and variation (and hence for the evolution of their offspring). This tutorial will begin with a comparative survey of approaches to the evolution of programs in expressive programming languages ranging from machine code to graphical and grammatical representations. Within this context it will then provide a detailed introduction to the Push programming language, which was designed specifically for expressiveness and specifically for use in genetic programming systems. Push programs are syntactically unconstrained but can nonetheless make use of multiple data types and express arbitrary control structures, supporting the evolution of complex, modular programs in a particularly simple and flexible way. The Push language will be described and ten years of Push-based research, including the production of human-competitive results, will be briefly surveyed. The tutorial will conclude with a discussion of recent enhancements to Push that are intended to support the evolution of complex and robust software systems.}, notes = {Also known as \cite{2480806} Distributed at GECCO-2013.}, } @inproceedings{Miller:2013:GECCOcompa, author = {Julian F. Miller}, title = {GECCO 2013 tutorial: cartesian genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {715--740}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2464578}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with Peter Thomson in 1997. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). This tutorial is will cover the basic technique, advanced developments and applications to a variety of problem domains. The first edited book on CGP was published by Springer in September 2011. CGP has its own dedicated website http://www.cartesiangp.co.uk}, notes = {Also known as \cite{2464578} Distributed at GECCO-2013.}, } @inproceedings{Bacardit:2013:GECCOcomp, author = {Jaume Bacardit and Xavier Llor\`{a}}, title = {Large scale data mining using genetics-based machine learning}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {741--764}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480807}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480807} Distributed at GECCO-2013.}, } @inproceedings{Tomassini:2013:GECCOcomp, author = {Marco Tomassini}, title = {Introduction to evolutionary game theory}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {765--778}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480808}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480808} Distributed at GECCO-2013.}, } @inproceedings{Jansen:2013:GECCOcomp, author = {Thomas Jansen and Christine Zarges}, title = {Artificial immune systems for optimisation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {779--796}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480809}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480809} Distributed at GECCO-2013.}, } @inproceedings{Stanley:2013:GECCOcomp, author = {Kenneth O. Stanley}, title = {Generative and developmental systems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {797--826}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466739}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In evolutionary computation it is common for the genotype to map directly to the phenotype such that each gene in the genotype represents a single discrete component of the phenotype. While this convention is widespread, in nature the mapping is not direct. Instead, the genotype maps to the phenotype through a process of development, which means that each gene may be activated multiple times and in multiple places in the process of constructing the phenotype. This tutorial will examine why such indirect mapping may be critical to the continuing success of evolutionary computation. Rather than just an artifact of nature, indirect mapping means that vast phenotypic spaces (e.g. the 100 trillion connection human brain) can be searched effectively in a space of far fewer genes (e.g. the 30,000 gene human genome). The tutorial will introduce this research area, called Generative and Developmental Systems (GDS), by surveying milestones in the field, exploring GDS-based representations, and introducing its most profound puzzles. Most importantly, what is the right abstraction of natural development to capture its essential advantages without introducing unnecessary overhead into the search?}, notes = {Also known as \cite{2466739} Distributed at GECCO-2013.}, } @inproceedings{Gagne:2013:GECCOcomp, author = {Christian Gagn\'{e}}, title = {Evolutionary computation for supervised learning}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {827--844}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480810}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480810} Distributed at GECCO-2013.}, } @inproceedings{Suganthan:2013:GECCOcomp, author = {Ponnuthurai N. Suganthan}, title = {Differential evolution: recent advances}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {845--876}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480811}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480811} Distributed at GECCO-2013.}, } @inproceedings{Sinha:2013:GECCOcomp, author = {Ankur Sinha and Pekka Malo and Kalyanmoy Deb}, title = {Evolutionary bilevel optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {877--892}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480812}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480812} Distributed at GECCO-2013.}, } @inproceedings{Stutzle:2013:GECCOcomp, author = {Thomas St\"{u}tzle and L\'{o}pez-Ib\'{a}\, {n}ez, Manuel}, title = {Automatic (offline) configuration of algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {893--918}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482681}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2482681} Distributed at GECCO-2013.}, } @inproceedings{Soule:2013:GECCOcomp, author = {Terence Soule}, title = {Designing and building powerful, inexpensive robots for evolutionary research}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {919--934}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480813}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480813} Distributed at GECCO-2013.}, } @inproceedings{Squillero:2013:GECCOcomp, author = {Giovanni Squillero}, title = {Industrial applications of evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {935--956}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480814}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480814} Distributed at GECCO-2013.}, } @inproceedings{Loiacono:2013:GECCOcomp, author = {Daniele Loiacono and Mike Preuss}, title = {Computational intelligence and games}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {957--978}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480815}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480815} Distributed at GECCO-2013.}, } @inproceedings{Bartz-Beielstein:2013:GECCOcomp, author = {Thomas Bartz-Beielstein and Martin Zaefferer and Boris Naujoks}, title = {How to create meaningful and generalizable results}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {979--1004}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480816}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2480816} Distributed at GECCO-2013.}, } @inproceedings{Galanter:2013:GECCOcomp, author = {Philip Galanter}, title = {Computational aesthetic evaluation: automated fitness functions for evolutionary art, design, and music}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1005--1038}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2483912}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2483912} Distributed at GECCO-2013.}, } @inproceedings{Trujillo:2013:GECCOcomp, author = {Leonardo Trujillo and Lee Spector and Enrique Naredo and Yuliana Mart\'{\i}nez}, title = {A behavior-based analysis of modal problems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1047--1054}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482682}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming (GP) has proved to be a powerful tool for (semi)automated problem solving in various domains. However, while the algorithmic aspects of GP have been a primary object of study, there is a need to enhance the understanding of the problems where GP is applied. One particular goal is to categorise problems in a meaningful way, in order to select the best tools that can possibly be used to solve them. This paper studies modal problems, a conceptual class of problems recently proposed by Spector at GECCO 2012. Modal problems are those for which a solution program requires different modes of operation for different contexts. The thesis of this paper is that modality, in this sense, is better understood by analysing program performance in behavioural space. The behaviour-based perspective is seen as part of a scale of different forms of analysing performance; with a coarse view given by a global fitness value and a highly detailed view provided by the semantics approach. On the other hand, behavioral analysis is seen as a flexible approach where the context of a program's performance is considered at in a domain-specific manner. The experimental evidence presented here suggests that behaviour-based search could allow a GP to find programs with disjoint behavioural structures, that can satisfy the requirements of each mode of operation of a modal problem.}, notes = {Also known as \cite{2482682} Distributed at GECCO-2013.}, } @inproceedings{Chicano:2013:GECCOcomp, author = {Francisco Chicano and Gabriel Luque and Enrique Alba}, title = {Problem understanding through landscape theory}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1055--1062}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482683}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to understand the structure of a problem we need to measure some features of the problem. Some examples of measures suggested in the past are autocorrelation and fitness-distance correlation. Landscape theory, developed in the last years in the field of combinatorial optimisation, provides mathematical expressions to efficiently compute statistics on optimization problems. In this paper we discuss how can we use landscape theory in the context of problem understanding and present two software tools that can be used to efficiently compute the mentioned measures.}, notes = {Also known as \cite{2482683} Distributed at GECCO-2013.}, } @inproceedings{Stoean:2013:GECCOcomp, author = {Catalin Stoean and Mike Preuss and Ruxandra Stoean}, title = {EA-based parameter tuning of multimodal optimization performance by means of different surrogate models}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1063--1070}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482684}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the current study, parameter tuning is performed for two evolutionary optimisation techniques, Covariance Matrix Adaptation Evolution Strategy and Topological Species Conservation. They are applied for three multimodal benchmark functions with various properties and several outputs are considered. A data set with input parameters and metaheuristic outcomes is used for training four surrogate models. They are then each used by a genetic algorithm that is employed for searching the best parameter settings for the initial approaches. The genetic algorithm uses the model outputs as the direct fitness evaluation and only the best found parameter setting is tested within the original metaheuristics. Each model quality is priory evaluated, but they are all subsequently used in the search process to observe how the (in)accuracy influences the final result. Additionally, the genetic algorithm is used for tuning these approaches directly to test if the search conducts to the same parameter set, or at least close to it.}, notes = {Also known as \cite{2482684} Distributed at GECCO-2013.}, } @inproceedings{McClymont:2013:GECCOcomp, author = {Kent McClymont}, title = {Recent advances in problem understanding: changes in the landscape a year on}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1071--1078}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482685}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper provides an updated survey of new literature in, and related to, the field of problem understanding which has been published or made available since January 2012. The bibliographic information from the survey is available online at http://bit.ly/ZWoY3X. The survey covers work on the topics of: Benchmark Problems; Problem Decomposition & Multiobjectivisation; Landscape Analysis; Problem Difficulty; and Algorithm Selection & Performance Prediction. In addition, special attention is drawn to three recently published and excellent topic specific surveys. A side note is also made regarding the parallels between problem understanding, and specifically landscape analysis and the work of fitness landscape analysis in theoretical, conventional and evolutionary biology.}, notes = {Also known as \cite{2482685} Distributed at GECCO-2013.}, } @inproceedings{Pigden:2013:GECCOcomp, author = {Tim Pigden}, title = {Missing from the model: orders, drivers, tractors and trailers and non-linear loading}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1079--1084}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482686}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Vehicle Routing Problem (VRP) was originally defined in 1959. The model used was a simple one that was appropriate to the software and hardware systems of the time. Since then many hundreds of papers have been written addressing the VRP and variants on it. Almost all are based on the original model or extensions of it. In particular notions of demand and the vehicle are adopted, seemingly without question. Capacity constraints, including volume, are considered to be linear. But this model does not match that used in commercial software -- such as Transport Management Systems (TMS). In particular the concepts of Order and separate resources corresponding to the Driver, the Tractor Unit and the Trailer in the TMS need to be properly addressed to solve a variety of common real-world problems. This paper shows, through examples taken from Optrak customers, how without these concepts some common aspects of the problem cannot be addressed and how any attempt to map them onto the standard VRP formulation will result in major inaccuracies in the model and hence the usefulness of the results.}, notes = {Also known as \cite{2482686} Distributed at GECCO-2013.}, } @inproceedings{Ochoa:2013:GECCOcomp, author = {Gabriela Ochoa}, title = {Search methodologies in real-world software engineering}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1085--1088}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482687}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the aims of software engineering is to reduce overall software costs. Optimisation is, therefore, relevant to the process of software development. This article describes recent case studies on the application of modern search methodologies to challenging real-world problems in software engineering. It also describes a recent research initiative: Dynamic Adaptive Automated Software Engineering (DAASE), whose goal is to embed optimisation into deployed software to create self-optimising adaptive systems. The article accompanies an invited talk for the Workshop on Bridging the Gap between Industry and Academia in Optimisation to be held as part of GECCO 2013.}, notes = {Also known as \cite{2482687} Distributed at GECCO-2013.}, } @inproceedings{Giagkiozis:2013:GECCOcomp, author = {Ioannis Giagkiozis and Robert J. Lygoe and Peter J. Fleming}, title = {Liger: an open source integrated optimization environment}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1089--1096}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466801}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Although there exists a number of optimisation frameworks only commercial and closed source software address, to an extent, real-world optimization problems and arguably these software packages are not very easy to use. In this work we introduce an open source integrated optimization environment which is designed to be extensible and have a smooth learning curve so that it can be used by the non-expert in industry. We call this environment, Liger. Liger is an application that is built about a visual programming language, by which optimisation work-flows can be created. Additionally, Liger provides a communication layer with external tools, whose functionality can be directly integrated and used with native components. This fosters code reuse and further reduces the required effort on behalf of the practitioner in order to obtain a solution to the optimisation problem. Furthermore, there exists a number of available algorithms which are fully configurable, however should the need arise new algorithms can also be created just as easily by reusing what we call operator nodes. Operator nodes perform specific tasks on a set, or a single solution. Lastly as visual exploration of the obtained solutions is essential for decision makers, we also provide state-of-the art visualisation capabilities.}, notes = {Also known as \cite{2466801} Distributed at GECCO-2013.}, } @inproceedings{Urquhart:2013:GECCOcompa, author = {Neil Urquhart and Catherine Scott and Emma Hart}, title = {Using graphical information systems to improve vehicle routing problem instances}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1097--1102}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466802}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper makes the assertion that vehicle routing research has produced increasingly more powerful problem solvers, but has not increased the realism or complexity of typical problem instances. This paper argues that the time has come of use realistic street network data to increase the relevance and challenge of our work. A particular benefit of real world street data is the ability to support vehicle emissions modelling. Thus allowing emissions to be used as an optimisation criterion. Two on-line demonstrations are presented which demonstrate the use of GIS data obtained from Open Street Map and Google Maps. The demonstrations prove the concept that Evolutionary Algorithms may be used to solve problem instances that are based upon GIS derived data.}, notes = {Also known as \cite{2466802} Distributed at GECCO-2013.}, } @inproceedings{Filipivc:2013:GECCOcomp, author = {Bogdan Filipi\v{c} and Tea Tu\v{s}ar}, title = {Challenges of applying optimization methodology in industry}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1103--1104}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482688}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This presentation starts with two case studies of applying optimisation methodology in industry, one involving numerical optimization based on simulation models, and the other combinatorial optimization with specific constraints and objectives. These case studies serve to identify some of the challenges frequently met by solution providers for industrial optimization problems. Based on our experience in applying optimization methodology in industry, we then provide suggestions for dealing with these challenges in order to bridge the gap between academia and industry in optimization.}, notes = {Also known as \cite{2482688} Distributed at GECCO-2013.}, } @inproceedings{Aleb:2013:GECCOcomp, author = {Nassima Aleb and Samir Kechid}, title = {An interpolation based crossover operator for genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1107--1112}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482689}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new crossover operator for genetic programming. We exploit two concepts of formal methods: Weakest precondition and Craig interpolation, to perform semantically aware crossover. Weakest preconditions are used to locate faulty parts of a program and Craig interpolation is used to correct these ones.}, notes = {Also known as \cite{2482689} Distributed at GECCO-2013.}, } @inproceedings{Fitzgerald:2013:GECCOcompa, author = {Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan}, title = {A bootstrapping approach to reduce over-fitting in genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1113--1120}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482690}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Historically, the quality of a solution in Genetic Programming (GP) was often assessed based on its performance on a given training sample. However, in Machine Learning, we are more interested in achieving reliable estimates of the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data during training without actually using any additional data. We do this by employing a technique called bootstrapping that repeatedly re-samples with replacement from the training data and helps estimate sensitivity of the individual in question to small variations across these re-sampled data sets. We minimise this sensitivity, as measured by the Bootstrap Standard Error, together with the training error, in an effort to evolve models that generalise better to the unseen data. We evaluate the proposed technique on four binary classification problems and compare with a standard GP approach. The results show that for the problems undertaken, the proposed method not only generalises significantly better than standard GP while the training performance improves, but also demonstrates a strong side effect of containing the tree sizes.}, notes = {Also known as \cite{2482690} Distributed at GECCO-2013.}, } @inproceedings{Kommenda:2013:GECCOcomp, author = {Michael Kommenda and Gabriel Kronberger and Stephan Winkler and Michael Affenzeller and Stefan Wagner}, title = {Effects of constant optimization by nonlinear least squares minimization in symbolic regression}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1121--1128}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482691}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this publication a constant optimisation approach for symbolic regression is introduced to separate the task of finding the correct model structure from the necessity to evolve the correct numerical constants. A gradient-based nonlinear least squares optimisation algorithm, the Levenberg-Marquardt (LM) algorithm, is used for adjusting constant values in symbolic expression trees during their evolution. The LM algorithm depends on gradient information consisting of partial derivations of the trees, which are obtained by automatic differentiation. The presented constant optimization approach is tested on several benchmark problems and compared to a standard genetic programming algorithm to show its effectiveness. Although the constant optimization involves an overhead regarding the execution time, the achieved accuracy increases significantly as well as the ability of genetic programming to learn from provided data. As an example, the Pagie-1 problem could be solved in 37 out of 50 test runs, whereas without constant optimisation it was solved in only 10 runs. Furthermore, different configurations of the constant optimisation approach (number of iterations, probability of applying constant optimisation) are evaluated and their impact is detailed in the results section.}, notes = {Also known as \cite{2482691} Distributed at GECCO-2013.}, } @inproceedings{Kvasnivcka:2013:GECCOcomp, author = {Vladim\'{\i}r Kvasni\v{c}ka and Ladislav Clementis and Ji\v{r}\'{\i} Posp\'{\i}chal}, title = {An extension of hill-climbing with learning applied to a symbolic regression of boolean functions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1129--1134}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466803}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we discuss an application of simple stochastic optimisation algorithm called the hill climbing with learning (HCwL) for a study of symbolic regression. A fundamental role in this approach plays the so-called probability vector w = (w1, w2, ..., wn) where an entry 0 w+i 1 specifies a probability that an i-th component of solution (e. g. a bit in binary representation) has a binary 1 value. An integral part of HCwL is a mutation process, where from a current solution xold is created a new solution xnew by a stochastic mutation process. The used probability vector w (considered here as a special type of collective memory) serves as an auxiliary device for a construction of new mutated solution xnew; in particular, it predicts promising directions during its creation that are specified by the previous history of adaptation process.}, notes = {Also known as \cite{2466803} Distributed at GECCO-2013.}, } @inproceedings{Voglis:2013:GECCOcomp, author = {Costas Voglis}, title = {Adapt-MEMPSODE: a memetic algorithm with adaptive selection of local searches}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1137--1144}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466804}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {MEMPSODE global optimisation software tool integrates Particle Swarm Optimisation, a prominent population-based stochastic algorithm, with well established efficient local search procedures. In the original description of the algorithm [17] a single local search with specific parameters was applied at selected best position vectors. In this work we present an adaptive variant of MEMPSODE where the local search is selected from a predefined pool of different algorithms. The choice of each local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of the local search. This new version of the algorithm, Adapt-MEMPSODE, is benchmarked against BBOB 2013 test bed. The results show great improvement with respect to the static version that was also benchmarked in earlier workshop.}, notes = {Also known as \cite{2466804} Distributed at GECCO-2013.}, } @inproceedings{Pal:2013:GECCOcomp, author = {L\'{a}szl\'{o} P\'{a}l}, title = {Benchmarking a hybrid multi level single linkagealgorithm on the bbob noiseless testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1145--1152}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482692}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi Level Single Linkage (MLSL) is a well known stochastic global optimisation method. In this paper, a new hybrid variant (HMLSL) of the MLSL algorithm is presented. The most important improvements are related to the sampling phase: the sample is generated from a Sobol quasi-random sequence and a few percent of the population is further improved by using crossover and mutation operators like in a traditional differential evolution (DE) method. The aim of this study is to evaluate the performance of the new HMLSL algorithm on the testbed of 24 noiseless functions. The new algorithm is also compared against a simple MLSL and a traditional DE in order to identify the benefits of the applied improvements. The results confirm that the HMLSL outperforms the MLSL and DE methods. The new method has a larger probability of success and usually is faster especially in the final stage of the optimization than the other two algorithms.}, notes = {Also known as \cite{2482692} Distributed at GECCO-2013.}, } @inproceedings{Pal:2013:GECCOcompa, author = {L\'{a}szl\'{o} P\'{a}l}, title = {Comparison of multistart global optimization algorithms on the BBOB noiseless testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1153--1160}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482693}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi Level Single Linkage is a multi-start, stochastic global optimisation method which relies on random sampling and local search. In this paper, we bench-marked three variants of the MLSL algorithm by using two gradient based and a derivative-free local search method on the noiseless function testbed. The three methods were also compared with a commercial multistart solver, called OQNLP (OptQuest/NLP). Our experiment showed that, the results may be influenced essentially by the applied local search procedure. Depending of the type of the problem the gradient based local search methods are faster in the initial stage of the optimisation, while the derivative-free method show a superior performance in the final phase for moderate dimensions. Considering the percentage of the solved problems, OQNLP is similar or even better (for multi-modal and weakly structured functions) in 5-D than the MLSL method equipped with the gradient type local search methods, while on 20-D the latter algorithms are usually more faster.}, notes = {Also known as \cite{2482693} Distributed at GECCO-2013.}, } @inproceedings{Liao:2013:GECCOcomp, author = {Tianjun Liao and Thomas St\"{u}tzle}, title = {Bounding the population size of IPOP-CMA-ES on the noiseless BBOB testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1161--1168}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482694}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A variant of CMA-ES that uses occasional restarts coupled with an increasing population size, which is called IPOP-CMA-ES, has shown to be a top performing algorithm on the BBOB benchmark set. In this paper, we test a mechanism that bounds the maximum size that the population may reach in IPOP-CMA-ES, and we experimentally explore the impact of a maximum population size on the BBOB benchmark set. In the proposed bounding mechanism, we use a maximum population size of 10 times D2 where D is problem dimension. Once the maximum population size is reached or surpassed, the population size is reset to its default starting value lambda, which is defined by the lambda = 4 + 3 ln(D). Our experimental results show that our scheme for the population size update can lead to improved performances on separable and weakly structured multi-modal functions.}, notes = {Also known as \cite{2482694} Distributed at GECCO-2013.}, } @inproceedings{Liao:2013:GECCOcompa, author = {Tianjun Liao and Thomas St\"{u}tzle}, title = {Testing the impact of parameter tuning on a variant of IPOP-CMA-ES with a bounded maximum population size on the noiseless BBOB testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1169--1176}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482695}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we experimentally explore the influence tuned parameter settings have on an IPOP-CMA-ES variant that uses a maximum bound on the population size. We followed our earlier work, where we exposed seven parameters that control parameters of IPOP-CMA-ES, and tune them by applying irace, an automatic algorithm configuration tool. A comparison of the tuned to the default settings on the BBOB benchmark shows that for difficult problems such as multi-modal functions with weak global structure, the tuned parameter settings can result in significant improvements over the default settings.}, notes = {Also known as \cite{2482695} Distributed at GECCO-2013.}, } @inproceedings{Loshchilov:2013:GECCOcomp, author = {Ilya Loshchilov and Marc Schoenauer and Michele S\`{e}bag}, title = {Bi-population CMA-ES agorithms with surrogate models and line searches}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1177--1184}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482696}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, three extensions of the BI-population Covariance Matrix Adaptation Evolution Strategy with weighted active covariance matrix update (BIPOP-aCMA-ES) are investigated. First, to address expensive optimisation, we benchmark a recently proposed extension of the self-adaptive surrogate-assisted CMA-ES which benefits from more intensive surrogate model exploitation (BIPOP-saACM-k). Second, to address separable optimization, we propose a hybrid of BIPOP-aCMA-ES and STEP algorithm with coordinate-wise line search (BIPOP-aCMA-STEP). Third, we propose HCMA, a hybrid of BIPOP-saACM-k, STEP and NEWUOA to benefit both from surrogate models and line searches. All algorithms were tested on the noiseless BBOB testbed using restarts till a total number of function evaluations of 106n was reached, where n is the dimension of the function search space. The comparison shows that BIPOP-saACM-k outperforms its predecessor BIPOP-saACM up to a factor of 2 on ill-conditioned problems, while BIPOP-aCMA-STEP outperforms the original BIPOP-based algorithms on separable functions. The hybrid HCMA algorithm demonstrates the best overall performance compared to the best algorithms of the BBOB-2009, BBOB-2010 and BBOB-2012 when running for more than 100n function evaluations.}, notes = {Also known as \cite{2482696} Distributed at GECCO-2013.}, } @inproceedings{Liao:2013:GECCOcompb, author = {Tianjun Liao and Thomas St\"{u}tzle}, title = {Expensive optimization scenario: IPOP-CMA-ES with a population bound mechanism for noiseless function testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1185--1192}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482697}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we test the results from the default and tuned IPOP-CMA-ES with population bound mechanism (labelled as def and texp, respectively) [8, 9] on the expensive optimisation scenario of the BBOB benchmark. In texp [9], seven parameters that directly control the internal parameters were tuned by applying an automatic algorithm configuration tool on the solution quality after 100 times D function evaluations. We compare the results of texp to those of the default variant (def) [8,9] in the expensive optimization scenario. We find that texp often converges faster than def.}, notes = {Also known as \cite{2482697} Distributed at GECCO-2013.}, } @inproceedings{Sawyerr:2013:GECCOcomp, author = {Babatunde A. Sawyerr and Aderemi O. Adewumi and Montaz M. Ali}, title = {Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1193--1200}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482698}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, a real-coded genetic algorithm (RCGA) which incorporates an exploratory search mechanism based on vector projection termed projection-based RCGA (PRCGA) is benchmarked on the noise free BBOB 2013 testbed. It is an enhanced version of RCGA-P in [22, 23]. The projection operator incorporated in PRCGA shows promising exploratory search capability in some problem landscape. PRCGA is equipped with a multiple independent restart mechanism and a stagnation alleviation mechanism. The maximum number of function evaluations (#FEs) for each test run is set to 105 times the problem dimension. PRCGA shows encouraging results on several problems in the low and moderate search dimensions. It is able to solve each type of problem with the dimension up to 40 with lower precision but not all the functions to the desired level of accuracy of 10-8 especially for high conditioning and multi-modal functions within the specified maximum #FEs.}, notes = {Also known as \cite{2482698} Distributed at GECCO-2013.}, } @inproceedings{Holtschulte:2013:GECCOcomp, author = {Neal J. Holtschulte and Melanie Moses}, title = {Benchmarking cellular genetic algorithms on the BBOB noiseless testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1201--1208}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482699}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we evaluate 2 cellular genetic algorithms (CGAs), a single-population genetic algorithm, and a hill-climber on the Black Box Optimisation Benchmarking testbed. CGAs are fine grain parallel genetic algorithms with a spatial structure imposed by embedding individuals in a connected graph. They are popular for their diversity-preserving properties and efficient implementations on parallel architectures. We find that a CGA with a uni-directional ring topology outperforms the canonical CGA that uses a bi-directional grid topology in nearly all cases. Our results also highlight the importance of carefully chosen genetic operators for finding precise solutions to optimization problems.}, notes = {Also known as \cite{2482699} Distributed at GECCO-2013.}, } @inproceedings{Hutter:2013:GECCOcomp, author = {Frank Hutter and Holger Hoos and Kevin Leyton-Brown}, title = {An evaluation of sequential model-based optimization for expensive blackbox functions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1209--1216}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2501592}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We benchmark a sequential model-based optimisation procedure, SMAC-BBOB, on the BBOB set of black-box functions. We demonstrate that with a small budget of 10xD evaluations of D-dimensional functions, SMAC-BBOB in most cases outperforms the state-of-the-art blackbox optimiser CMA-ES. However, CMA-ES benefits more from growing the budget to 100xD, and for larger number of function evaluations SMAC-BBOB also requires increasingly large computational resources for building and using its models.}, notes = {Also known as \cite{2501592} Distributed at GECCO-2013.}, } @inproceedings{Tran:2013:GECCOcomp, author = {Thanh-Do Tran and Dimo Brockhoff and Bilel Derbel}, title = {Multiobjectivization with NSGA-ii on the noiseless BBOB testbed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1217--1224}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482700}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The idea of multiobjectivization is to reformulate a single-objective problem as a multiobjective one. In one of the scarce studies proposing this idea for problems in continuous domains, the distance to the closest neighbour (DCN) in the population of a multiobjective algorithm has been used as the additional (dynamic) second objective. As no comparison with other state-of-the-art single-objective optimisers has been presented for this idea, we have bench-marked two variants (with and without the second DCN objective) of the original NSGA-II algorithm using two different mutation operators on the noiseless BBOB'2013 testbed. It turns out that multiobjectivization helps for several of the 24 benchmark functions, but that, compared to the best algorithms from BBOB'2009, a significant performance loss is visible. Moreover, on some functions, the choice of the mutation operator has a stronger impact on the performance than whether multiobjectivization is employed or not.}, notes = {Also known as \cite{2482700} Distributed at GECCO-2013.}, } @inproceedings{Auger:2013:GECCOcompa, author = {Anne Auger and Dimo Brockhoff and Nikolaus Hansen}, title = {Benchmarking the local metamodel CMA-ES on the noiseless BBOB'2013 test bed}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1225--1232}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482701}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper evaluates the performance of a variant of the local-meta-model CMA-ES (lmm-CMA) in the BBOB 2013 expensive setting. The lmm-CMA is a surrogate variant of the CMA-ES algorithm. Function evaluations are saved by building, with weighted regression, full quadratic meta-models to estimate the candidate solutions' function values. The quality of the approximation is appraised by checking how much the predicted rank changes when evaluating a fraction of the candidate solutions on the original objective function. The results are compared with the CMA-ES without meta-modelling and with previously benchmarked algorithms, namely BFGS, NEWUOA and saACM. It turns out that the additional meta-modeling improves the performance of CMA-ES on almost all BBOB functions while giving significantly worse results only on the attractive sector function. Over all functions, the performance is comparable with saACM and the lmm-CMA often outperforms NEWUOA and BFGS starting from about 2 times D2 function evaluations with D being the search space dimension.}, notes = {Also known as \cite{2482701} Distributed at GECCO-2013.}, } @inproceedings{Iqbal:2013:GECCOcomp, author = {Muhammad Iqbal and Will N. Browne and Mengjie Zhang}, title = {Comparison of two methods for computing action values in XCS with code-fragment actions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1235--1242}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482702}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {XCS is a learning classifier system that uses accuracy-based fitness to learn a problem. Commonly, a classifier rule in XCS is encoded using a ternary alphabet based condition and a numeric action. Previously, we implemented a code-fragment action based XCS, called XCSCFA, where the typically used numeric action was replaced by a genetic programming like tree-expression. In XCSCFA, the action value in a classifier was computed by loading the terminal symbols in the action-tree with the corresponding binary values in the condition of the classifier rule. This enabled accurate, general and compact rule sets to be simply produced. The main contribution of this work is to investigate an intuitive way, i.e. using the environmental instance, to compute the action value in XCSCFA, instead of the condition of the classifier rule. The methods will be compared in five different Boolean problem domains, i.e. multiplexer, even-parity, majority-on, design verification, and carry problems. The environmental instance based XCSCFA approach had better classification performance than standard XCS as well as classifier condition based XCSCFA and solved all the problems experimented here. In addition it produced more general and compact classifier rules in the final solution. However, classifier condition based XCSCFA has the advantage of producing the optimal classifiers such that they are clearly separated from the sub-optimal ones in certain domains.}, notes = {Also known as \cite{2482702} Distributed at GECCO-2013.}, } @inproceedings{Marzukhi:2013:GECCOcompa, author = {Syahaneim Marzukhi and Will N. Browne and Mengjie Zhang}, title = {Adaptive artificial datasets through learning classifier systems for classification tasks}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1243--1250}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466805}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In existing artificial classification systems, the problem domain is created and controlled by humans. Humans set up and tune the problem domain, such as determining the problem's complexity. If humans can set up the problem appropriately then the machines can extract beneficial knowledge to solve classification task. This paper introduces an autonomous classification problem generation approach. The classification problem's difficulty is adapted based on the classification agent's performance within the defined attributes. An automated problem generator has been created to evolve the simulated datasets whilst the classification agent, in this case a learning classifier system, attempts to learn the evolving problem. The idea here is to tune the datasets autonomously such that the problem characteristics may be determined efficiently to empirically test the learning bounds of the classification agent by lowering human involvement. In this way, the effect of the problem's characteristics, which alter the classification agent's performance, becomes human readable. Tabu Search has been applied in the problem generator to discover the best combination of domain features in order to adjust the problem's complexity. Experiments confirm that the problem generator was able to tune the problem's complexity either to make the problem 'harder' or 'easier' so that it can either 'increase' or 'decrease' the classification agent's performance.}, notes = {Also known as \cite{2466805} Distributed at GECCO-2013.}, } @inproceedings{Debie:2013:GECCOcomp, author = {Essam Soliman Debie and Kamran Shafi and Chris Lokan}, title = {REUCS-CRG: reduct based ensemble of sUpervised classifier system with combinatorial rule generation for data mining}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1251--1258}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482703}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes REUCS-CRG, a Reduct-based Ensemble of sUpervised Learning Classifier Systems with Combinatorial Rule Generation, which is an extension to the classical sUpervised Classifier System (UCS). In REUCS-CRG we build a two-stage ensemble architecture to improve generalisation in UCS. In the first-stage, rough set attribute reduction is used to generate a set of reducts with different attribute subspaces, and then a diverse subset of these reducts is selected to train an ensemble of base classifiers. New instances are sent to several UCS-CRGs for classification, which includes a combinatorial rule searching component based on differential evolution algorithm. In the second-stage, a fusion method is used to combine the classification results of individual UCS-CRGs into a final decision. Three combining method are used and their results are compared: simple majority voting, winner-takes-all, and median rule. Experiments on some benchmark data sets from the UCI repository have shown that REUCS-CRG has better performance and better generalisation ability than the single UCS and other UCS extensions. It also produces comparable results with other supervised learning methods. The experiments did not show significant differences in the accuracy rates obtained by the three combination methods.}, notes = {Also known as \cite{2482703} Distributed at GECCO-2013.}, } @inproceedings{Rudd:2013:GECCOcomp, author = {James Rudd and Jason Moore and Ryan Urbanowicz}, title = {A simple multi-core parallelization strategy for learning classifier system evaluation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1259--1266}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482704}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Permutation strategies for statistically evaluating the significance of predictions and patterns identified within learning classifier systems (LCSs) have only appeared since 2012. While already considered to be computationally expensive algorithms, a permutation testing based approach to determining statistical significance has the potential to be many times more demanding. One area of LCS research which has become both feasible and popularised in recent years is the adoption of parallelisation strategies. In the present study we explore the simple benefits of parallelizing a set of LCS analyses in an attempt to make the completion of a permutation test with cross validation more feasible on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that on Windows 7 computers, as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear.}, notes = {Also known as \cite{2482704} Distributed at GECCO-2013.}, } @inproceedings{Hemberg:2013:GECCOcompa, author = {Erik Hemberg and Kalyan Veeramachaneni and Franck Dernoncourt and Mark Wagy and Una-May O'Reilly}, title = {Efficient training set use for blood pressure prediction in a large scale learning classifier system}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1267--1274}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482705}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We define a machine learning problem to forecast arterial blood pressure. Our goal is to solve this problem with a large scale learning classifier system. Because learning classifiers systems are extremely computationally intensive and this problem's eventually large training set will be very costly to execute, we address how to use less of the training set while not negatively impacting learning accuracy. Our approach is to allow competition among solutions which have not been evaluated on the entire training set. The best of these solutions are then evaluated on more of the training set while their offspring start off being evaluated on less of the training set. To keep selection fair, we divide competing solutions according to how many training examples they have been tested on.}, notes = {Also known as \cite{2482705} Distributed at GECCO-2013.}, } @inproceedings{Lin:2013:GECCOcompa, author = {Hsuan-Ta Lin and Po-Ming Lee and Tzu-Chien Hsiao}, title = {The subsumption mechanism for XCS using code fragmented conditions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1275--1282}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482706}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {By using a code-fragmented representation of Extended Classifier System (XCS) condition in conjunction with building-block extraction technique, autonomous scaling has been realised in the latest work of XCS. The technique substantially reduces the number of training instances required in various benchmark problems. However, the subsumption mechanism was not included in the former report of the technique. Therefore, we invented the subsumption mechanism for XCS with such technique, and observed the characteristics of such the system in multiplexer problems. The finding indicates that our subsumption mechanism decreased the number of macro-classifiers.}, notes = {Also known as \cite{2482706} Distributed at GECCO-2013.}, } @inproceedings{Elyasaf:2013:GECCOcomp, author = {Achiya Elyasaf and Moshe Sipper}, title = {HH-evolver: a system for domain-specific, hyper-heuristic evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1285--1292}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482707}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can be used with combinatorial search algorithms such as A* and IDA* for solving problems of a given domain. HH-Evolver has a rich GUI that enables easy operation, including: running experiments in parallel, pausing and resuming experiments, and saving them and analysing the results. Implementing new domains and heuristics with HH-Evolver is easily accomplished.}, notes = {Also known as \cite{2482707} Distributed at GECCO-2013.}, } @inproceedings{Dinis:2013:GECCOcomp, author = {Rafael Dinis and Sim\, {o}es, Anabela and Bernardino, Jorge}, title = {GraphEA: a 3D educational tool for genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1293--1300}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482708}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {During the last decades Genetic Algorithms (GAs) have proved to be a powerful technique for solving difficult problems. Consequently, GA courses are becoming increasingly common in universities. The laboratory classes of such courses are crucial for students to consolidate and apply the concepts learnt in theoretical classes. However, it is required a lot of programming effort and sometimes students tend to have difficulties on this part, either because the number of different GA variants they have to implement or even because the lack of programming skills. To overcome this problem we present a new educational tool for GAs called GraphEA. This tool aims to help students to learn GAs without the need of programming effort, offering novel features like the 3D visualisation of the chromosome formation process and the online modification of problem data. In this paper we demonstrate three well-known optimisation problems implemented on the tool, namely the Knapsack Problem, the Traveling Salesman Problem, and the Function Optimization Problem.}, notes = {Also known as \cite{2482708} Distributed at GECCO-2013.}, } @inproceedings{Garcia-Valdez:2013:GECCOcomp, author = {Mario Garc\'{\i}a-Valdez and Juan J. Merelo and Leonardo Trujillo and Francisco Fern\'{a}ndez-de-Vega and Jos\'{e} C. Romero and Alejandra Mancilla}, title = {EvoSpace-i: a framework for interactive evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1301--1308}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482709}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary art (EvoArt) encompasses a variety of research devoted to the development of evolutionary systems that can help produce artistic artifacts in an automated or semi-automated process. Given the difficulty of evaluating subjective artistic preferences, one of the main approaches used by EvoArt researchers is interactive evolution where user input guides the search. However, despite the growth of EvoArt over recent years the research area still lacks a comprehensive software tool that can help in the development of EvoArt applications. Therefore, this work presents EvoSpace-i, an open source framework for the development of collaborative-interactive evolutionary algorithms for art and design. The main components of the framework are: (i) Evospace, a population store for the development of cloud-based evolutionary algorithms, implemented using Re-dis key-value server; and an (ii) Interactive web application where end-users collaborate in a social network sharing, collecting, rating and ultimately evolving individuals. Individuals can be presented as multimedia elements or artistic artifacts (images, animations, sound) using the Processing programming language, a development language specifically aimed at artists. EvoSpace-i is designed to be easy to use and setup, allowing researchers, and more importantly artists, to quickly develop distributed and collaborative EvoArt applications. This paper presents the main details of EvoSpace-i and two example applications to illustrate the potential of the tool.}, notes = {Also known as \cite{2482709} Distributed at GECCO-2013.}, } @inproceedings{Vaseux:2013:GECCOcomp, author = {Lo\"{\i}c Vaseux and Fernando E.B. Otero and Tom Castle and Colin G. Johnson}, title = {Event-based graphical monitoring in the EpochX genetic programming framework}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1309--1316}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482710}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {EpochX is a genetic programming framework with provision for event management - similar to the Java event model - allowing the notification of particular actions during the life cycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX genetic programming framework, taking full advantage of EpochX's event management. A set of representation-independent and tree-dependent GUI components are presented, showing how statistic information can be presented in a rich format using the information provided by EpochX's Stats system.}, notes = {Also known as \cite{2482710} Distributed at GECCO-2013.}, } @inproceedings{Cora:2013:GECCOcomp, author = {Hilal Kevser Cora and H. Turgut Uyar and A. \c{S}ima Etaner-Uyar}, title = {HH-DSL: a domain specific language for selection hyper-heuristics}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1317--1324}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482711}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A domain specific language (DSL) is a programming language which provides a natural notation and suitable data structures to express solutions to problems of a targeted domain. Although using a general purpose programming language together with a special library for the domain is common practice, it still requires a considerable amount of programming knowledge, making it hard for domain experts who might have limited or no programming skills. In the CHeSC (Cross-domain Heuristic Search Challenge) competition, researchers and practitioners from different research fields use the HyFlex platform to develop hyper-heuristics. The domain specific language proposed in this study aims to help these researchers to focus on hyper-heuristic development rather than the details of Java programming.}, notes = {Also known as \cite{2482711} Distributed at GECCO-2013.}, } @inproceedings{Rojas-Galeano:2013:GECCOcomp, author = {Sergio Rojas-Galeano and Nestor Rodriguez}, title = {Goldenberry: EDA visual programming in orange}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1325--1332}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482712}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Orange is an open-source component-based software framework, featuring visual and scripting interfaces for many machine learning algorithms. Currently it does not support Estimation of Distribution Algorithms (EDA) or other methods for black-box optimisation. Here we introduce Goldenberry, an Orange toolbox of EDA visual components for stochastic search-based optimization. Its main purpose is to provide an user-friendly workbench for researchers and practitioners, building upon the versatile visual front-end of Orange, and the powerful reuse and glue principles of component-based software development. Architecture of the toolbox and implementation details are given, including description and working examples for the components included in its first release: cGA, UMDA, PBIL, TILDA, UMDAc, PBILc, BMDA, Cost Function Builder and Black Box Tester. Goldenberry is open-source and freely available at: http://goldenberry.codeplex.com.}, notes = {Also known as \cite{2482712} Distributed at GECCO-2013.}, } @inproceedings{Kronberger:2013:GECCOcomp, author = {Gabriel Kronberger and Michael Kommenda and Stefan Wagner and Heinz Dobler}, title = {GPDL: a framework-independent problem definition language for grammar-guided genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1333--1340}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482713}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Defining custom problem types in genetic programming (GP) software systems is a tedious task that usually involves the implementation of custom classes and methods including framework-specific code. Users who want to solve a custom problem have to know the details of the targeted framework, for instance cloning semantics, and often have to write a lot of boilerplate code in order to implement the necessary functionality correctly. This can lead to frustration and hinders new developments and the application of GP to solve interesting problems. In this contribution we propose a framework-independent definition language for GP problems that can reduce the required effort and facilitate the integration of new problem types. We draw a parallel between the implementation of compilers for programming languages and the implementation of GP problems and reuse the well-established concept of attributed grammars with semantic actions to define computational symbols, semantics and structural constraints for GP. This goes beyond previous work in the area of context-free-grammar GP and grammatical evolution, because we also interweave the definition of symbol semantics and the target function with the definition of the grammar. This paper describes the proposed GP problem definition language (GPDL) and exemplary definitions of two popular benchmark problems using GPDL. We also describe a reference implementation of a GPDL compiler for HeuristicLab.}, notes = {Also known as \cite{2482713} Distributed at GECCO-2013.}, } @inproceedings{Garcia-Sanchez:2013:GECCOcomp, author = {Pablo Garc\'{\i}a-S\'{a}nchez and Mar\'{\i}a Isabel Garc\'{\i}a Arenas and Antonio Miguel Mora and Pedro \'{A}ngel Castillo and Carlos Fernandes and Paloma de las Cuevas and Gustavo Romero and Jes\'{u}s Gonz\'{a}lez and Juan Juli\'{a}n Merelo}, title = {Developing services in a service oriented architecture for evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1341--1348}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466806}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper shows the design and implementation of services for Evolutionary Computation following the Service Oriented Architecture paradigm. This paradigm allows independence over language and distribution mechanism. This development is challenging because some technological and design issues, such as abstract design or unordered execution. To solve them, OSGiLiath, an implementation of an abstract Service Oriented Architecture for Evolutionary Algorithms, is used to develop new interoperable services taking into account these restrictions.}, notes = {Also known as \cite{2466806} Distributed at GECCO-2013.}, } @inproceedings{Burlacu:2013:GECCOcomp, author = {Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan Winkler and Gabriel Kronberger}, title = {Visualization of genetic lineages and inheritance information in genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1351--1358}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482714}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many studies emphasise the importance of genetic diversity and the need for an appropriate tuning of selection pressure in genetic programming. Additional important aspects are the performance and effects of the genetic operators (crossover and mutation) on the transfer and stabilisation of inherited information blocks during the run of the algorithm. In this context, different ideas about the usage of lineage and genealogical information for improving genetic programming have taken shape in the last decade. Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent sub-trees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behaviour of the evolutionary process. The visualisation part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem.}, notes = {Also known as \cite{2482714} Distributed at GECCO-2013.}, } @inproceedings{Moshaiov:2013:GECCOcomp, author = {Amiram Moshaiov and Yevgeny Rizakov}, title = {Signaling and visualization for interactive evolutionary search and selection of conceptual solutions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1359--1366}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482715}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper deals with the problem of selecting and ordering a pre-defined number of conceptual solutions (concepts) out of a given finite set of candidate concepts. The process involves human intervention which allows the inclusion of un-modeled considerations, as well as the saving of computational resources. To support human intervention special visualisation elements are developed. The proposed interactive method is demonstrated on a path planning problem using the method of Evolution Strategies.}, notes = {Also known as \cite{2482715} Distributed at GECCO-2013.}, } @inproceedings{Tusvsar:2013:GECCOcomp, author = {Tea Tus\v{s}ar and Bogdan Filipi\v{c}}, title = {An approach to visualizing the 3D empirical attainment function}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1367--1372}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482716}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When analysing the performance of a bi-objective optimisation algorithm, the empirical attainment function (EAF) is often used to visualise the attained parts of the objective space. Similarly, when comparing two algorithms, the differences in EAF values can be used to show the parts of the objective space in which the first algorithm outperforms the second one, and vice versa. This paper proposes to visualise the EAF values and differences also when assessing algorithms that optimise three criteria. This can be achieved by cutting through the 3D EAFs using multiple cutting planes and presenting the resulting intersections in 2D. The approach is described in detail and demonstrated on two artificial Pareto front approximations.}, notes = {Also known as \cite{2482716} Distributed at GECCO-2013.}, } @inproceedings{Cancino:2013:GECCOcomp, author = {Waldo Cancino and Nadia Boukhelifa and Anastasia Bezerianos and Evelyne Lutton}, title = {Evolutionary visual exploration: experimental analysis of algorithm behaviour}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1373--1380}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482717}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent publications in the domains of interactive evolutionary computation and data visualisation consider an emerging topic coined Evolutionary Visual Exploration (EVE). EVE systems combine visual analytics with stochastic optimisation to aid the exploration of complex, multidimensional datasets. In this work we present an experimental analysis of the behaviour of an EVE system that is dedicated to the visualisation of multidimensional datasets, which are generally characterised by a large number of possible views or projections. EvoGraphDice is an interactive evolutionary system that progressively evolves a small set of new dimensions, to provide new viewpoints on the dataset, in the form of linear and non-linear combinations of the original dimensions. The criteria for evolving new dimensions are not known a priori and are partially specified by the user via an interactive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function to take into account the user input, and then calculates new views, with the help of an evolutionary engine. In previous work (an observational study), we showed that EvoGraphDice was able to facilitate exploration tasks, helping users to discover new interesting views and relationships in their data. Here, we focus on the system's convergence behaviour, conducting an experiment with users who have a precise task to perform. The experimental task is set up as a geometrical game, and collected data show that EvoGraphDice is able to learn user preferences in a way that helps users fulfil their task (i.e. converge to desired solutions).}, notes = {Also known as \cite{2482717} Distributed at GECCO-2013.}, } @inproceedings{Headleand:2013:GECCOcomp, author = {Christopher James Headleand and William J. Teahan}, title = {Template based evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1383--1390}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482718}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a novel approach to multi-agent simulation where agents evolve freely within their environment. We present Template Based Evolution (TBE), a genetic evolution algorithm that evolves behaviour for embodied situated agents whose fitness is tested implicitly through repeated trials in an environment. All agents that survive in the environment breed freely, creating new agents based on the average genome of two parents. This paper describes the design of the algorithm and applies it to a model where virtual migratory creatures are evolved to survive the simulated environment. Comparisons made between the evolutionary responses of the artificial creatures and observations of natural systems justify the strength of the methodology for species simulation.}, notes = {Also known as \cite{2482718} Distributed at GECCO-2013.}, } @inproceedings{Chalupa:2013:GECCOcomp, author = {David Chalupa}, title = {Adaptation of a multiagent evolutionary algorithm to NK landscapes}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1391--1398}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482719}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multiagent evolutionary algorithm (MEA) is a relatively new optimisation technique, where a life cycle of a population of agents, which perform local search, is simulated. The algorithm was originally intended as a method for solving the graph colouring problem and incorporates ideas such as lifespans of agents and a positive or negative reinforcement for the ability of the agent to improve fitness or its stagnation. In this paper, we propose to use MEA for optimisation on NK fitness landscapes. These landscapes are popular for the tunability of their ruggedness and are a particularly interesting use case for MEA. This algorithm is especially well suited for functions, where local search tends to fail because of their multimodality. However, using many short-term local search subroutines in a well-tuned version of MEA can significantly improve the results of the same local search algorithm. Experimental results are presented for MEA with the simple (1+1) Evolutionary Algorithm ((1+1) EA) used as a local search subroutine. These results show that in large and more rugged NK landscapes, MEA outperforms the multi-start (1+1) EA with number of parallel starts equal to the initial population size of MEA. This is the first time we obtained results, which clearly indicate that solely the emergent multiagent nature of MEA, driven by the lifespans and the reinforcement mechanism, is able to improve the results of multi-start local search.}, notes = {Also known as \cite{2482719} Distributed at GECCO-2013.}, } @inproceedings{Sack:2013:GECCOcomp, author = {Graham Alexander Sack and Daniel Wu and Benji Zusman}, title = {Simulating the cultural evolution of literary genres}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1399--1406}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482720}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The purpose of this paper is to explore the evolutionary dynamics of literary genre: the development of the 19th Century British novel is used as a motivating case study. The author constructs an agent-based model in NetLogo consisting of two interacting levels: (1) a genetic algorithm in which cultural forms (e.g., works of literature, pieces of music, etc.) are represented as binary feature strings. Cultural forms evolve across generations via asexual and sexual reproduction. Genres are represented as hierarchical clusters of similar feature strings. (2) Cultural forms are subjected to the selection pressure of consumer preferences. Preferences are heterogeneous: each consumer's tastes are represented by an ideal point in feature space. Preferences are configured in landscapes that vary in their levels of structure, entropy, and diversity. Landscapes are dynamic and may change due to (i) exogenous demographic shifts (e.g., population growth, generational turnover) or (ii) endogenous feedback (e.g., conformity / anti-conformity effects).}, notes = {Also known as \cite{2482720} Distributed at GECCO-2013.}, } @inproceedings{Merkel:2013:GECCOcomp, author = {Sabrina Merkel and Patrick Unger and Hartmut Schmeck}, title = {Evolutionary algorithm for optimal anchor node placement to localize devices in a mobile ad hoc network during building evacuation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1407--1414}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466807}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Using mobile devices to support the evacuation of a building is a relatively new and promising research field. An essential requirement to realise this endeavour is to be able to track the location of the mobile devices. Since GPS is generally not available in buildings, alternative localisation methods, i.e. methods to determine the devices' locations, need to be used. Many of these alternative localisation algorithms use a small number of so called anchor nodes which are assumed to know their positions to derive the locations of all other devices in the network. The placement of these anchor nodes is essential to the accuracy of the derived locations and has, so far, been mainly studied for static networks. Mobile networks pose different challenges, especially when used for evacuation support, where devices are simultaneously moved towards the exits of a building. Here, we present an Evolutionary Algorithm in combination with a multi-agent simulation to optimise the placement of anchor nodes in order to localise devices during evacuation. It is shown that the proposed Evolutionary Algorithm is a suitable instrument to find a good placement and some essential criteria for such a placement are identified.}, notes = {Also known as \cite{2466807} Distributed at GECCO-2013.}, } @inproceedings{Godoy:2013:GECCOcomp, author = {Alan Godoy and Fernando J. Von Zuben}, title = {Topology of social networks and efficiency of collective intelligence methods}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1415--1422}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482721}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work we analysed the role social networks play in the efficiency of collective problem-solving, evaluating whether the topological characteristics seen in real-world networks yield any performance improvement in such processes. To study this we used the Particle Swarm Optimisation as a testbed for social groups performing a collective task, defining the structure of communication between individuals in the swarm through topologies generated by a model for the creation and evolution of social networks. The experimental results indicate that groups using these networks may, indeed, experience better performance in collective problem-solving, so that these groups were able to overcome the results achieved by swarms using classical neighbourhoods for PSO and reached results very close to those found by swarms using the topology of DMS-PSO, usually considered to be part of the state-of-the-art of Particle Swarm Optimization.}, notes = {Also known as \cite{2482721} Distributed at GECCO-2013.}, } @inproceedings{Grappiolo:2013:GECCOcompa, author = {Corrado Grappiolo and Julian Togelius and Georgios N. Yannakakis}, title = {Interaction-based group identity detection via reinforcement learning and artificial evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1423--1430}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482722}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a computational framework capable of inferring the existence of group identities, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial Societies (CAAS) by solely observing the flow of interactions occurring among the agents. Our modelling framework infers the group identities by following two steps: first, it aims to learn the ongoing levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learnt cooperation values, to partition the agents into groups and assign group identities to the agents. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum (or Bargain) Social Dilemma Game, show that a cooperation learning phase, based on Reinforcement Learning, can provide highly promising results for minimising the mismatch between the existing and the inferred group identities. The proposed method appears to be robust independently of the size and the ongoing social dynamics of the societies.}, notes = {Also known as \cite{2482722} Distributed at GECCO-2013.}, } @inproceedings{Duong:2013:GECCOcomp, author = {Deborah Duong and Jerry Pearman and Christopher Bladon}, title = {The nexus cognitive agent-based model: coevolution for valid computational social modeling}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1431--1436}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482723}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper discusses the central problems with creating valid computational social science simulations, and then suggests answers to these problems that involve co-evolution, autonomy, interpretation, and data processing under uncertainty. We also present the use of these techniques in the Nexus cognitive agent simulation, used at the US Department of Defense (DoD) in multiple major analyses of Irregular Warfare. We introduce the technique of data absorption, which leverages co-evolutionary pressure to reproduce the same dynamic structures that caused observable real word data in the simulation through the motivations of the agents. This technique gives a causal explanation for the data, and sets the stage for testing the effects of interventions never seen by the system on the system. By mimicking the state of equilibrium reached by the natural system, data absorption closes the gap between theory-centric simulations and data centric simulations.}, notes = {Also known as \cite{2482723} Distributed at GECCO-2013.}, } @inproceedings{Hecker:2013:GECCOcomp, author = {Joshua Peter Hecker and Melanie E. Moses}, title = {An evolutionary approach for robust adaptation of robot behavior to sensor error}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1437--1444}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482724}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms can adapt the behaviour of individual agents to maximise the fitness of populations of agents. We use a genetic algorithm (GA) to optimise behaviour in a team of simulated robots that mimic foraging ants. We introduce positional and resource detection error models into this simulation, emulating the sensor error characterised by our physical iAnt robot platform. Increased positional error and detection error both decrease resource collection rates. However, they have different effects on GA behaviour. Positional error causes the GA to reduce time spent searching for local resources and to reduce the likelihood of returning to locations where resources were previously found. Detection error causes the GA to select for more thorough local searching and a higher likelihood of communicating the location of found resources to other agents via pheromones. Agents that live in a world with error and use parameters evolved specifically for those worlds perform significantly better than agents in the same error-prone world using parameters evolved for an error-free world. This work demonstrates the utility of employing evolutionary methods to adapt robot behaviours that are robust to sensor errors.}, notes = {Also known as \cite{2482724} Distributed at GECCO-2013.}, } @inproceedings{Langdon:2013:GECCOcomp, author = {W. B. Langdon}, title = {Correlation of microarray probes give evidence for mycoplasma contamination in human studies}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1447--1454}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482725}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {At least 473 Affymetrix HG-U133 +2 Homosapiens probes match one or more species of mycoplasma. Analysis of published data from thousands of human GeneChips finds correlations in homo sapiens studies between different microbiology laboratories in different countries which suggests contamination with mycoplasma is the common factor. This high lights the problem of experts in evolutionary computation needing to apply due diligence before relying on public medical datasets. Caveat emptor even if the data are free!}, notes = {Also known as \cite{2482725} Distributed at GECCO-2013.}, } @inproceedings{Mesejo:2013:GECCOcomp, author = {Pablo Mesejo and Stefano Cagnoni and Alessandro Costalunga and Davide Valeriani}, title = {Segmentation of histological images using a metaheuristic-based level set approach}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1455--1462}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466808}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a two-phase method to segment the hippocampus in histological images. The first phase represents a training stage where, from a training set of manually labelled images, the hippocampus representative shape and texture are derived. The second one, the proper segmentation, uses a metaheuristic to evolve the contour of a geometric deformable model using region and texture information. Three different metaheuristics (real-coded GA, Particle Swarm Optimization and Differential Evolution) and two classical segmentation algorithms (Chan & Vese model and Geodesic Active Contours) were compared over a test set of 10 histological images. The best results were attained by the real-coded GA, achieving an average and median Dice Coefficient of 0.72 and 0.77, respectively.}, notes = {Also known as \cite{2466808} Distributed at GECCO-2013.}, } @inproceedings{Winkler:2013:GECCOcomp, author = {Stephan M. Winkler and Michael Affenzeller and Herbert Stekel}, title = {Evolutionary identification of cancer predictors using clustered data: a case study for breast cancer, melanoma, and cancer in the respiratory system}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1463--1470}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466809}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we discuss the effects of using pre-clustered data on the identification of estimation models for cancer diagnoses. Based on patients' data records including standard blood parameters, tumour markers, and information about the diagnosis of tumors, the goal is to identify mathematical models for estimating cancer diagnoses. We have applied a hybrid clustering and classification approach that first identifies data clusters (using standard patient data and tumor markers) and then learns prediction models on the basis of these data clusters. In the empirical section we analyse the clusters of patient data samples formed using k-means clustering: The optimal number of clusters is identified, and we investigate the homogeneity of these clusters. Several evolutionary modelling approaches implemented in HeuristicLab have been applied for subsequently identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbour learning, artificial neural networks, and support vector machines (all optimised using evolutionary algorithms) as well as genetic programming. As we show in the results section, the investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 84.2percent, 80.3percent, and 94.1percent of the analysed test cases, respectively; without tumour markers up to 78.2percent, 78percent, and 93.3percent of the test samples are correctly estimated, respectively.}, notes = {Also known as \cite{2466809} Distributed at GECCO-2013.}, } @inproceedings{Amelio:2013:GECCOcomp, author = {Alessia Amelio and Clara Pizzuti}, title = {Skin lesion image segmentation using a color genetic algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1471--1478}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466810}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The development of computer-aided diagnosis systems for skin cancer detection has attracted a lot of interest in the research community. In particular, the availability of an accurate automatic segmentation tool for detecting skin lesions from background skin is of primary importance for the overall diagnosis system. In this paper we investigate the capability of a colour image segmentation method based on Genetic Algorithms in discriminating skin lesions. Experimental results show that the segmentation approach is able to detect lesion borders quite accurately, thus coupled with a merging technique of the surrounding region could reveal a promising method for isolating skin tumour.}, notes = {Also known as \cite{2466810} Distributed at GECCO-2013.}, } @inproceedings{Lacy:2013:GECCOcomp, author = {Stuart E. Lacy and Michael A. Lones and Stephen L. Smith and Jane E. Alty and D.R. Stuart Jamieson and Katherine L. Possin and Norbert Schuff}, title = {Characterisation of movement disorder in parkinson's disease using evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1479--1486}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482726}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parkinson's Disease is a devastating illness with no currently available cure. As the population ages, the disease becomes more common with a large financial cost to society. A rapid and accurate diagnosis, as well as practical monitoring methods are essential for managing the disease as best as possible. This paper discusses two approaches to discriminating movement data between healthy controls or Parkinson's Disease patients. One is a standard statistical analysis, influenced by prior work into classifying patients. The other is a programmatic expression evolved using genetic programming, which is trained to observe differences in specific motion segments, rather than using arbitrary windows of a full data series. The performance of the statistical analysis method is relatively high, but it still cannot discriminate as well as the evolved classifier. This study compares favourably to previous work, highlighting the usefulness of analysing a successful classifier to influence design decisions for future work. Examination of the evolved programmatic expressions that had high discriminatory ability provided useful insight into how Parkinson's Disease patients and healthy subjects have differing movement characteristics. This could be used to inform future research into the physiology of repetitive motions in Parkinson's Disease patients.}, notes = {Also known as \cite{2482726} Distributed at GECCO-2013.}, } @inproceedings{Kamrath:2013:GECCOcomp, author = {Nathaniel R. Kamrath and Brian W. Goldman and Daniel R. Tauritz}, title = {Using supportive coevolution to evolve self-configuring crossover}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1489--1496}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482727}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Creating an Evolutionary Algorithm (EA) which is capable of automatically configuring itself and dynamically controlling its parameters is a challenging problem. However, solving this problem can reduce the amount of manual configuration required to implement an EA, allow the EA to be more adaptable, and produce better results on a range of problems without requiring problem specific tuning. Using Supportive Coevolution (SuCo) to evolve Self-Configuring Crossover (SCX) combines the automatic configuration technique of multiple populations from SuCo with the dynamic crossover operator creation and evolution of SCX. This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.}, notes = {Also known as \cite{2482727} Distributed at GECCO-2013.}, } @inproceedings{Martin:2013:GECCOcomp, author = {Matthew A. Martin and Daniel R. Tauritz}, title = {Evolving black-box search algorithms employing genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1497--1504}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482728}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifically tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can address, than a specialised BBSA. This paper introduces a novel approach to creating tailored BBSAs through automated design employing genetic programming. An experiment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs including canonical evolutionary algorithms.}, notes = {Also known as \cite{2482728} Distributed at GECCO-2013.}, } @inproceedings{deSa:2013:GECCOcomp, author = {Alex Guimar de S\'{a}\, {a}es Cardoso and Pappa, Gisele Lobo}, title = {Towards a method for automatically evolving bayesian network classifiers}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1505--1512}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482729}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When faced with a new machine learning problem, selecting which classifier is the best to perform the task at hand is a very hard problem. Most solutions proposed in the literature are based on meta-learning, and use meta-data about the problem to recommend an effective algorithm to solve the task. This paper proposes a new approach to this problem: to build an algorithm tailored to the application problem at hand. More specifically, we propose an evolutionary algorithm (EA) to automatically evolve Bayesian Network Classifiers (BNCs). The method receives as input a list of the main components of BNC algorithms, and uses an EA to encode these components. Given an input dataset, the method tests different combinations of components to that specific application domain. The method was tested in 10 UCI datasets, and compared to three classical BNCs and a greedy search algorithm. Results show that the current algorithms can indeed be improved, but that the EA is currently outperformed by the greedy search.}, notes = {Also known as \cite{2482729} Distributed at GECCO-2013.}, } @inproceedings{Ogur:2013:GECCOcomp, author = {Emin Ogur and Mehmet E. Aydin}, title = {Refining scheduling policies with genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1513--1518}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482730}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Algorithms (GAs) are popular approaches in solving various complex real-world problems. However, it is required that a careful attention is to be paid to the contextual knowledge as well as the implementation of genetic material and operators. On the other hand, the job-shop scheduling (JSS) problem remains as challenging NP-hard combinatorial problem, which attracts researchers since it is invented. The dynamic version of job-shop is even more challenging due to its dynamically changing characteristics. Similar to other metaheuristic approaches, GA has not been so successful in solving this sort of problems due to instant decision making process needed in solving this type of problems. Heuristic procedures such as those so called Priority Rule or Dispatching Rules are more useful for this purpose, but, depending on the properties and purpose of use of each, the same performance is not expected from these instant decision making operators. In this paper, a policy refinement approach is proposed to optimise a sequence of Dispatching Rules (DRs) for a time-window of scheduling process in which a GA algorithm evolves the sequences towards an optimum configuration. The preliminary results provided in this paper seem very encouraging.}, notes = {Also known as \cite{2482730} Distributed at GECCO-2013.}, } @inproceedings{Corne:2013:GECCOcomp, author = {David Corne and Alan Reynolds and Stuart Galloway and Edward Owens and Andrew Peacock}, title = {Short term wind speed forecasting with evolved neural networks}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1521--1528}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482731}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Concerns about climate change, energy security and the volatility of the price of fossil fuels has led to an increased demand for renewable energy. With wind turbines being one of the most mature renewable energy technologies available, the global use of wind power has been growing at over 20percent annually, with further adoption to be expected. As a result of the inherent variability of the wind in combination with the increased uptake, demand for accurate wind forecasting, over a wide range of time scales has also increased. We report early work as part of the EU FP7 project 'ORIGIN', which will exploit wind speed forecasting, and implement and evaluate smart-meter based energy management in 300 households in three ecovillages across Europe. The ORIGIN system will capitalise on automated weather station data (available cheaply) to inform predictions of the wind-turbine generated power that may be available in short term future time windows. Accurate and reliable wind-speed forecasting is essential in this enterprise. A range of different methods for wind forecasting have been developed, ranging from relatively simple time series analysis to the use of a combination of global weather forecasting, computational fluid dynamics and machine learning methods. Here we focus on the application of neural networks, without (for the time being) the use of numerical weather predictions or expensive physical modelling methods. While work of this nature has been performed before, using past wind speeds to make predictions into the future, here we explore the use of additional recent meteorological data to improve on short-term forecasting. Specifically, we employ evolved networks and explore many configurations to assess the merits of using additional features such as cloud cover, temperature and pressure, to predict future wind speed.}, notes = {Also known as \cite{2482731} Distributed at GECCO-2013.}, } @inproceedings{Hutterer:2013:GECCOcomp, author = {Stephan Hutterer and Stefan Vonolfen and Michael Affenzeller}, title = {Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1529--1536}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482732}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The optimal power flow (OPF) is one of the central Optimization problems in power grid engineering, building an essential tool for numerous control as well as planning issues. Methods for solving the OPF that mainly treat steady-state situations have been studied extensively, ignoring uncertainties of system variables as well as their volatile behaviour. While both the economical as well as well as technical importance of accurate control is high, especially for power flow control in dynamic and uncertain power systems, methods are needed that provide (near-) optimal actions quickly, eliminating issues on convergence speed or robustness of the Optimization. This paper shows an approximate policy-based control approach where optimal actions are derived from policies that are learnt offline, but that later provide quick and accurate control actions in volatile situations. These policies are evolved using genetic programming, where multiple and interdependent policies are learnt synchronously with simulation-based Optimization. Finally, an approach is available for learning fast and robust power flow control policies suitable to highly dynamic power systems such as smart electric grids.}, notes = {Also known as \cite{2482732} Distributed at GECCO-2013.}, } @inproceedings{Egarter:2013:GECCOcomp, author = {Dominik Egarter and Wilfried Elmenreich}, title = {EvoNILM: evolutionary appliance detection for miscellaneous household appliances}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1537--1544}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482733}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To improve the energy awareness of consumers, it is necessary to provide them with information about their energy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy demand of each individual appliances. In this paper we present an evolutionary Optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a probabilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented pre-processing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.}, notes = {Also known as \cite{2482733} Distributed at GECCO-2013.}, } @inproceedings{Dobson:2013:GECCOcomp, author = {Richard Dobson and Kathleen Steinh\"{o}fel}, title = {Sa based power efficient FPGA LUT mapping}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1545--1552}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482734}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Look up Table (LUT) based Field Programmable Gate Arrays (FPGAs) are commonly used in mobile devices due to their efficient signal processing capabilities and flexibility to be reprogrammed in situ. However the mechanisms which enable a FPGA to be re-programmable make it require more power than an Application Specific Integrated Circuit. In this paper we consider the power reduction of a FPGA by optimising the mapping the underlying Boolean circuit onto the LUT based FPGA with respect to cumulative switching. We formulate the power minimisation problem as a combinatorial optimisation problem. To tackle this NP hard problem we propose the application of a local search method. Here we introduce a complete a neighbourhood function and apply heuristic simulated annealing in conjunction with the objective function from [20] 'cumulative switching'. Our experimental results show a 42.96percent average reduction in power consumption compared to SIS based mapping and 27.44percent average reduction in power consumption compared to a genetic algorithm.}, notes = {Also known as \cite{2482734} Distributed at GECCO-2013.}, } @inproceedings{Chang:2013:GECCOcomp, author = {Xiaolin Chang and Bin Wang and Jiqiang Liu and Wenbo Wang and Jogesh Muppala}, title = {Green cloud virtual network provisioning based ant colony optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1553--1560}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482735}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Network virtualisation is being regarded as a promising technology to create an ecosystem for cloud computing applications. One critical issue in network virtualisation technology is power-efficient virtual network embedding (PE-VNE), which deals with the physical resource allocation to virtual nodes and links of a virtual network while minimising the energy consumption in the cloud data centre. When the node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VN embedding problem is NP-hard, even in the offline case. This paper aims to investigate the ability of the Ant-Colony-Optimization (ACO) technique in handling PE-VNE problem. We propose an ACO-based heuristic PE-VNE algorithm, called E-ACO. E-ACO minimises the energy consumption by considering the embedding power consumption in the node mapping phase and by making an implicit coordination between the node and link mapping phases. Extensive simulations are conducted to evaluate the performance of the proposed algorithm and investigate different energy-aware link embedding algorithms on the ability of E-ACO.}, notes = {Also known as \cite{2482735} Distributed at GECCO-2013.}, } @inproceedings{Rodrigues:2013:GECCOcomp, author = {S\'{\i}lvio Miguel Fragoso Rodrigues and Pavol Bauer and Jan Pierik}, title = {Modular approach for the optimal wind turbine micro siting problem through CMA-ES algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1561--1568}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482736}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Although, only in recent years, northern European countries started to install large offshore wind farms, it is expected that by 2020, several dozens of far and large offshore wind farms (FLOWFs) will be built in the Baltic, Irish and North seas. These FLOWFs will be constituted of a considerable amount of wind turbines (WTs) packed together, leading to an energy density increase. However, due to shadowing effects between WTs, power production is reduced, resulting in a revenues decrease. Therefore, when FLOWFs are considered, wake losses reduction is an important optimisation goal. This work presents a modular approach to optimise the energy yield of FLOWFs through an evolutionary algorithm. In order to do so the algorithm is set to find an optimal WF layout. The method consists of a modular strategy where the site wind rose information is used in different steps, which accelerates the calculation speed of the wake losses. The results presented demonstrate the method effectiveness. A computational time decrease is observed when compared to the standard optimisation strategy, without jeopardising the quality of the optimal layouts achieved.}, notes = {Also known as \cite{2482736} Distributed at GECCO-2013.}, } @inproceedings{Gonzalez-Alvarez:2013:GECCOcomp, author = {David L. Gonz\'{a}lez-\'{A}lvarez and Miguel A. Vega-Rodr\'{\i}guez}, title = {Designing a novel hybrid swarm based multiobjective evolutionary algorithm for finding DNA motifs}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1571--1578}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482738}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present a novel local search for improving the ability of multiobjective evolutionary algorithms when finding repeated patterns -motifs- in DNA sequences. In the metaheuristic design, two competing goals must be taken into account: exploration and exploitation. Exploration is needed to cover most of the optimisation problem search space and provide a reliable estimation of the global optimum. In turn, exploitation is also important since normally the solutions refinement allows the achievement of better results. In this work we take advantage of both concepts by combining the exploration capabilities of a population-based evolutionary algorithm and the power of a local search, especially designed to optimise the Motif Discovery Problem (MDP). For doing this, we have implemented a new hybrid multiobjective metaheuristic based on Artificial Bee Colony (ABC). After analysing the results achieved by this algorithm, named Hybrid-MOABC (H-MOABC), and comparing them with those achieved by three multiobjective evolutionary algorithms and thirteen well-known biological tools, we prove that the hybridisation computes accurate biological predictions on real genetic instances in an optimum way. In fact, to the best of our knowledge, the results presented in this paper improve those presented in the literature.}, notes = {Also known as \cite{2482738} Distributed at GECCO-2013.}, } @inproceedings{To:2013:GECCOcomp, author = {Cuong To and Mohamed Elati}, title = {A parallel genetic programming for single class classification}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1579--1586}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466811}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present an algorithm based on genetic programming for single (one) class classification that uses one set containing similar patterns in training process. This type of problem is called single (one) class classification, a novel detection. The proposed algorithm was tested and compared to seven other traditional methods based on two publicly available transcriptomic and proteomic time series datasets and two public breast cancer datasets. The results show that the algorithm could find most similar patterns in the databases with rather low misclassification rates. We also applied parallel genetic programming for this algorithm and it proves that the island model can give better solutions than sequential genetic programming.}, notes = {Also known as \cite{2466811} Distributed at GECCO-2013.}, } @inproceedings{Santander-Jimenez:2013:GECCOcomp, author = {Sergio Santander-Jim\'{e}nez and Miguel A. Vega-Rodr\'{\i}guez}, title = {A comparative study on distance methods applied to a multiobjective firefly algorithm for phylogenetic inference}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1587--1594}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482739}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Throughout the years, researchers have reported a wide variety of proposals to infer evolutionary histories from biological data. Recent studies suggested the use of matrices of genetic distances to represent phylogenetic topologies in population-based metaheuristics. A key question that must be addressed is the choice of a particular method to build phylogenies from evolutionary distances. In addition to this, there is a growing need to overcome the problems that arise when different optimality criteria describe conflicting hypotheses about the evolution of the input species. In this paper, we tackle the phylogenetic inference problem by using a multiobjective algorithm with matrix representation inspired by the bioluminescence of fireflies. Our main goal is to study the behaviour of several clustering and neighbour-joining methods applied to infer phylogenies from the distance matrices processed by our algorithm. Experimental results on four real nucleotide data sets point out the advantages and disadvantages of each proposal, in terms of multiobjective performance and processing times.}, notes = {Also known as \cite{2482739} Distributed at GECCO-2013.}, } @inproceedings{Santos:2013:GECCOcomp, author = {Jos\'{e} Santos and Pablo Villot and Mart\'{\i}n Di\'{e}guez}, title = {Protein folding with cellular automata in the 3D HP model}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1595--1602}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466812}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the difficult ab initio prediction in protein folding only the information of the primary structure of amino acids is used to determine the final folded conformation. The complexity of the interactions and the nature of the amino acid elements are reduced with the use of lattice models like HP, which categorises the amino acids regarding their hydrophobicity. On the contrary to the intense research performed on the direct prediction of the final folded conformation, our aim here is to model the dynamic and emergent folding process through time, using the scheme of cellular automata but implemented with artificial neural networks optimised with Differential Evolution. Moreover, as the iterative folding also provides the final folded conformation, we can compare the results with those from direct prediction methods of the final protein conformation.}, notes = {Also known as \cite{2466812} Distributed at GECCO-2013.}, } @inproceedings{Shatnawi:2013:GECCOcomp, author = {Maad Shatnawi and Nazar Zaki}, title = {Prediction of protein inter-domain linkers using compositional index and simulated annealing}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1603--1608}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482740}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Protein chains are typically large and consist of multiple domains which are difficult and computationally expensive to characterise using experimental methods. Therefore, accurate and reliable prediction of protein domain boundaries is often the initial step in both experimental and computational protein research. In this paper, we propose a straightforward yet effective method to predict inter-domain linker segments by using the amino acid compositional index from the amino acid sequence information. Each amino acid in the protein sequence is represented by a compositional index which is deduced from a combination of the difference in amino acid occurrences in domains and linker segments in training protein sequences and the amino acid composition information. Further, we employ simulated annealing to improve the prediction by finding the optimal set of threshold values that separate domains from inter-domain linkers. The performance of the proposed method is compared to the current approaches on two protein sequence datasets. Experimental results show superior performance by the proposed method when compared to the state-of-the-art methods for inter-domain linker prediction.}, notes = {Also known as \cite{2482740} Distributed at GECCO-2013.}, } @inproceedings{Aslanov:2013:GECCOcomp, author = {Jeyhun Aslanov and B\"{u}lent \c{C}atay and Mehmet Serkan Apaydin}, title = {An ant colony optimization approach for solving the nuclear magnetic resonance structure based assignment problem}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1609--1616}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482741}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids. Structure Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach NVR-BIP computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-TS extended the applicability of the NVR approach for such proteins, however the accuracies varied significantly from run to run. In this paper, we propose NVR-ACO, an Ant Colony Optimization (ACO) based approach to this problem. NVRACO is similar to other ACO algorithms in a way that it also consists of three phases: the construction phase, an optional local search phase and a pheromone update phase. But it has some important differences from other ACO algorithms in terms of solution construction and pheromone update functions and convergence rules. We studied the data set used in NVR-BIP and NVR-TS. Our new method finds optimal solutions for small proteins and achieves perfect assignment on EIN and higher accuracy on MBP compared to NVR-TS. It is also more robust.}, notes = {Also known as \cite{2482741} Distributed at GECCO-2013.}, } @inproceedings{Chaves-Gonzalez:2013:GECCOcomp, author = {Jose M. M. Chaves-Gonz\'{a}lez and Miguel A. A. Vega-Rodr\'{\i}guez}, title = {DNA base-code generation for reliable computing by using standard multi-objective evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1617--1624}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466813}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificially generated DNA strands have to meet several complex bio-chemical constraints when they are used to solve any computational problem. In this context, DNA sequences have to satisfy several design criteria to prevent DNA strands from producing undesirable reactions which usually lead to incorrect computations. This study is focused on six different design criteria that ensure the reliability and efficiency of the operations performed with the generated DNA sequences. We have formulated DNA base-code generation as a multiobjective optimisation problem in which there is not only a unique optimal solution, but a Pareto set of high-quality solutions. Reliable DNA sequences have been generated by using two well-known multiobjective approaches: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA 2). We have performed experiments with three different-sized realistic datasets. Results show that the multiobjective algorithms developed are very appropriate for our problem, especially NSGA-II, which provides more reliable DNA sequences than other relevant approaches previously published in the literature.}, notes = {Also known as \cite{2466813} Distributed at GECCO-2013.}, } @inproceedings{Helmuth:2013:GECCOcomp, author = {Thomas Helmuth and Lee Spector}, title = {Evolving a digital multiplier with the pushgp genetic programming system}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1627--1634}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466814}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A recent article on benchmark problems for genetic programming suggested that researchers focus attention on the digital multiplier problem, also known as the multiple output multiplier problem, in part because it is scalable and in part because the requirement of multiple outputs presents challenges for some forms of genetic programming [20]. Here we demonstrate the application of stack-based genetic programming to the digital multiplier problem using the PushGP genetic programming system, which evolves programs expressed in the stack-based Push programming language. We demonstrate the use of output instructions and argue that they provide a natural mechanism for producing multiple outputs in a stack-based genetic programming context. We also show how two recent developments in PushGP dramatically improve the performance of the system on the digital multiplier problem. These developments are the ULTRA genetic operator, which produces offspring via Uniform Linear Transformation with Repair and Alternation [12], and lexicase selection, which selects parents according to performance on cases considered sequentially in random order [11]. Our results using these techniques show not only their utility, but also the utility of the digital multiplier problem as a benchmark problem for genetic programming research. The results also demonstrate the exibility of stack-based genetic programming for solving problems with multiple outputs and for serving as a platform for experimentation with new genetic programming techniques.}, notes = {Also known as \cite{2466814} Distributed at GECCO-2013.}, } @inproceedings{Keijzer:2013:GECCOcomp, author = {Maarten Keijzer}, title = {Push-forth: a light-weight, strongly-typed, stack-based genetic programming language}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1635--1640}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482742}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper defines the push-forth language, a recombination of Push [3] and Joy [7], borrowing type-safety considerations from Alp [2]. Push-forth is stack-based, strongly typed and easy to extend. The concept of an Evolutionary Development Environment is presented, and some informal experiments are described to illustrate the utility of such an environment.}, notes = {Also known as \cite{2482742} Distributed at GECCO-2013.}, } @inproceedings{Akhtar:2013:GECCOcomp, author = {Junaid Akhtar and Mian Awais and Basit Koshul}, title = {An evolutionary algorithm derived from Charles Sanders Peirce's theory of universal evolution}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1643--1646}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482743}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Historically, Evolutionary Algorithms (EAs) have been important for Evolutionary Computation (EC) community for two reasons: 1) As a simulation of evolutionary processes as they happen in nature, and 2) as a solution to hard optimisation problems. With the passage of time EAs have become increasingly focused on function optimization. Given this narrowing of vision in the EC community, it is worth revisiting a paper written in 1997 by Hans-Paul Schwefel on the future challenges for EC. In that paper the author argues that the more an algorithm models natural evolution at work in the universe, the better it will perform (even in terms of function optimisation). The present paper tests Schwefel's hypothesis by designing an EA based on Charles Peirce's theory of evolution. Peirce's theory not only accounts for biological evolution on earth (as other theories of evolution do) but also offers an account of global, cosmological and universal evolution. In going beyond just biological evolution, Peirce's theory of evolution meets the criteria suggested by Schewefel in his 1997 paper. The present paper mainly contributes in testing the Peircean EA on an extended set of benchmark optimisation functions and compares the results with a classical EA that is based on Darwin's theory of evolution. In majority of these comparisons the performance of the Peircean EA is notably superior. This exercise provides preliminary results that support Schwefel's hypothesis. In return the experiments in evolutionary computation help provide important insights into Peirce's theory of evolution.}, notes = {Also known as \cite{2482743} Distributed at GECCO-2013.}, } @inproceedings{Anton-Sanchez:2013:GECCOcomp, author = {Laura Anton-Sanchez and Concha Bielza and Larra\, {n}aga, Pedro}, title = {Towards optimal neuronal wiring through estimation of distribution algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1647--1650}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482744}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {One of the greatest challenges of our time is to understand brain functions. Our goal is to study the existence of an optimal neuronal design, defined as the one that has a minimum total wiring. Many researchers have studied the problem of optimal wiring in neuronal trees. Here we propose a new approach. We start from point clouds formed by the branching points of real neuronal trees and we search for the optimal forest from these point clouds. To do this, we formalise the problem as a forest of degree constrained minimum spanning trees (DCMST). Since the DCMST problem is NP-hard, we will try to solve it using estimation of distribution algorithms, particularly in permutation domains.}, notes = {Also known as \cite{2482744} Distributed at GECCO-2013.}, } @inproceedings{Athanasiou:2013:GECCOcomp, author = {Anastasia Athanasiou and Giuseppe Oliveto and Mineo Takayama and Keiko Morita}, title = {Problems in the identification of base isolation systems from earthquake records}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1651--1654}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482745}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work extends some recently obtained results on the identification of base isolation systems from earthquake response records. The identification is carried out by means of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES). By extending the number of iterations in each run and the number of runs it is shown that the obtained results have engineering significance. The design of a fictitious problem on the basis of a real seismic isolation system has allowed for the evaluation of the error on the individual system parameters. Some information on the completeness of the data used for the identification has also been provided. The work is concluded by the description of some open problems that the authors are facing and are determined to solve using the recent advances in computer science and technology.}, notes = {Also known as \cite{2482745} Distributed at GECCO-2013.}, } @inproceedings{Buzdalov:2013:GECCOcomp, author = {Maxim Buzdalov and Arina Buzdalova and Irina Petrova}, title = {Generation of tests for programming challenge tasks using multi-objective optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1655--1658}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482746}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, an evolutionary approach to generation of test cases for programming challenge tasks is investigated. Multi-objective and single-objective evolutionary algorithms, as well as helper-objective selection strategies, are compared. Particularly, a previously proposed method of choosing between helper-objectives with reinforcement learning is considered. This method is applied to the multi-objective evolutionary algorithm for the first time. Results of the experiment show that the most reasonable approach for the considered problem is using multi-objective evolutionary algorithm with automated helper-objective selection.}, notes = {Also known as \cite{2482746} Distributed at GECCO-2013.}, } @inproceedings{Ren:2013:GECCOcomp, author = {Zhigang Ren and Wen Chen and Aimin Zhang and Chao Zhang}, title = {Enhancing invasive weed optimization with taboo strategy}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1659--1662}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2466815}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Invasive weed optimisation (IWO) is a recently developed metaheuristic that imitates the invasive behaviour of weeds in nature. However, the reproduction and spatial dispersal operators in original IWO may make most seeds located around the best weed, which will result in premature convergence. To overcome this drawback, we propose an enhanced IWO algorithm (EIWO) by using the core idea of taboo search. When no better solution is found in the neighbourhood of a weed within a certain number of iterations, EIWO judges that this weed has been stagnated and taboos it, thus avoiding the repeated search in its neighborhood. In addition, EIWO also defines a self-production operator which generates some new weeds in a random way rather than directly choosing from the current plant population, so that new solution regions can be explored. To verify the efficiency of the proposed algorithm, we compared it with the original IWO, an improved IWO, and a modified particle swarm optimisation on a set of 16 benchmark functions. Computational results indicate that EIWO can prevent premature convergence and produce competitive solutions.}, notes = {Also known as \cite{2466815} Distributed at GECCO-2013.}, } @inproceedings{Garcia-Sanchez:2013:GECCOcompa, author = {Pablo Garc\'{\i}a-S\'{a}nchez}, title = {A service oriented evolutionary architecture: applications and results}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1663--1666}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482747}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper shows the stage of development of a Service Oriented Architecture for Evolutionary Algorithms and the first results obtained in two different areas. The abstract architecture is presented, with its associated implementation using a widely used technology. Results attained in experiments with parameter adaptation in distributed heterogeneous machines are presented and the usage of the architecture in Evolutionary Art is also applied.}, notes = {Also known as \cite{2482747} Distributed at GECCO-2013.}, } @inproceedings{Gardner:2013:GECCOcompa, author = {Marc-Andr\'{e} Gardner and Christian Gagn\'{e} and Marc Parizeau}, title = {Combinatorial optimization EDA using hidden Markov models}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1667--1670}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482748}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a well-known graphical model useful for modelling populations of variable-length sequences of discrete values. Surprisingly, HMMs have not yet been used as distribution estimators for an EDA, although they are a very powerful tool for estimating sequential samples. This paper thus proposes a new method, called HMM-EDA, implementing this idea, along with some preliminary experimental results.}, notes = {Also known as \cite{2482748} Distributed at GECCO-2013.}, } @inproceedings{Joshi:2013:GECCOcomp, author = {Ayush Joshi}, title = {Design of a parallel immune algorithm based on the germinal center reaction}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1671--1674}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482749}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial Immune algorithms are relatively new randomised meta-heuristics and not a lot of work has been done on parallel immune algorithms yet. Most of these implementations use some version of the first generation artificial immune algorithms. In this research a novel parallel artificial immune algorithm for optimisation is proposed based on cutting edge research in the study of germinal center reaction. This parallelism of the algorithm is inherent in the system as a whole, which is different than other parallel implementations of nature inspired algorithms, where several instances of the algorithm is run multiple times to exploit parallel architecture of computers. This system is being developed with input from immunologist and incorporates new ideas which have not been explored before. Some preliminary results are presented which hint that it could perform better than the evolutionary algorithm ((1+1)EA), with which it is compared. The algorithm is not limited to optimization and in the future the research will look into other application areas. Also limitations, improvements and applications where it excels, will be explored in the research.}, notes = {Also known as \cite{2482749} Distributed at GECCO-2013.}, } @inproceedings{Katz:2013:GECCOcomp, author = {Gali Katz and Amit Benbassat and Liana Diesendruck and Moshe Sipper and Avishai Henik}, title = {From size perception to counting: an evolutionary computation point of view}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1675--1678}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482750}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The ability to perceive size is shared by humans and animals. Babies present this basic ability from birth, and it improves with age. Counting, on the other hand, is a more complex task than size perception. We examined the theory that the counting system evolved from a more primitive system of size perception (the leading alternative being that the two systems evolved separately). By using evolutionary computation techniques, we generated artificial neural networks (ANNs) that excelled in size perception and presented a significant advantage in evolving the ability to count over those that evolved this ability from scratch. This advantage was observed also when evolving from ANNs that master other simple classification tasks. We also show that ANNs who train to perceive size of continuous stimuli present better counting skills than those that train with discrete stimuli.}, notes = {Also known as \cite{2482750} Distributed at GECCO-2013.}, } @inproceedings{Lattarulo:2013:GECCOcomp, author = {Valerio Lattarulo and Geoffrey T. Parks}, title = {Testing of the multi-objective alliance algorithm on benchmark functions}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1679--1682}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482751}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A new version of the Multi-objective Alliance Algorithm (MOAA) is described. The MOAA's performance is compared with that of NSGA-II using the epsilon and hypervolume indicators to evaluate the results. The benchmark functions chosen for the comparison are from the ZDT and DTLZ families and the main classical multi-objective (MO) problems. The results show that the new MOAA version is able to outperform NSGA-II on almost all the problems.}, notes = {Also known as \cite{2482751} Distributed at GECCO-2013.}, } @inproceedings{Liskowski:2013:GECCOcomp, author = {Pawe\l Liskowski}, title = {Quantitative analysis of the hall of fame coevolutionary archives}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1683--1686}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482752}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper provides an attempt to investigate the properties of the Hall of Fame archive in two-population competitive coevolution environment applied to the game of Othello. Using the measure of expected utility, a round-robin tournament and performance profiles, we show that coevolution can be biased towards playing better with stronger opponents if it is driven by interactions with the past champions kept in the archive, in addition to pure competition among coevolving individuals. Moreover, the Hall of Fame does not necessarily influence the overall performance in terms of expected utility, as it trades-off the ability to cope with opponents of various strength, so that the resulting players are more likely to win with a strong opponent than with a weak one.}, notes = {Also known as \cite{2482752} Distributed at GECCO-2013.}, } @inproceedings{Liu:2013:GECCOcomp, author = {Yi Ling Liu and Fernando Gomide}, title = {Genetic participatory algorithm and system modeling}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1687--1690}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482753}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper suggests a genetic participatory learning algorithm and illustrates its use in fuzzy systems modelling. The algorithm emerges from the concepts of participatory learning, selective transfer, and differential evolution. In genetic participatory learning the current population plays an important role in shaping evolution of the population individuals themselves. Selection uses compatibility between best and ramdonly chosen individuals. Exchange of information between individuals employes a recombination operator built from a selective transfer mechanism, whereas mutation proceeds analogously as in differential evolution. Recombination and mutation operations are affected by compatibility between individuals. An application example regarding fuzzy modelling of an electric maintenance problem using actual data serves to illustrate the effectiveness of the algorithm, and to compare with alternative participatory and genetic fuzzy systems approaches. Computational results suggest that genetic participatory learning produces accurate and competitive models when compared with current state of the art approaches.}, notes = {Also known as \cite{2482753} Distributed at GECCO-2013.}, } @inproceedings{Mist:2013:GECCOcomp, author = {Joseph James Mist and Stuart James Gibson}, title = {Optimization of weighted vector directional filters using an interactive evolutionary algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1691--1694}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482754}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Weighted vector directional filters are used to enhance multichannel image data and have attracted a lot of interest from researchers in the image processing community. This paper describes a novel method for deriving the weights of a vector directional filter that uses an interactive evolution strategy. We performed an empirical study in which 30 participants each developed two filters using our approach. Each participant compared the performance of his/her filters to the basic vector directional filter and a filter that had previously been developed using a genetic algorithm. Of the filters studied, our interactive approach was the most effective at removing salt and pepper noise for the case when the percentage of corrupt image pixels was low.}, notes = {Also known as \cite{2482754} Distributed at GECCO-2013.}, } @inproceedings{Moore:2013:GECCOcomp, author = {Jared M. Moore}, title = {Applying evolutionary computation to harness passive material properties in robots}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1695--1698}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482755}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolution has produced a wide variety of organisms that interact with their physical environment through musculoskeletal systems. Movements are often aided by passive characteristics of an organism's body and the inherent flexibility of muscles. Emulating these characteristics in a robot can potentially increase performance and manoeuvrability, but requires finding effective solutions among an infinite set of possible morphology and controller combinations. Evolutionary computation provides a means to explore this large search space. However, developing simulation models to account for these material properties presents challenges. In this paper, we present an overview of the challenges in implementing such an evolutionary approach. We also present preliminary results demonstrating the effectiveness of our proposed methods.}, notes = {Also known as \cite{2482755} Distributed at GECCO-2013.}, } @inproceedings{Sosa-Hernandez:2013:GECCOcomp, author = {Victor Adrian Sosa-Hernandez and Oliver Sch\"{u}tze and G\"{u}nter Rudoph and Heike Trautmann}, title = {Directed search method for indicator-based multi-objective evolutionary algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1699--1702}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482756}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Indicator based evolutionary algorithms have caught the interest of many researchers for the treatment of multi-objective optimisation problems in the recent past since they deliver the desired approximation of the solution set and due to a usually better performance compared to dominance based algorithms. Nevertheless, these methods still suffer the drawback that many function evaluations are required to obtain a suitable representation of the solution set. The aim of this study is to present the Directed Search (DS) Method as local searcher within global indicator based optimisation algorithms. For this, we will present the DS in the context of hypervolume maximisation leading to both a new local search algorithm and a new memetic algorithm. Further, we will present first attempts to adapt the DS to a class of parameter dependent problems.}, notes = {Also known as \cite{2482756} Distributed at GECCO-2013.}, } @inproceedings{Fister:2013:GECCOcomp, author = {Iztok Fister,Jr. and Du\v{s}an Fister and Iztok Fister}, title = {Differential evolution strategies with random forest regression in the bat algorithm}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1703--1706}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482757}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we present a novel solution for the hybridisation of the bat algorithm with differential evolution strategies and a random forests machine learning method. Extensive experiments and tests on standard benchmark functions have shown that these hybridised algorithms improved the original bat algorithm significantly.}, notes = {Also known as \cite{2482757} Distributed at GECCO-2013.}, } @inproceedings{Hessel:2013:GECCOcomp, author = {Jack Hessel and Sherri Goings}, title = {Evolving multicellularity in digital organisms through reproductive altruism}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1707--1710}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2482758}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The processes by which multicellular organisms first emerged from their unicellular ancestors are fundamental to the biology of complex, differentiated life forms. Previous work suggests that reproductive division of labour between specialised germ and soma cells was central to this evolution in some cases. Here, we use the digital life platform Avida to examine the trade-off between survival and replication in multicellular organisms. Avida uses a grid of self-replicating computer programs capable of mutation and evolution to address biological questions computationally. We model our digital organisms after the Volvocales, a flagellated order of photosynthetic green algae that includes both unicellular and multicellular species. We show that, given selective pressures similar to those experienced by the Volvocales in nature, digital organisms are capable of evolving multicellularity within the Avida platform. The strategies we observed that best handled the trade-off between survival and replication involved germ cells producing sterile, somatic offspring. These strategies are similar to those observed in volvocine algae, which suggests that digital platforms are appropriate to use in the study of reproductive altruism.}, notes = {Also known as \cite{2482758} Distributed at GECCO-2013.}, } @inproceedings{Ahmed:2013:GECCOcomp, author = {Saif Ahmed and Md. Tanvir Alam Anik and K.M. Rakibul Islam}, title = {An efficient evolutionary programming algorithm using mixed mutation operators for numerical optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1711--1712}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480788}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms often suffer from premature convergence when dealing with complex multi-modal function optimisation problems as the fitness landscape may contain numerous local optima. To avoid premature convergence, sufficient amount of genetic diversity within the evolving population needs to be preserved. In this paper we investigate the impact of two different categories of mutation operators on evolutionary programming in an attempt to preserve genetic diversity. Participation of the mutation operators on the evolutionary process is guided by fitness stagnation and localisation information of the individuals. A simple experimental analysis has been shown to demonstrate the effectiveness of the proposed scheme over a test-suite of five classical benchmark functions}, notes = {Also known as \cite{2480788} Distributed at GECCO-2013.}, } @inproceedings{Shakhnaz:2013:GECCOcomp, author = {Akhmedova Shakhnaz and Andrey Shabalov}, title = {Development and investigation of biologically inspired algorithms cooperation metaheuristic}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1713--1714}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480791}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cooperation of biologically inspired algorithms as an optimisation meta-heuristic is considered. Its performance evaluation and comparison with component algorithms performance on benchmark optimization problems is fulfilled. Workability of the meta-heuristic is demonstrated with artificial neural networks based classifiers tuned to two real world problems.}, notes = {Also known as \cite{2480791} Distributed at GECCO-2013.}, } @inproceedings{Bajer:2013:GECCOcomp, author = {Luk\'{a}\v{s} Bajer and Viktor Charypar and Martin Hole\v{n}a}, title = {Model guided sampling optimization with gaussian processes for expensive black-box optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1715--1716}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480794}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Model Guided Sampling Optimization (MGSO) is a novel expensive black-box optimisation method based on a combination of ideas from Estimation of Distribution Algorithms and global optimization methods using Gaussian Processes. The algorithm is described and its implementation tested on three benchmark functions as a proof of concept.}, notes = {Also known as \cite{2480794} Distributed at GECCO-2013.}, } @inproceedings{BenabidNajjar:2013:GECCOcomp, author = {Abir Benabid Najjar}, title = {Toward an optimized arabic keyboard design for single-pointer applications}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1717--1718}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480784}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces an ongoing project that aims to design an Arabic keyboard for applications that predominantly use single pointer input device. Such applications are available in mobile devices like Portable Data Assistant (PDA) and Smart phones, as well as in gaze controlled interfaces which constitute an on-growing mode of communication, especially for people with mobility impairment. In this paper, we focus on the optimisation of the key arrangement based on the movement time of one pointer (finger, stylus, eye...) and character transition frequencies in the Arabic language. Experimental results show that the optimized layout improves the overall typing speed and outperforms the commonly used Arabic keyboards.}, notes = {Also known as \cite{2480784} Distributed at GECCO-2013.}, } @inproceedings{Benbassat:2013:GECCOcomp, author = {Amit Benbassat and Moshe Sipper}, title = {Evolving artificial neural networks with FINCH}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1719--1720}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480780}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present work with the FINCH automatic evolutionary programming tool to evolve code that generates Artificial Neural Networks (ANNs) that perform desired tasks. We show how FINCH can be used to evolve code that generates an ANN that performs a simple classifying task with proficiency.}, notes = {Also known as \cite{2480780} Distributed at GECCO-2013.}, } @inproceedings{Bhardwaj:2013:GECCOcomp, author = {Arpit Bhardwaj and Aruna Tiwari}, title = {Performance improvement in genetic programming using modified crossover and node mutation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1721--1722}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480787}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness'a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and point mutation operation in GP algorithm in order to reduce the problem of bloat. To demonstrate our approach, we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying modified crossover together with modified node mutation reduces the problem of bloat substantially without compromising the performance.}, notes = {Also known as \cite{2480787} Distributed at GECCO-2013.}, } @inproceedings{Cruz:2013:GECCOcomp, author = {J. Albert Cruz and Juan-Juli\'{a}n Merelo Guerv\'{o}s and Antonio M. Mora Garc\'{\i}a and Paloma de las Cuevas}, title = {Adapting evolutionary algorithms to the concurrent functional language Erlang}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1723--1724}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480782}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we describe how the usual sequential and procedural Evolutionary Algorithm is mapped to a concurrent and functional framework using the Erlang language. The design decisions, as well as some early results, are shown.}, notes = {Also known as \cite{2480782} Distributed at GECCO-2013.}, } @inproceedings{Darabos:2013:GECCOcompa, author = {Christian Darabos and Britney E. Graham and Ting Hu and Jason H. Moore}, title = {Bipartite networks to study the genotype-to-phenotype relationship in cellular automata models}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1725--1726}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480789}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In biological organisms, a single genotype may map to several phenotypes and vice-versa. This many-to-many relationship is believed to be a major drive of the phenotypic robustness and genotypic evolvability found in all life forms. Given the inherent complexity of the genotype-to-phenotype (G2P) mappings, we use cellular automata (CAs) as rudimentary proxies for biological organisms. CA models have the same many-to-many G2P mappings, and their sensitivity to initial conditions allows the same genotype to differentiate into different phenotypes. We use a bipartite network to study the G2P landscape, and its projections in either space. The network and its projections all have a heavy-tailed degree distribution, hinting at an increased robustness supported by the network structure. We also show a strong correlation between the phenotype's complexity and its robustness. We are currently working on analysing the relationships between the robustness and the evolvability both at the genotypic and phenotypic level. Preliminary results agree with those of previous similar studies, using different computational models.}, notes = {Also known as \cite{2480789} Distributed at GECCO-2013.}, } @inproceedings{Elyasaf:2013:GECCOcompa, author = {Achiya Elyasaf and Michael Orlov and Moshe Sipper}, title = {A heuristiclab evolutionary algorithm for FINCH}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1727--1728}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480786}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a HeuristicLab plugin for FINCH. FINCH (Fertile Darwinian Bytecode Harvester) is a system designed to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer's toolkit.}, notes = {Also known as \cite{2480786} Distributed at GECCO-2013.}, } @inproceedings{Dougan:2013:GECCOcomp, author = {Erkan Do\u{g}an and Ferhat Erdal}, title = {Hunting search algorithm based design optimization of steel cellular beams}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1729--1730}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480777}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The present study examines a hunting search based optimum design algorithm for cellular beams. Hunting search is a numerical optimisation method inspired by group hunting of animals. The proposed algorithm selects the optimum UB section to be used in the production of a cellular beam subjected to a general loading, the optimum holes diameter and number of these holes in the beam. Furthermore, this selection is also carried out such that the design limitations are satisfied and the weight of the cellular beam is the minimum. A design example is considered to demonstrate the application of the optimum design algorithm developed.}, notes = {Also known as \cite{2480777} Distributed at GECCO-2013.}, } @inproceedings{Fonseca:2013:GECCOcomp, author = {Jorge Fonseca and Rui Neves and Nuno Horta}, title = {Optimizing investment strategies based on companies earnings using genetic algorithms}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1731--1732}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480785}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work proposes an investment strategy using Genetic Algorithms applied to the stock market. In order to build a portfolio of promising stocks we look at fundamental analysis by using indicators such as earnings volatility and growth, Price-to-Earnings ratio and Price/Earnings to Growth ratio. Additionally technical indicators such as moving average crossovers and Relative Strength Index are used to adapt the portfolio to the market's trends. The proposed solution was applied to the S&P500 Index during the period from 2006 to 2011. In order to evolve a robust strategy these are evaluated according to the average return on investment, Drawdown and Sharpe ratio. The results obtained are promising with the solution outperforming the market with a considerable lower level of risk.}, notes = {Also known as \cite{2480785} Distributed at GECCO-2013.}, } @inproceedings{Fredericks:2013:GECCOcomp, author = {Erik M. Fredericks and Betty H.C. Cheng}, title = {Exploring automated software composition with genetic programming}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1733--1734}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480790}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Much research has been performed in investigating the numerous dimensions of software composition. Challenges include creating a composition-based design process, designing software for reuse, investigating various strategies for composition, and automating the composition process. Depending on the complexity of the relevant components, numerous composition strategies may exist, each of which may have several options and variations for aggregate steps in realising these strategies. This paper presents an evolutionary computation-based framework for automatically searching for and realising an optimal composition strategy for composing a given target module into an existing software system.}, notes = {Also known as \cite{2480790} Distributed at GECCO-2013.}, } @inproceedings{Goss:2013:GECCOcomp, author = {Ryan G. Goss and Geoff S. Nitschke}, title = {Network protocol identification ensemble with EA optimization}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1735--1736}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480783}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In computer networks, the ability to correctly classify and control traffic flows is essential in order to manage network resources. A number of works have focused on the identification of flow attributes, or discriminators, able to distinguish the underlying application protocol of a flow at an early stage of it's existence. In this study k-means is investigated for identifying distinct application protocols present within flow data sets generated using a select number of discriminators. The clusters identified were used in a supervised training process that correctly identified protocols with an almost perfect (99percent percent) success rate.}, notes = {Also known as \cite{2480783} Distributed at GECCO-2013.}, } @inproceedings{Gwak:2013:GECCOcomp, author = {Jeonghwan Gwak and Moongu Jeon}, title = {A robust real-coded genetic algorithm using an ensemble of crossover operators}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1737--1738}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480792}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Although a lot of crossover operators have been developed for genetic algorithms (GAs), there is not much research on combining different crossover operators to form robust real-coded GAs. In this work, we propose an ensemble of crossover operators which is realised by two different parallel populations. The effectiveness of the proposed method is evaluated for traditional 6 benchmark functions. Results demonstrated that the proposed method has good generalisation performance.}, notes = {Also known as \cite{2480792} Distributed at GECCO-2013.}, } @inproceedings{Horn:2013:GECCOcomp, author = {Jeffrey Horn and Matthew J. Holliday and Dylan J. Elliott and Joshua A. Chomicki and David A. Pfeiffer and Steven M. Scheel and Lewis D. Steiner and Erik P. Wisuri and James M. Zeits and Jordan M. Bal and Chelsea G. Burton and Micah A. Erickson and Nicholas D. McIntyre-Wyma}, title = {Quality versus quantity of rules in a classifier jury: extended abstract}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1739--1740}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480795}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We show that under certain general circumstances there exists a choice of classifier rule length versus number of classifier rules, when given a fixed length classifier system, that maximises performance of the system.}, notes = {Also known as \cite{2480795} Distributed at GECCO-2013.}, } @inproceedings{Langdon:2013:GECCOcompa, author = {W B. Langdon}, title = {Which is faster: bowtie2GP $>$ bowtie $>$ bowtie2 $>$ BWA}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1741--1742}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480772}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We have recently used genetic programming to automatically generate an improved version of Langmead's DNA read alignment tool Bowtie2 [RN/12/09, Sect.5.3]. We find it runs more than four times faster than the Bioinformatics sequencing tool (BWA) currently used with short next generation paired end DNA sequences by the Cancer Institute, takes less memory and yet finds similar matches in the human genome.}, notes = {Also known as \cite{2480772} Distributed at GECCO-2013.}, } @inproceedings{Osaba:2013:GECCOcomp, author = {Eneko Osaba and Fernando Diaz and Enrique Onieva}, title = {A novel meta-heuristic based on soccer concepts to solve routing problems}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1743--1744}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480776}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we describe a new meta-heuristic to solve routing problems. This meta-heuristic is called Golden Ball (GB), and it is based on soccer concepts. To prove its quality we apply it to the Vehicle Routing Problem with Backhauls (VRPB) and we compare its results with the results obtained by a basic Genetic Algorithm (GA) and an Evolutionary Algorithm (EA).}, notes = {Also known as \cite{2480776} Distributed at GECCO-2013.}, } @inproceedings{Rodriguez-vazquez:2013:GECCOcomp, author = {Katya Rodriguez-vazquez}, title = {Sunspots modelling: comparison of GP approaches}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {genetic algorithms, genetic programming}, pages = {1745--1746}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480779}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a comparative study of the Multi-Branches Genetic Programming (MBGP), GP-NARMAX model approach and Standard Genetic Programming (SGP) for modelling problems. Sunspots data have been considered as study case in order to performance this comparison. The main point is to generate mathematical models in a polynomial form; thus the root node for MBGP has been set as the addition operator. Results show that MBGP rapidly evolves towards good mathematical models which are also easily to translate as well as the GP-NARMAX approach represented in its polynomial form in contrast to SGP.}, notes = {Also known as \cite{2480779} Distributed at GECCO-2013.}, } @inproceedings{Salto:2013:GECCOcomp, author = {Carolina Salto and Francisco Luna and Enrique Alba}, title = {Enhancing distributed EAs using proactivity}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1747--1748}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480796}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this abstract we describe a proactive strategy followed by a distributed evolutionary algorithm to adapt its migration policy. The proactive decision is made locally within each subpopulation, ant it is based on the entropy of that subpopulation. In that way, each subpopulation can ask for more/less frequent migrations from its neighbours in order to maintain the genetic diversity at a desired level, thus avoiding the subpopulations to get trapped into local minima. We conduct computational experiments on a set of different problems and it is shown that our proactive approach outperforms classical dEA settings by reaching accurate solutions in a lower number of generations.}, notes = {Also known as \cite{2480796} Distributed at GECCO-2013.}, } @inproceedings{Sato:2013:GECCOcomp, author = {Yuji Sato and Mikiko Sato}, title = {Fault-tolerance of distributed genetic algorithms on many-core processors}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1749--1750}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480778}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper reports on fault-tolerant technology for use with high-speed parallel evolutionary computation on many-core processors. In particular, for distributed GA models which communicate between islands, we propose a method where an island's ID number is added to the header of data transferred by this island for use in fault detection, and we evaluate this method using Deceptive functions and Sudoku puzzles. As a result, we show that it is possible to detect single stuck-at faults with practically negligible overheads in applications where the time spent performing genetic operations is large compared with the data transfer speed between islands. We also show that it is still possible to obtain an optimal solution when a single stuck-at fault is assumed to have occurred, and that increasing the number of parallel threads has the effect of making the system less susceptible to faults and more sustainable.}, notes = {Also known as \cite{2480778} Distributed at GECCO-2013.}, } @inproceedings{Semenkina:2013:GECCOcomp, author = {Maria Semenkina}, title = {Parallel version of self-configuring genetic algorithm application in spacecraft control system design}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1751--1752}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480793}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Technological and command-programming control contours of a spacecraft are modelled with Markov chains. These models are used for the preliminary design of spacecraft control system effective structure with the use of special DSS. Corresponding optimisation problems with algorithmically given functions of mixed variables are solved with a special stochastic algorithm called self-configuring genetic algorithm that requires no settings determination and parameter tuning. The high performance of the suggested algorithm is proved by solving real problems of the control contours structure preliminary design.}, notes = {Also known as \cite{2480793} Distributed at GECCO-2013.}, } @inproceedings{Tsutsui:2013:GECCOcomp, author = {Shigeyoshi Tsutsui and Noriyuki Fujimoto}, title = {A preliminary study of crowding with biased crossover}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1753--1754}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480774}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a novel crowding method, which is called Crowding with Biased Crossover (CBX). The Biased crossover operator begins with two parents. Then two offspring individuals are created, each offspring taking more characteristics from one of the two parents. This is an easy method to perform replacement between parents and offspring individuals. Experimental results showed that CBX is very effective in finding both single global solutions and multiple solutions (niching).}, notes = {Also known as \cite{2480774} Distributed at GECCO-2013.}, } @inproceedings{Vargas:2013:GECCOcomp, author = {Danilo Vasconcellos Vargas and Hirotaka Takano and Junichi Murata}, title = {A study on the importance of selection pressure and low dimensional weak learners to produce robust ensembles}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1755--1756}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480775}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Ensembles of classifiers have been studied for some time. It is widely known that weak learners should be accurate and diverse. However, in the real world there are many constraints and few have been said about the robustness of ensembles and how to develop it. In the context of random subspace methods, this paper addresses the question of developing ensembles to face problems under time constraints. Experiments show that selecting weak learners based on their accuracy can be used to create robust ensembles. Thus, the selection pressure in ensembles is a key technique to create not just effective ensembles but also robust ones. Moreover, the experiments motivate further research on ensembles made of low dimensional classifiers which achieve general accurate results.}, notes = {Also known as \cite{2480775} Distributed at GECCO-2013.}, } @inproceedings{Zhang:2013:GECCOcomp, author = {Jin Zhang and Dietmar Maringer}, title = {Indicator selection for daily equity trading with recurrent reinforcement learning}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1757--1758}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480773}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which uses the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.}, notes = {Also known as \cite{2480773} Distributed at GECCO-2013.}, } @inproceedings{Harman:2013:GECCOcomp, author = {Mark Harman}, title = {Software engineering: an ideal set of challenges for evolutionary computation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1759--1760}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480770}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Software is an engineering material to be optimised. Until comparatively recently many computer scientists doubted this; why would one want to optimise something that could be made perfect by pure logical reasoning? However, the wider community has come to realise that, while very small programs may be perfect in isolation, larger software systems may never be (because the world in which they operate is not perfect). Once we accept this, we soon arrive at evolutionary computation as a means of optimising software. However, software is not merely some other engineering material to be optimised. Software is virtual and inherently adaptive, making it better suited to evolutionary computation than any other engineering material. This is leading to breakthroughs at the interface of software engineering and evolutionary computation, though there are still many exciting open problems for evolutionary commutation researchers to get their teeth into. This talk will cover some of these recent developments in Search Based Software Engineering (SBSE) and Dynamic Adaptive SBSE.}, notes = {Also known as \cite{2480770} Distributed at GECCO-2013.}, } @inproceedings{Yao:2013:GECCOcomp, author = {Xin Yao}, title = {Challenges and opportunities in dynamic optimisation}, booktitle = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = {2013}, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A.N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks}, isbn13 = {978-1-4503-1964-5}, keywords = {}, pages = {1761--1762}, month = {6-10 July}, organisation = {SIGEVO}, address = {Amsterdam, The Netherlands}, doi = {doi:10.1145/2464576.2480771}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Dynamic optimisation has been studied for many years within the evolutionary computation community. Many strategies have been proposed to tackle the challenge, e.g., memory schemes, multiple populations, random immigrants, restart schemes, etc. This talk will first review a few of such strategies in dealing with dynamic optimisation. Then some less researched areas are discussed, including dynamic constrained optimisation, dynamic combinatorial optimisation, time-linkage problems, and theoretical analyses in dynamic optimisation. A couple of theoretical results, which were rather unexpected at the first sight, will be mentioned. Finally, a few future research directions are highlighted. In particular, potential links between dynamic optimisation and online learning are pointed out as an interesting and promising research direction in combining evolutionary computation with machine learning.}, notes = {Also known as \cite{2480771} Distributed at GECCO-2013.}, } @proceedings(Blum:2013:GECCOcomp, title = {GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion}, year = 2013, editor = {Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar 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, Introductory tutorials, Advanced tutorials, Specialized techniques and applications tutorials, Workshop on problem understanding and real-world optimisation, Symbolic regression and modeling workshop, Black box optimization benchmarking 2013 (BBOB 2013), Sixteenth international workshop on learning classifier systems, Evolutionary computation software systems (EvoSoft'13), Visualisation methods in genetic and evolutionary computation (VizGEC 2013), Evolutionary computation and multi-agent systems and simulation (EcoMass) seventh annual workshop, Medical applications of genetic and evolutionary computation (MedGEC'13), 3rd workshop on evolutionary computation for the automated design of algorithms, Green and efficient energy applications of genetic and evolutionary computation workshop, International workshop on evolutionary computation in bioinformatics, genetic algorithms, genetic programming, Stack-based workshop, Student workshop, Late-breaking abstracts, Keynote talks}, ISBN13 = {978-1-4503-1964-5}, notes = {Distributed at GECCO-2013.}, )