%5 Aug 2011 ensure keys are unique %processed by gecco2011_authors.awk Thu Jul 28 20:37:23 BST 2011 %1 gecco2011comp.bib3_ %processed by gecco2011_keywords.txt Thu Jul 28 20:37:23 BST 2011 %1 gecco2011comp_keywords.txt %2 gecco2011comp.bib2_ %processed by gecco2011_toc.awk $Revision: 1.10 $ Thu Jul 28 20:36:47 BST 2011 %1 gecco2011comp_toc.txt %2 gecco2011comp_editors.txt %3 gecco2011comp.bib %4 gecco2011comp.bib %WBL 28 Jul 2020 bugfix @ for gecco_errors.txt from Paul Ortyl Jul 12, 2020 %WBL 1 Aug 2017 ensure passes bibclean v3.02 @inproceedings{Coates:2011:GECCOcomp, author = {Richard D. Coates and Simon J. Hickinbotham}, title = {Estimating swarm parameters by evolutionary learning}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {1--2}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001860}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {If we assume that the collective dynamics of wild animals can be modelled, it would be desirable to recover the dynamics of the model via interaction with them. In this paper, we demonstrate that it is possible to recover the parameters of a shoaling model used by a swarm. This can be achieved by evolving the parameters of a single agent that interacts with the swarm. We present an evaluation of this approach, using a genetic algorithm to learn the parameters of a shoaling model.}, notes = {Also known as \cite{2001860} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Rodriguez-Portela:2011:GECCOcomp, author = {Arles Ernesto {Rodriguez Portela} and Jonatan {G\'{o}mez Perdomo}}, title = {Mechanism of failure diagnosis based on language games and q-learning over a termites simulator}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {3--4}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001861}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a mechanism of failure diagnosis in a multi-agent environment of termites looking for food. The failure system is defined based on the probability that each termite (agent) has of executing a movement instruction. By using language games concepts and a version of the Q-learning algorithm, termites diagnose failures with the highest failure probabilities. Termites also have enough information to determine which movement actuator is failing based on a simple voting system that is the result of language games of diagnosis. Results show that the proposed approach is able, from local interactions, to build a set of very specific diagnosis questions, allowing the system to diagnose more than one type of failure at the same time, while the accounted number of diagnosis questions for instructions with low failure probability is reduced.}, notes = {Also known as \cite{2001861} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gwizdalla:2011:GECCOcomp, author = {Tomasz M. Gwizda\l\la}, title = {Different versions of particle swarm optimization for magnetic problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {5--6}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001862}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper presents the application of Particle Swarm Optimisation into the magnetic problems where the structure of sample, its stoichiometry and the character of magnetic interactions is described by some well known models. We use three different models or approximations what enables to use three different versions of PSO: binary, real-number and discrete (multi-state). We show that, in order to prepare the efficient code leading to the correct results, we have to include some changes. The most important is the modification of the relative strength of the cognitive and social factors determining the value of velocity. We show also that the computational hardness of the optimisation problem depends on the choice of physical parameters. This feature makes it possible to use the presented cases as an interesting testing tool. We compare also our results with the results obtained by using genetic algorithms found either in references or generated by our own code.}, notes = {Also known as \cite{2001862} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{YiJunChen:2011:GECCOcomp, author = {YiJun Chen and Man Leung Wong}, title = {Optimizing stacking ensemble by an ant colony optimization approach}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {7--8}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001863}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An ensemble is a collective decision making system which applies some strategy to combine the predictions of classifiers to generate its prediction on new instances. Stacking is a well-known approach among the ensembles. It is not easy to find a suitable ensemble configuration for a specific dataset. Ant Colony Optimisation (ACO) is a popular metaheuristic approach which could be a solution to find configurations. In this work, we propose a new Stacking construction method which applies ACO in the Stacking construction process to generate domain-specific configurations. The experiment results show that the new approach can achieve promising results on 18 datasets compared with some well-known ensemble approaches.}, notes = {Also known as \cite{2001863} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ciesielski:2011:GECCOcomp, author = {Victor Ciesielski and Perry Barile}, title = {Animations rendered by braitenberg vehicles}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {9--10}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001864}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We describe an approach to generating animated sequences of images drawn using stroke-based rendering. Individual strokes are generated as a history of movements of a class of purely reactive computational agents known as Braitenberg Vehicles embedded on a digital canvas. The entire animation represents an aggregate of all the movements of the agents across an entire run. We provide end users with a software tool to produce such animations and allow users to set the sensory capabilities of agents on a low level basis. The agents perform feature detection based on such capabilities. The end users thus interact with the agents with a variety of input images to discover a range of renderable outcomes. Artists inform us that the animations that result from our output provide an engaging visual experience.}, notes = {Also known as \cite{2001864} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Umer:2011:GECCOcomp, author = {Samreen Umer and Adnan Ahmed Khan}, title = {Optimal relay station placement in cooperative networks using particle swarm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {11--12}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001865}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper explores the application of the particle swarm algorithm for a mixed integer non linear optimisation problem in the area of wireless communications. An appropriate relay station placement can enhance the overall capacity of wireless cooperative networks. The specific problem is finding out a realisable and efficient scheme for relay station placement in wireless cooperative networks. This paper defines a method based on particle swarm optimisation (PSO) to design and optimise a useful search space for relay station placement and also gives an efficient technique to allocate bandwidth to relay-subscriber association. The proposed algorithm out-performs the previous techniques without compromising overall efficiency. Simulation results guarantees improved performance compared to the existing systems and offers an acceptable trade-off between performance and complexity.}, notes = {Also known as \cite{2001865} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Oliveira:2011:GECCOcomp, author = {Sabrina M. Oliveira and Mohamed Saifullah Hussin and Thomas Stuetzle and Andrea Roli and Marco Dorigo}, title = {A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {13--14}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001866}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The population-based ant colony optimisation algorithm (P-ACO) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACO's behaviour for solving the travelling salesman problem (TSP) and the quadratic assignment problem (QAP). In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool whose parameters and behaviour depend strongly on the problem tackled and on whether a local search is used.}, notes = {Also known as \cite{2001866} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lin:2011:GECCOcomp, author = {Ying Lin and Jun Zhang}, title = {An ant colony optimization approach for efficient admission scheduling of elective inpatients}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {15--16}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001867}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an ant colony optimisation (ACO) approach to offer online decision support for making admission plans of inpatients. The approach considers patients' severity degrees and urgency levels, aiming to find an admission plan that offers treatment in time to as many patients as possible. At each decision point, the ACO approach builds a construction graph, with each vertices denoting one possible admission time for a patient in wait. Artificial ants walk on the construction graph to construct feasible admission plans by selecting vertices under guides of pheromones and heuristic information. The resulting plans are evaluated from both terms of the total admission rate and the severity degrees of admitted patients. The weights of the two components can be determined according to the preferences of hospital administrators. When implementing the admission plan, only the admissions that are scheduled before the next decision point are actually executed. The rest of the admission plan is used as guides for optimising the implemented admissions. Simulations based on actual data show that the ACO approach outperforms two classical admission policies and improve the hospital performance in the long run.}, notes = {Also known as \cite{2001867} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Coelho:2011:GECCOcomp, author = {Tiago Amador Coelho and Ahmed Ali Abdala Esmin and Wagner Meira J\'{u}nior}, title = {Particle swarm optimization for multi-label classification}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Ant colony optimization and swarm intelligence: Poster}, pages = {17--18}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001868}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-label classification learning first arose in the context of text categorisation, where each document may belong to several classes simultaneously and has attracted significant attention lately, as a consequence of both the challenge it represents and its relevance in terms of application scenarios. In this paper, we propose a new hybrid approach, Multi Label K-Nearest Michigan Particle Swarm Optimisation (ML-KMPSO), that is based on two strategies: Michigan Particle Swarm Optimisation (MPSO) and ML-KNN. We evaluated the performance of ML-KMPSO using two real-world datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.}, notes = {Also known as \cite{2001868} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tanev:2011:GECCOcomp, author = {Ivan Tanev and T\"{u}ze Kuyucu and Katsunori Shimohara}, title = {Incremental genetic programming via genetic transposition}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware: Poster}, pages = {19--20}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001870}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic transposition is a process of moving sequences of DNA to different positions within the genome of a single cell. Inspired by the role of genetic transposons in biology, we introduce a genetic transposition inspired mechanism in genetic programming (GP). This mechanism, a simple variation from seeding in incremental evolution, provides a more effective approach to the evolution of systems with multiple features.}, notes = {Also known as \cite{2001870} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Nakajima:2011:GECCOcomp, author = {Kohei Nakajima and Tao Li and Naveen Kuppuswamy and Rolf Pfeifer}, title = {Biologically inspired control of a simulated octopus ARM via recurrent neural networks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {21--22}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001871}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The aim of this study is to explore a control architecture that can control a soft and flexible octopus-like arm for an object reaching task. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we discuss that the important factor of the control is not to regulate the arm muscles one by one but rather to control them globally with appropriate timing, and we propose an architecture equipped with a recurrent neural network (RNN). By setting the task environment for the reaching behaviour, and training the network with an incremental learning strategy, we evaluate whether the network is then able to achieve the reaching behaviour or not. As a result, we show that the RNN can successfully achieve the reaching behaviour, exploiting the physical dynamics of the arm due to the timing-based control.}, notes = {Also known as \cite{2001871} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Couvertier:2011:GECCOcomp, author = {Daniel J. Couvertier and Philip K. McKinley}, title = {Effects of biased group selection on cooperative predation in digital organisms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {23--24}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001872}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A key issue in cooperative task completion is team composition. Prior studies have addressed two ends of a spectrum, with homogeneous teams on one end and heterogeneous teams on the other. In this paper we explore a space in between. In biased group selection, subpopulations compete against one another with respect to a cooperative task, but an external bias favours the genes of those individuals actually participating in the task. We evaluate this selection model on a cooperative predation task in digital organisms, where feasible solutions can be carried out by either homogeneous or heterogeneous teams. Our results show that, consistent with earlier studies, homogeneous teams tend to find better overall solutions than their heterogeneous counterparts. However, populations comprising teams with some degree of heterogeneity found solutions more frequently. Effectively, while evolution pushed heterogeneous teams toward functional homogeneity for this particular task, heterogeneity with a selection bias proved more effective at exploring the search space.}, notes = {Also known as \cite{2001872} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Montanier:2011:GECCOcomp, author = {Jean-Marc Montanier and Nicolas Bredeche}, title = {Emergence of altruism in open-ended evolution in a population of autonomous agents}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {25--26}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001873}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper summarises recent works on the evolution of altruism to solve the tragedy of commons in the context of open-ended evolution with a fixed number of robotic agents.}, notes = {Also known as \cite{2001873} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Eiben:2011:GECCOcomp, author = {A. E. Eiben and N. Ferreira and M. C. Schut and S. Kernbach}, title = {Embodied artificial evolution: the future of artificial evolutionary systems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {27--28}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001874}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This article is a vision paper about what we call embodied artificial evolution. The main objective is to offer an 'umbrella' term and vision to aid the development of a high potential research area. We introduce the vision by a few concrete examples and identify three major enablers. We also describe some possible benefits of embodied artificial evolution technology and discuss some of the essential challenges on the technical level, e.g., reproduction mechanisms, kill switch, and on a higher level, e.g., design methodology.}, notes = {Also known as \cite{2001874} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Murdock:2011:GECCOcomp, author = {Jaimie Murdock and Larry S. Yaeger}, title = {Genetic clustering for the identification of species}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {29--30}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001875}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial life simulations can yield distinct populations of agents representing different adaptations to a common environment or specialised adaptations to different environments. Here we apply a standard clustering algorithm to the genomes of such agents to discover and characterise these subpopulations. As evolution proceeds new subpopulations are produced, which show up as new clusters. Cluster centroids allow us to characterise these different subpopulations and identify their distinct adaptation mechanisms. We suggest these subpopulations may reasonably be thought of as species, even if the simulation software allows interbreeding between members of the different subpopulations. Our results indicate both sympatric and allopatric speciation are present in the Polyworld artificial life system. Our analysis suggests that intra- and inter-cluster fecundity differences may be sufficient to foster sympatric speciation in artificial and biological ecosystems.}, notes = {Also known as \cite{2001875} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Yosinski:2011:GECCOcomp, author = {Jason Yosinski and Jeff Clune and Diana Hidalgo and Sarah Nguyen and Juan Cristobal Zagal and Hod Lipson}, title = {Generating gaits for physical quadruped robots: evolved neural networks vs. local parameterized search}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Artificial life/robotics/evolvable hardware: Poster}, pages = {31--32}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001876}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Creating gaits for legged robots is an important task to enable robots to access rugged terrain, yet designing such gaits by hand is a challenging and time-consuming process. In this paper we investigate various algorithms for automating the creation of quadruped gaits. Because many robots do not have accurate simulators, we test gait learning algorithms entirely on a physical robot. We compare the performance of two classes of learning gaits: locally searching parametrised motion models and evolving artificial neural networks with the HyperNEAT generative encoding. All parameter search methods outperform a manually-designed reference gait, but HyperNEAT performs better still, producing gaits nearly 9 times faster than the reference gait.}, notes = {Also known as \cite{2001876} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cornforth:2011:GECCOcomp, author = {Theodore W. Cornforth and Jim Torresen and Hod Lipson}, title = {Ion channel modeling with analog circuit evolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Bioinformatics, computational, systems, and synthetic biology: Poster}, pages = {33--34}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001878}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose that analog electrical circuits are a natural representation for modelling the dynamical systems that arise in neuroscience. Here we use analog circuit evolution, a reverse engineering technique designed to search through analog circuit space, to automatically design circuit models of ion channel behaviour. Results comparing several different multiobjective and coevolutionary techniques demonstrate the importance of evaluating the fitness of evolved circuits under multiple behavioural conditions.}, notes = {Also known as \cite{2001878} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hoover:2011:GECCOcomp, author = {Kristopher Hoover and Rachel Marceau and Tyndall Harris and Nicholas Hardison and David Reif and Alison Motsinger-Reif}, title = {Optimization of grammatical evolution decision trees}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology: Poster}, pages = {35--36}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001879}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimisation, evaluation, and comparison of this and related methods, and for proper application in real data.}, notes = {Also known as \cite{2001879} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pallez:2011:GECCOcomp, author = {Denis Pallez and Andrea G.B. Tettamanzi and C\'{e}lia {da Costa Pereira}}, title = {Comparing paired comparison-based interactive DE and tournament interactive GA on stained glass design}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Digital entertainment technologies and arts: Poster}, pages = {37--38}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001881}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Tournament Interactive Genetic Algorithm (T-IGA) and Paired Comparison-based Interactive Differential Evolution (PC-IDE) are applied to the design of stained glass windows and the two algorithms with variable length genotype are compared in a context of interactive evolutionary computation. For both methods, stained glass windows are represented by coloured 2D Voronoi diagrams, and a specific phenotypic crossover operator allows offspring to inherit visual features from both parents. The two algorithms have been evaluated by two professional stained-glass artists whom use them to create original designs in a controlled experimental setting. The results indicate superiority of PC-IDE, thus confirming previous theoretical results.}, notes = {Also known as \cite{2001881} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Vazquez-Fernandez:2011:GECCOcomp, author = {Eduardo Vazquez-Fernandez and Carlos Artemio Coello and Feliu Davino {Sagols Troncoso}}, title = {An adaptive evolutionary algorithm based on typical chess problems for tuning a chess evaluation function}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Digital entertainment technologies and arts: Poster}, pages = {39--40}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001882}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a method for adjusting weights of the evaluation function of a chess engine. Such an adjustment is carried out through an evolutionary algorithm which adopts a mechanism that selects the virtual players (individuals in the population) that have the highest number of problems properly solved from a database of typical chess problems. This method has the advantage that we only mutate those weights involved in the solution of the current problem. Furthermore, the mutation mechanism is adapted through the number of problems solved by each virtual player. Our results indicate that the material values obtained by our approach are similar to the values known from chess theory. Additionally, we also show that, using the approach proposed here, the strength of our chess engine is increased in 335 points.}, notes = {Also known as \cite{2001882} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Jensen:2011:GECCOcomp, author = {Johannes H. Jensen and Pauline C. Haddow}, title = {Evolutionary music composition based on Zipf's law}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Digital entertainment technologies and arts: Poster}, pages = {41--42}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001883}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Artificial Evolutionary techniques have shown great potential for musical tasks. One such task is the automatic generation - or composition - of music. However, when constructing evolutionary music composition systems, a major challenge is finding a suitable fitness function. The most common fitness approach is interactive evaluation. However, efficiency challenges with such an approach has inspired the search for automatic alternatives. In this work, a music composition system is presented for the evolution of novel melodies. The system uses an automatic fitness function based on Zipf's Law, which captures the scaling properties of music. Results show that the generated melodies exhibit several favourable musical properties, including melodic themes.}, notes = {Also known as \cite{2001883} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Byrne:2011:GECCOcomp, author = {Jonathan Byrne and Erik Hemberg and Michael O'Neill}, title = {Interactive operators for evolutionary architectural design}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts: Poster}, pages = {43--44}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001884}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process.}, notes = {Also known as \cite{2001884} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Che:2011:GECCOcomp, author = {Chan Hou Che and Zizhen Zhang and Andrew Lim}, title = {A memetic algorithm for solving multiperiod vehicle routing problem with profit}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {45--46}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001886}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Most literature on variations of vehicle routing problem assumes that a vehicle is continuously available within the planning horizon. However, in practice, due to the working time regulation, this assumption may not be valid in some applications. In this paper, we study a multiperiod vehicle routing problem with profit (mVRPP), where the goal is to determine a set of routes within the planning horizon that maximises the collected reward from nodes visited. The vehicles can only travel during working hours within each period in the planning horizon. An effective memetic algorithm with giant-tour representation is proposed to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy split procedure to optimally partition a given giant-tour into individual routes. We conduct extensive experiments on a set of modified benchmark instances. The result demonstrates that our approach generates promising solutions which are close to the upper bounds.}, notes = {Also known as \cite{2001886} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Grimme:2011:GECCOcomp, author = {Christian Grimme and Markus Kemmerling and Joachim Lepping}, title = {An expertise-guided multi-criteria approach to scheduling problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {47--48}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001887}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In production environments, decision makers are often confronted with scheduling problems that demand an optimisation of workflow regarding multiple criteria. For specific sub-problems experienced experts have available good heuristics which may contribute to generating a set of multi-criteria solutions. However, current evolutionary multi-criteria optimisation algorithms (EMCAs) usually offer structures that do not allow easy integration of such expertise. Thus, we propose and evaluate an integration of expertise into a loosely-coupled and agent-based system. We show that this concept provides an easy-to-adapt algorithm and is capable to approximate challenging multi-criteria scheduling problems more efficiently than standard approaches.}, notes = {Also known as \cite{2001887} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Conant-Pablos:2011:GECCOcomp, author = {Santiago Enrique Conant-Pablos and Halley Jarumy Ferrer-Soriano and Hugo Terashima-Marin}, title = {Evolving solutions of mixed-model assembly line balancing problems by chaining heuristic optimization methods: track: evolutionary combinatorial optimization and meta-heuristics}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {49--50}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001888}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Mixed-model Assembly Line Balancing (MALB) is needed on production of a variety of models on the same assembly line as required by just-in-time manufacturing. This paper presents an approach that applies Computational Intelligence techniques for solving MALB problems. The proposed solution consists in a heuristic optimisation method that works in three stages: first, it creates an initial population of based on heuristics from classic assembly line balancing methods; second, it uses a algorithm to maximise the line balancing level; and finally, it uses a min-conflicts algorithm to find a solution that better conforms to a set of preferences while trying to maintain the line efficiency of the previous stage. The results yielded by this method demonstrated to be competitive solutions and very close to the optimal.}, notes = {Also known as \cite{2001888} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Steitz:2011:GECCOcomp, author = {Wolfgang Steitz and Franz Rothlauf}, title = {Guided local search for the optimal communication spanning tree problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {51--52}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001889}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper considers the optimal communication spanning tree (OCST) problem. Previous work analysed features of high-quality solutions. Consequently, integrating this knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favouring edges with low weights pointing towards the tree's centre, GLS penalises low-quality edges with large weights that do not point towards the tree's center.}, notes = {Also known as \cite{2001889} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Qin:2011:GECCOcomp, author = {A. K. Qin and Florence Forbes}, title = {Dynamic regional harmony search with opposition and local learning}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {53--54}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001890}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Harmony search (HS), mimicking the musician's improvisation behaviour, has demonstrated strong efficacy in optimisation. To deal with the deficiencies in the original HS, a dynamic regional harmony search (DRHS) algorithm with opposition and local learning is proposed. DRHS uses opposition-based initialisation, and performs independent harmony searches with respect to multiple groups created by periodically regrouping the harmony memory. An opposition-based harmony creation scheme is used in DRHS to update each group memory. Any prematurely converged group is restarted with its size being doubled to enhance exploration. Local search is periodically applied to exploit promising regions around top-ranked candidate solutions. DRHS consistently outperforms HS on 12 numerical test problems from the CEC2005 benchmark at both 10D and 30D.}, notes = {Also known as \cite{2001890} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Chou:2011:GECCOcomp, author = {Yao-Hsin Chou and Chia-Hui Chiu and Yi-Jyuan Yang}, title = {Quantum-inspired tabu search algorithm for solving 0/1 knapsack problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {55--56}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001891}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we propose a novel quantum-inspired evolutionary algorithm, called quantum-inspired Tabu search (QTS). QTS is based on the classical Tabu search and the characteristic of quantum computation such as superposition. We will present how we implement QTS to solve 0/1 knapsack problem. Furthermore, the results of experiments are also compared with other quantum-inspired evolutionary algorithm and other heuristic algorithms' experimental results. The final outcomes show that QTS performs much better than the other heuristic algorithms on 0/1 knapsack problem, without premature convergence and more efficiency.}, notes = {Also known as \cite{2001891} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{de-Marcos:2011:GECCOcomp, author = {Luis de-Marcos and Antonio Garc\'{\i}a and Eva Garc\'{\i}a and Jos\'{e}-Amelio Medina and Salvador Ot\'{o}n}, title = {Comparing the performance of evolutionary algorithms for permutation constraint satisfaction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {57--58}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001892}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a systematic comparison of canonical versions of two evolutionary algorithms, namely Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA), for permutation constraint satisfaction (permut-CSP). Permut-CSP is first characterised and a test case is designed. Agents are then presented, tuned and compared. They are also compared with two classic methods (A* and hill climbing). Results show that PSO statistically outperforms all other agents, suggesting that canonical implementations of this technique return the best trade-off between performance and development cost for our test case.}, notes = {Also known as \cite{2001892} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Dominguez-Isidro:2011:GECCOcomp, author = {Sa\'{u}l Dom\'{\i}nguez-Isidro and Efr\'{e}n Mezura-Montes and Luis Guillermo Osorio-Hern\'{a}ndez}, title = {Addition chain length minimization with evolutionary programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary combinatorial optimization and metaheuristics: Poster}, pages = {59--60}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001893}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents the use of an evolutionary metaheuristic algorithm called evolutionary programming to minimise the length of addition chains, which is an NP-hard problem. Addition chains are used in modular exponentiation for data encryption and decryption public-key cryptosystems, such as RSA, DSA and others. The algorithm starts with a population of feasible addition chains. After that, the combination of a mutation operator, which allows each individual to generate a feasible offspring, and a replacement process based on stochastic encounters provides a simple approach which is tested on exponents with different features. The proposed algorithm is able to find competitive results with respect to other nature-inspired metaheuristic approaches but with a lower number of evaluations per run.}, notes = {Also known as \cite{2001893} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Chang:2011:GECCOcomp, author = {Jen-Hao Chang and Chung-Hsiang Hsueh and Hsuan Lee and Tian-Li Yu and Tsung-Yu Ho}, title = {A test function with full controllability over overlapping: estimation of distribution algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {61--62}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001895}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work proposes a test function to study overlapping. The test function provides full controllability over overlapping. To achieve full controllability, the building block assigning problem is reduced to a bipartite matching problem which allow us to directly assign extent of overlapping to each gene. At the end, an experiment on overlapping shows that to four chosen crossover methods, the problem difficulty increases exponentially with the extent of overlapping.}, notes = {Also known as \cite{2001895} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Higo:2011:GECCOcomp, author = {Takayuki Higo}, title = {Importance sampling regularization for estimation of distribution algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {63--64}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001896}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {}, notes = {Also known as \cite{2001896} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wallin:2011:GECCOcomp, author = {David Wallin and Conor Ryan and R. Muhammad Atif Azad}, title = {Candidate oversampling prefers two to tango: estimation of distribution algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {65--66}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001897}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent work has enhanced the Evolutionary Bayesian Classifier-based Optimisation Algorithm (EBCOA) by oversampling the next generation and identifying promising solutions without actually evaluating their fitness values. In order to model the existing generation, that work considered two classes of solutions, that is, high performing solutions (H-Group) and poorly performing solutions (L-Group). In this study, we test the utility of using two classes instead of using a single class, as is the norm in standard Estimation of Distribution Algorithms (EDAs). Our results show that a dual class model is preferable when oversampling is used.}, notes = {Also known as \cite{2001897} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wattanapornprom:2011:GECCOcomp, author = {Warin Wattanapornprom and Prabhas Chongstitvatana}, title = {Solving multimodal combinatorial puzzles with edge-based estimation of distribution algorithm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {67--68}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001898}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This article compares two edge-based Estimation of Distribution Algorithms named Edge Histogram Based Sampling Algorithm (EHBSA) and Coincidence Algorithm (COIN) in multimodal combinatorial puzzles benchmarks. Both EHBSA and COIN make use of joint probability matrix of adjacent events (edge) derived from the population of candidate solutions. These algorithms are expected to be competitive in solving problems where relative relation between two nodes is significant. The experiment results imply that EHBSAs are better in convergence to a single optima point, while COINs are better in maintaining the diversity among the population and are better in preventing the premature convergence.}, notes = {Also known as \cite{2001898} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Segovia-Dominguez:2011:GECCOcomp, author = {Ignacio Segovia-Dominguez and Arturo Hernandez-Aguirre and Enrique Villa-Diharce}, title = {The gaussian polytree EDA for global optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {69--70}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001899}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper explains how to construct Gaussian polytrees and their application to estimation of distribution algorithms in continuous variables.}, notes = {Also known as \cite{2001899} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Fan:2011:GECCOcomp, author = {Kai-Chun Fan and Tian-Li Yu and Jui-Ting Lee}, title = {Interaction detection by NFE estimation: a practical view of building blocks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {71--72}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001900}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-variate estimation of distribution algorithms (EDAs) build models via detecting interactions between genes and estimate the distributions to solve problems. EDAs have been applied for real world applications, but whether the models given by EDAs match what are really needed to solve the problems is yet unknown. This paper proposes using the number of function evaluation (Nfe) to measure the performance of models and defines the optimal model to be the one that consumes the fewest Nfe for EDAs to solve the problem. Then the building blocks (BBs) that construct the optimal model are defined as the correct BBs. The capabilities of some existing interaction-detection metrics are compared based on this definition. This paper also proposes a test problem by using Bezier curve. We find that all the mentioned metrics fail to identify the correct BBs for the proposed problems intrinsically. This paper then proposes a new metric directly based on the idea of Nfe to enhance the existing interaction-detection mechanisms. Empirical results show that the new metric is able to build the optimal models. The preliminary success suggests another view on learning linkage.}, notes = {Also known as \cite{2001900} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Salehi:2011:GECCOcomp, author = {Elham Salehi and Robin Gras}, title = {Efficient EDA for large opimization problems via constraining the search space of models}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Estimation of distribution algorithms: Poster}, pages = {73--74}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001901}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Introducing efficient Bayesian learning algorithms in Bayesian network based EDAs seems necessary in order to use them for large problems. In this paper we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) [3] in order to constraint the models search space. This algorithm does not consider a fix and small upper bound on the order of interaction between variables and is able solve problems with large number of variables efficiently. We compare the efficiency of CMSS-BOA with standard Bayesian network based EDA for solving several benchmark problems.}, notes = {Also known as \cite{2001901} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Mashwani:2011:GECCOcomp, author = {Wali Khan Mashwani}, title = {Integration of NSGA-II and MOEA/D in multimethod search approach: algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {75--76}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001903}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Integration of single methods into their hybrids are researched scarcely in the recent few years. This paper presents the feasibility study for integration of two methods: MOEA/D [7] and NSGA-II [4] in the proposed multimethod search approach (MMTD). During implementation of MMTD, we borrows some concepts from the specialised literature of EMO. In MMTD, the synergistic combination of MOEA/D and NSGA-II can unleash their full power and strength self-adaptively for tackling two set of problems: 1) ZDT test problems [6], 2) cec09 unconstrained test instances [1]. The final best approximated results illustrates the usefulness of MMTD in multiobjective optimisation (MO).}, notes = {Also known as \cite{2001903} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wei:2011:GECCOcomp, author = {Jingxuan Wei and Mengjie Zhang}, title = {Attraction based PSO with sphere search for dynamic constrained multi-objective optimization problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {77--78}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001904}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Developing efficient algorithms for dynamic constrained multi-objective optimisation problems (DCMOPs) is very challenging. This paper describes an attraction based particle swarm optimisation (PSO) algorithm with sphere search for such problems. A dynamic constrained multi-objective optimization problem is transformed into a series of static constrained multi-objective optimization problems by dividing the time period into several equal intervals. To speed up optimization process and reuse the information of Pareto optimal solutions obtained from previous time, a new method based on sphere search is proposed to generate the initial swarm for the next time interval. To deal with the transformed problem effectively, a new particle comparison strategy is proposed for handling constraints in the problem. A local search operator based on the concept of attraction is introduced for finding good search directions of the particles. The results show that the proposed algorithm can effectively track the varying Pareto fronts with time.}, notes = {Also known as \cite{2001904} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pilat:2011:GECCOcomp, author = {Martin Pil\'{a}t and Roman Neruda}, title = {LAMM-MMA: multiobjective memetic algorithm with local aggregate meta-model}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {79--80}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001905}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we describe a multiobjective algorithm using local distance based meta-models. This algorithm is evaluated and compared to standard multiobjective evolutionary algorithms (MOEA) as well as to a similar algorithm with a global meta-model.}, notes = {Also known as \cite{2001905} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wu:2011:GECCOcomp, author = {Bing Wu and Weihua Sun and Yoshihiro Murata and Keiichi Yasumoto and Minoru Ito}, title = {A stamina-aware sightseeing tour scheduling method}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {81--82}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001906}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In general, a tour schedule is composed of multiple sightseeing spots taking into account the user' s preferences. However, during the tour, the stamina of the tourists may be exhausted. In this paper, we propose a sightseeing scheduling method that maximises the degree of user satisfaction taking stamina into account. In our method, break times are allocated in the schedule to satisfy the stamina constraint. Since this problem implies a TSP and thus is NP-hard, it is difficult to solve in practical time. To calculate a semi-optimal solution in practical time, we propose a method that first composes a schedule visiting multiple sightseeing spots without considering stamina, and then, to recover stamina, allocates break times, based on a predatory search technique. To evaluate the proposed method, we compared our method through a simulation experiment with some conventional methods including a brute-force method. As a result, the proposed method composed a schedule in practical time whose expected degree of satisfaction was near the optimum.}, notes = {Also known as \cite{2001906} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lopez:2011:GECCOcomp, author = {Javier L\'{o}pez and Laura Lanzarini and Armando {De Giusti}}, title = {Evolutionary multiobjective optimization for emergency medical services}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {83--84}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001907}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, the use of evolutionary metaheuristics for the optimisation of emergency medical services (EMS) applied to a real-world case in Argentina is analysed. The problem requires the simultaneous optimisation of two opposing objectives -- reducing service delay time and minimizing the use of third-party medical vehicle. Therefore, a multiobjective technique was implemented. Several multiobjective techniques that had good results reported in the literature were assessed. The techniques that presented the best indicators in this case were selected. Also, a disturbance operator that improves the results found by the assessed algorithms was developed. The objectives were achieved. A process to dispatch medical vehicles to home medical services based on evolutionary computing was successfully carried out, maximising the use of the available installed capacity, improving response time rates and using a smaller amount of resources.}, notes = {Also known as \cite{2001907} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ma:2011:GECCOcomp, author = {Jingjing Ma and Yanhui Wang and Maoguo Gong and Licheng Jiao and Qingfu Zhang}, title = {Spatio-temporal data evolutionary clustering based on MOEA/D}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {85--86}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001908}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Clustering the data evolve with time, which is termed evolutionary clustering, is an emerging and important research area in recent literature of data mining, and it is very effective to cluster the dynamic data. It needs to consider two conflicting criteria. One is the snapshot quality function; the other is the history cost function. Most state-of-the-art methods combine these two objectives into one and apply a single objective optimisation method for optimizing it. In this paper, we propose a new evolutionary clustering approach by using a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to optimise these two conflicting functions in evolutionary k-means algorithm (EKM). The experimental results demonstrate that our algorithm significantly outperforms EKM.}, notes = {Also known as \cite{2001908} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Suardi:2011:GECCOcomp, author = {Stefano Suardi and Rajkumar Roy and Jorn Mehnen and Yoseph Tafesse Azene}, title = {Uncertainty based evolutionary optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {87--88}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001909}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a robust evolutionary optimisation approach for real life design problems characterised by uncertainty. The proposed approach handles uncertainty in the design space, as well as in the objective functions and constrains, thanks to a new Pareto dominance criterion based on the neighbourhood around a solution. The approach is applied on a gearbox design optimisation problem as a case study. A comparison between two approaches, robust Pareto dominance criterion and a preference based penalty function, for deal with noisy environment is done for highlight the strength of the proposed robust Pareto dominance criterion.}, notes = {Also known as \cite{2001909} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Brownlee:2011:GECCOcomp, author = {Alexander E.I. Brownlee and Jonathan A. Wright and Monjur M. Mourshed}, title = {A multi-objective window optimisation problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {89--90}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001910}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present an optimisation problem which seeks to locate the Pareto-optimal front of building window and shading designs minimising two objectives: projected energy use of the operational building and its construction cost. This problem is of particular interest because it has many variable interactions and each function evaluation is relatively time-consuming. It also makes use of a freely-available building simulation program EnergyPlus which may be used in many other building design optimisation problems. We describe the problem and report the results of experiments comparing the performance of a number of existing multi-objective evolutionary algorithms applied to it. We conclude that this represents a promising real-world application area.}, notes = {Also known as \cite{2001910} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Santana:2011:GECCOcomp, author = {Roberto Santana and Hossein Karshenas and Concha Bielza and Pedro Larranaga}, title = {Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {91--92}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001911}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper shows that statistical algorithms proposed for the quantitative trait loci (QTL) mapping problem, and the equation of the multivariate response to selection can be of application in multi-objective optimisation. We introduce the conditional dominance relationships between the objectives and propose the use of results from QTL analysis and G-matrix theory to the analysis of multi-objective evolutionary algorithms (MOEAs).}, notes = {Also known as \cite{2001911} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tulshyan:2011:GECCOcomp, author = {Rupesh Tulshyan and Kalyanmoy Deb and Sunith Bandaru}, title = {KKT proximity measure for testing convergence in smooth multi-objective optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {93--94}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001912}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An earlier study defined a KKT-proximity measure to test the convergence property of an evolutionary algorithm for solving single-objective optimisation problems. In this paper, we extend this measure for testing convergence of a set of non-dominated solutions to the Pareto-optimal front in the case of smooth multi-objective optimization problems. Simulation results of NSGA-II on different two and three objective test problems indicate the suitability of using the proximity measure as a convergence metric for terminating a simulation of an evolutionary multi-criterion optimisation algorithm.}, notes = {Also known as \cite{2001912} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{AlMoubayed:2011:GECCOcomp, author = {Noura {Al Moubayed} and Andrei Petrovski and John McCall}, title = {Clustering based leaders' selection in multi-objective evolutionary algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {95--96}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001913}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Clustering-based Leaders Selection (CLS) is a novel leaders selection technique in multi-objective evolutionary algorithms. Clustering is applied on both the objective and solution spaces whereby each individual is assigned to two clusters; one in the objective space and the other in the solution space. Mapping between clusters in both spaces is then applied to recognise regions with potentially better solutions. A leaders archive is used where a representative of each cluster in the objective and solution spaces is stored. The results of applying CLS integrated with NSGAII on seven standard multi-objective problems, show that clustering based leaders selection NSGAII (NSGAII/C) is highly competitive comparing with the original algorithm.}, notes = {Also known as \cite{2001913} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ghandar:2011:GECCOcomp, author = {Adam Mostafa Ghandar and Zbigniew Michalewicz}, title = {Considerations of the nature of the relationship between generalization and interpretability in evolutionary fuzzy systems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {97--98}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001914}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Performance out of sample is a clear determinant of the usefulness of any prediction model regardless of the application. Fuzzy knowledge base systems are also useful due to interpretability; this factor is often cited as an advantage over black box systems which make model verification by expert users more difficult. Here we examine additional advantages of interpretability for promoting general performance out side training data.}, notes = {Also known as \cite{2001914} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Li:2011:GECCOcomp, author = {Ke Li and Sam Kwong and Kim-Fung Man}, title = {JGBL paradigm: a novel strategy to enhance the exploration ability of nsga-ii}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {99--100}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001915}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {NSGA-II is one of the most efficient multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimisation problems (MOPs). In this paper, a Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II. JGBL paradigm simulates the natural behaviour of maize and is incorporated into the framework of the original NSGA-II. It only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance. The activation of JGBL operation is entirely adapted online according to the search status of evolutionary process to give a needed fuel when the population evolves slowly with inherent variation operators.}, notes = {Also known as \cite{2001915} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bueno:2011:GECCOcomp, author = {Marcos Luiz de Paula Bueno and Gina Maira Barbosa de Oliveira}, title = {Adaptive shifting of auxiliary strategies over three formulations of multicast routing problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {101--102}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001916}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multicast routing algorithms have recently been intensively investigated due to the increment over the last years in the use of new point-to-multipoint applications. In this work, three formulations for the routing problem are investigated, considering 3, 4 and 5 objectives related to Quality of Service and Traffic Engineering requirements. A multiobjective evolutionary model is proposed to tackle this problem, using the well-known SPEA2 scheme as the underlying search. The key investigation performed here is about the incorporation of two strategies to help SPEA2 convergence to Pareto solutions, namely, filtering to reduce repeated individuals, and a mating selection based on neighbourhood crossover. Results indicate that the adequacy of the strategies depends on the dynamics of currently non-dominated set over the generations. A new adaptive environment is proposed in which this information is considered periodically to decide what kind of strategy will be used in each situation.}, notes = {Also known as \cite{2001916} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zou:2011:GECCOcomp, author = {Wenping Zou and Yunlong Zhu and Hanning Chen and Hai Shen}, title = {A novel multi-objective optimization algorithm based on artificial bee colony}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {103--104}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001917}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Multi-objective optimisation has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on Artificial Bee Colony (ABC) to deal with multi-objective optimisation problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behaviour of a honey bee swarm. It uses less control parameters and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems ZDT1 to ZDT3 and ZDT6, and simulation results show that the proposed approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.}, notes = {Also known as \cite{2001917} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tantar:2011:GECCOcomp, author = {Alexandru-Adrian Tantar and Emilia Tantar and Pascal Bouvry}, title = {A classification of dynamic multi-objective optimization problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {105--106}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001918}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A classification of dynamic multi-objective optimization problems is proposed in this article. As compared to previous studies, we focus not on the changes or the effects that are induced in the Pareto optimal front or set but on the components that lead to the observed dynamic behaviour. Four main classes are identified, including parameter and function time-dependent evolution as well as state-dependent parameter and function transforms or environment changes.}, notes = {Also known as \cite{2001918} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Walker:2011:GECCOcomp, author = {David J. Walker and Richard M. Everson and Jonathan E. Fieldsend}, title = {Rank-based dimension reduction for many-criteria populations}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {107--108}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001919}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Interpreting individuals described by a set of criteria can be a difficult task when the number of criteria is large. Such individuals can be ranked, for instance in terms of their average rank across criteria as well as by each distinct criterion. We therefore investigate criteria selection methods which aim to preserve the average rank of individuals but with fewer criteria. Our experiments show that these methods perform effectively, identifying and removing redundancies within the data, and that they are best incorporated into a multi-objective algorithm.}, notes = {Also known as \cite{2001919} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Grimme:2011:GECCOcompp, author = {Christian Grimme and Joachim Lepping}, title = {Integrating niching into the predator-prey model using epsilon-constraints}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolutionary multiobjective optimization: Poster}, pages = {109--110}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001920}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Predator Prey Model (PPM) for multi-objective evolutionary optimization features a simple abstraction from natural species interplay: predators represent different objectives and collectively hunt for prey solutions which have to adapt to all predators in order to survive. In this work, we start from previous insights to motivate significant changes in predators by enabling adaptation of selection behaviour. For this, we integrate aspects of the epsilon-Constraint method into the PPM mechanisms. Our results show that this model extension results in good Pareto-fronts for bi-objective test problems.}, notes = {Also known as \cite{2001920} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gong:2011:GECCOcomp, author = {Wenyin Gong and Zhihua Cai}, title = {Adaptive parameter selection for strategy adaptation in differential evolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolution strategies and evolutionary programming: Poster}, pages = {111--112}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001922}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to automatically select the most suitable strategy for a specific problem without any prior knowledge, in this paper, we present an adaptive parameter selection technique for strategy adaptation in differential evolution (DE). First, a simple strategy adaptation mechanism is employed to implement the adaptive strategy selection in DE. Then, the probability- matching-based adaptive parameter selection method is proposed to select the best parameter of the strategy adaptation mechanism; in this way, it can accelerate the strategy adaptation mechanism to choose the most suitable strategy while solving a problem. Experimental results indicate that our method obtains better results in terms of the quality of the final solutions and the convergence speed, compared with the classical DE algorithms with single strategy.}, notes = {Also known as \cite{2001922} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Guerrero:2011:GECCOcomp, author = {Jos\'{e} Luis Guerrero and Antonio Berlanga and Jos\'{e} Manuel Molina}, title = {A robust memetic algorithm with self-stopping capabilities}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolution strategies and evolutionary programming: Poster}, pages = {113--114}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001923}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms exhibit some traditional handicaps: lack of a stopping criterion, slow convergence towards the minimum, etc. Memetic algorithms try to combine the best exploration qualities of population based approaches with the exploitation qualities of local search ones. The proposed solution in this work, Robust Evolutionary Strategy Learnt with Automated Termination Criteria (R-ESLAT) uses a memetic approach, combining an evolutionary strategy with derivative-free local search methods, adding as well a termination criteria based on the population diversity, according to the principles of the original ESLAT algorithm. The original algorithm is analysed and its features improved towards an increased robustness, comparing the results obtained with the Covariance Matrix Adaptation Evolutionary Strategy (CMAES).}, notes = {Also known as \cite{2001923} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cuesta-Infante:2011:GECCOcomp, author = {Alfredo Cuesta-Infante and Jos\'{e} Ignacio Hidalgo and Mar\'{\i}a Victoria Rivas}, title = {(1+2)-evolution strategy for fitting a straight shuffle of min to a dataset}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolution strategies and evolutionary programming: Poster}, pages = {115--116}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001924}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes an Evolution Strategy to find a shuffle of M that suits to a batch of datasets that serve as benchmark. Results are compared with wavelet estimation.}, notes = {Also known as \cite{2001924} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Boumaza:2011:GECCOcomp, author = {Amine Boumaza}, title = {Designing artificial tetris players with evolution strategies and racing}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolution strategies and evolutionary programming: Poster}, pages = {117--118}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001925}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This article describes how racing procedures in evolution strategies can help reduce the number of evaluations. This idea is illustrated on learning Tetris players which can be addressed as a stochastic optimisation problem. Different experiments show the benefits of the racing procedures in evolution strategies which can significantly reduce the number of evaluations.}, notes = {Also known as \cite{2001925} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bartz-Beielstein:2011:GECCOcomp, author = {Thomas Bartz-Beielstein and Martina Friese and Martin Zaefferer and Boris Naujoks and Oliver Flasch and Wolfgang Konen and Patrick Koch}, title = {Noisy optimization with sequential parameter optimization and optimal computational budget allocation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Evolution strategies and evolutionary programming: Poster}, pages = {119--120}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001926}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Sequential parameter optimization (SPO) is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. In this study, SPO is directly used as an optimization method on different noisy mathematical test functions. SPO includes a broad variety of meta models, which can have significant impact on its performance. Additionally, Optimal Computing Budget Allocation (OCBA), which is an enhanced method for handling the computational budget spent for selecting new design points, is presented. The OCBA approach can intelligently determine the most efficient replication numbers. Moreover, we study the of performance of different meta models being integrated in SPO. Our results reveal that the incorporation of OCBA and the selection of Gaussian process models are highly beneficial. SPO outperformed three different alternative optimization algorithms on a set of five noisy mathematical test functions.}, notes = {Also known as \cite{2001926} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ibrahimov:2011:GECCOcomp, author = {Maksud Ibrahimov and Arvind Mohais and Sven Schellenberg and Zbigniew Michalewicz}, title = {Comparison of cooperative, multiobjective cooperative and classical evolutionary algorithms for global supply chain optimisation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {121--122}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001928}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and three approaches were used for global optimisation: a classical evolutionary approach, a cooperative coevolutionary approach and a cooperative coevolutionary approach with non-dominated partner selection. The second approach produced higher quality solutions due to its use of communication between silos.}, notes = {Also known as \cite{2001928} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wilkerson:2011:GECCOcompF, author = {Josh L. Wilkerson and Daniel R. Tauritz}, title = {A guide for fitness function design}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {123--124}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001929}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Fitness function design is often both a design and performance bottleneck for evolutionary algorithms. The fitness function for a given problem is directly related to the specifications for that problem. This paper outlines a guide for transforming problem specifications into a fitness function. The target audience for this guide are both non-expert practitioners and those interested in formalising fitness function design. The goal is to investigate and formalise the fitness function generation process that expert developers go through and in doing so make fitness function design less of a bottleneck. Solution requirements in the problem specifications are identified and classified, then an appropriate fitness function component is generated based on its classifications, and finally the fitness function components combined to yield a fitness function for the problem in question. The competitive performance of a guide generated fitness function is demonstrated by comparing it to that of an expert designed fitness function.}, notes = {Also known as \cite{2001929} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Matayoshi:2011:GECCOcomp, author = {Mitsukuni Matayoshi}, title = {A new packing method for two dimensional rectilinear polygons using genetic algorithm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {125--126}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001930}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a new placement method for two dimensional rectilinear polygons. The proposed method is based on the idea of corner junction method and uses only genetic algorithm approach with a new hierarchical chromosome structure to pack rectilinear polygons on container. Experimental results show the proposed method is succeeded in placement of some complicated two dimensional rectilinear polygons in feasible time.}, notes = {Also known as \cite{2001930} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wang:2011:GECCOcomp, author = {Guan Wang and Degang Wu and Wenjin Chen and Kwok Yip Szeto}, title = {Importance of information exchange in quasi-parallel genetic algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {127--128}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001931}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we make a brief study on the effect of exchange rate in quasi-parallel genetic algorithms. The exchange rate is determined by two elements: the communication topology of the parallel populations and the communication capacity on each link. Here we formulate the communication capacity as the number of chromosomes one population exchanges with its neighbours. To study the effect of the two elements of exchange rate separately we did some tests on the minimisation of the Weierstrass Function. Our results show that topology with a larger number of exchanged chromosomes generally yields better performance.}, notes = {Also known as \cite{2001931} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Spector:2011:GECCOposter, author = {Lee Spector and Thomas Helmuth and Kyle Harrington}, title = {Fecundity and selectivity in evolutionary computation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {129--130}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001932}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The number of offspring produced by each parent---that is, the fecundity of reproducing individuals---varies among evolutionary computation methods and settings. In most prior work fecundity has been tied directly to selectivity, with higher selection pressure giving rise to higher fecundity among individuals selected to reproduce. In nature, however, there is a wider variety of strategies, with different organisms producing different numbers of offspring under the influence of a range of factors including not only selection pressure but also other factors such as environmental stability and competition within a niche. In this work we consider possible lessons that may be drawn from nature's approaches to these issues and applied to evolutionary computation systems. In particular, we consider ways in which fecundity can be dissociated from selectivity and situations in which it may be beneficial to do so. We present a simple modification to the standard evolutionary algorithm, called decimation, that permits high fecundity in conjunction with modest selection pressure and which could be used in various forms of evolutionary computation. We also present a simple example, showing that decimation can improve the problem-solving performance of a genetic algorithm when applied to a deceptive problem.}, notes = {Also known as \cite{2001932} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Beckmann:2011:GECCOcomp, author = {Marcelo Beckmann and Beatriz Souza L.P. {de Lima} and Nelson F.F. Ebecken}, title = {Genetic algorithms as a pre processing strategy for imbalanced datasets}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {131--132}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001933}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In data mining, the traditional classification algorithms tend to loose its predictive capacity when applied on a dataset which distribution between classes is imbalanced. This work aims to present a new methodology using genetic algorithms, in order to create synthetic instances from the minority class. The experiments with the proposed methodology demonstrated a better classification performance in most of the problems, in comparison with other work in the specific literature.}, notes = {Also known as \cite{2001933} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Moraglio:2011:GECCOcomp, author = {Alberto Moraglio and Yong-Hyuk Kim and Yourim Yoon}, title = {Geometric surrogate-based optimisation for permutation-based problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {133--134}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001934}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In continuous optimisation, surrogate models (SMs) are used when tackling real-world problems whose candidate solutions are expensive to evaluate. In previous work, we showed that a type of SMs - radial basis function networks (RBFNs) - can be rigorously generalised to encompass combinatorial spaces based in principle on any arbitrarily complex underlying solution representation by extending their natural geometric interpretation from continuous to general metric spaces. This direct approach to representations does not require a vector encoding of solutions, and allows us to use SMs with the most natural representation for the problem at hand. In this work, we apply this framework to combinatorial problems using the permutation representation and report experimental results on the quadratic assignment problem.}, notes = {Also known as \cite{2001934} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Parkinson:2011:GECCOcomp, author = {Eddy Parkinson and Adam Ghandar and Zbigniew Michalewicz and Andrew Tuson}, title = {Controlling the tradeoff between time and quality by considering the reproductive potential of offspring}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {135--136}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001935}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {To improve evolutionary algorithm performance, this paper proposes a strategy to aid ascent and to help avoid premature convergence. Rapid increases in population fitness may result in premature convergence and sub optimal solution. A thresholding mechanism is proposed which discards child solutions only if their fitnesses are either too bad, in which case they are discarded, nor too good, in which case they pose the danger of premature convergence. This strategy is evaluated using two combinatorial optimisation problems: the classic TSP benchmark and the more constrained vehicle routing problem (VRP) benchmark. The idea offers a relatively straight forward method for adding value by improving both runtime or solution quality. We consider a stochastic hill climber and a population based heuristic (an evolutionary algorithm).}, notes = {Also known as \cite{2001935} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{deLima:2011:GECCOcomp, author = {Telma Woerle {de Lima} and Alexandre Claudio Botazzo Delbem and Franz Rothlauf}, title = {Analysis of properties of recombination operators proposed for the node-depth encoding}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {137--138}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001936}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The node-depth encoding is a representation for evolutionary algorithms applied to tree problems. Its represents trees by storing the nodes and their depth in a proper ordered list. The original formulation of the node-depth encoding has only mutation operators as the search mechanism. Although it is computationally efficient, the exclusive use of mutation restricts the exploration of the search space and the algorithm convergence. Then, this work proposes two specific recombination operators to improve the convergence of the algorithm using the node-depth encoding representation. These operators are based on recombination operators for permutation representations. Analysis of the proposed recombination operators have shown that both operators have a bias towards stars and high heritability.}, notes = {Also known as \cite{2001936} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tserenchimed:2011:GECCOcomp, author = {Badarch Tserenchimed and Shu Liu and Hitoshi Iba}, title = {A trading method in FX using evolutionary algorithms: extensions based on reverse trend and settlement timing}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {139--140}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001937}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In foreign exchange (FX) markets, the key issues to achieve profitable trading rules are the combination of the indicators, selection of their parameters, and decision of the trade timing for orders and settlements. In this paper, we present a trading system using a combination of genetic algorithm (GA) and genetic programming (GP). Unlike related researches on this problem, our work focuses on two aspects. First, a calculation of appropriate settlement timing is proposed, to make more profits and less losses. Second, reverse trend data are generated using in-sample data, to overcome the over fitting problem and suppress the risk of loss. To examine the effectiveness of the method, we employed simulations using real-world trading intraday data. It is verified the enhanced capability of our method to make consistent gain out-of-sample and avoid large draw-downs.}, notes = {Also known as \cite{2001937} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Shi:2011:GECCOcomp, author = {Min Shi}, title = {Empirical analysis of cooperative coevolution using blind decomposition}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {141--142}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001938}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {How to decompose problems effectively and how to select appropriate collaboration methods for problems with particular separabilities have been discussed in Cooperative Coevolutionary Algorithms (CCEAs) research for many years. In most of the previous work, the prior knowledge of decomposition about the problems is obtainable. However, there could be some real problems that the information to conduct the decomposition of the problems is unclear. This paper offers a solution by decomposing the problems in a blind way. We provide an analysis if the blind decomposition is feasible and give some basic advice on how to implement the blind decomposition.}, notes = {Also known as \cite{2001938} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Yang:2011:GECCOcomp, author = {Ming Yang and Zhihua Cai and Jing Guan and Wenyin Gong}, title = {Differential evolution with improved population reduction}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {143--144}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001939}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the Differential Evolution (DE), there are many adaptive DE algorithms proposed for parameter adaptation. However, they are mainly focus on the the mutation factor F and crossover probability CR. The adaptation of population size NP is not widely studied in the scope of DE. If reduce population size but not jeopardise performance of the algorithm significantly, it could reduce the number of evaluations for individuals and accelerate algorithm's convergence speed. This is beneficial to the optimisation problems which need expensive evaluations. In this paper, we propose an improved population reduction method, considering the difference between individuals, and embed it into classic DE/rand/1/bin strategy, named dynNPMinD-DE. When population needs to reduce, select the best individual and the individuals with minimal-step difference vectors to form a new population. dynNPMinD-DE is applied to minimise a set of 13 scalable benchmark functions of dimensions D=30. The results show that compared with selecting better individuals and DE/rand/1/bin, dynNPMinD-DE can get better results on average, and the convergence becomes faster and faster as each population reduction.}, notes = {Also known as \cite{2001939} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lutton:2011:GECCOcomp, author = {Evelyne Lutton and Jean-Daniel Fekete}, title = {Visual analytics of EA data}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {145--146}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001940}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An experimental analysis of evolutionary algorithms usually generates a huge amount of multidimensional data, including numeric and symbolic data. It is difficult to efficiently navigate in such a set of data, for instance to be able to tune the parameters or evaluate the efficiency of some operators. Usual features of existing EA visualisation systems consist in visualising time- or generation-dependent curves (fitness, diversity, or other statistics). When dealing with genomic information, the task becomes even more difficult, as a convenient visualisation strongly depends on the considered fitness landscape. In this latter case the raw data are usually sets of successive populations of points of a complex multidimensional space. The purpose of this paper is to evaluate the potential interest of a recent visual analytics tool for navigating in complex sets of EA data, and to sketch future developements of this tool, in order to better adapt it to the needs of EA experimental analysis.}, notes = {Also known as \cite{2001940} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Meekhof:2011:GECCOcomp, author = {Timothy Meekhof and Terence Soule}, title = {Noise, fitness distribution, and selection intensity in genetic algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {147--148}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001941}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many Genetic Algorithm (GA) problems have noisy fitness functions. In this paper, we describe a mathematical model of the noise distribution after selection and then show how this model of the noise distribution can be used to model the real, underlying selection intensity of the GA population, which promises to give us a better way to model GA convergence in the presence of noise.}, notes = {Also known as \cite{2001941} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Sher:2011:GECCOcomp, author = {Gene Sher}, title = {DXNN: evolving complex organisms in complex environments using a novel tweann system}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {149--150}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001942}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The goal of this paper is twofold. First, to briefly present a novel type of memetic algorithm based Topology and Weight Evolving Artificial Neural Network (TWEANN) system called DXNN, among whose numerous novel features are: a simple and database friendly tuple based NN encoding method, a two phase neuroevolutionary approach which produces high diversity populations, a new Targeted Tuning Phase aimed at dealing with the curse of dimensionality, and a new Random Intensity Mutation (RIM) method that removes the need for cross-over algorithms. Second, to discuss the excellent experimental results of applying DXNN to co-evolutionary artificial life simulations.}, notes = {Also known as \cite{2001942} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Luke:2011:GECCOcomp, author = {Sean Luke and Keith Sullivan and Faisal Abidi}, title = {Large scale empirical analysis of cooperative coevolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {151--152}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001943}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a study of cooperative coevolution applied to moderately complex optimisation problems in large-population environments. The study asks three questions. First: what collaboration methods perform best, and when? Second: how many subpopulations are desirable? Third: is it worthwhile to do more than one trial per fitness evaluation? We discovered that parallel methods tended to work better than sequential ones, that shuffling (a collaboration method) predominated in performance in more complex problems, that more subpopulations generally did better, and that more trials performed marginally better.}, notes = {Also known as \cite{2001943} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Manso:2011:GECCOcomp, author = {Ant\'{o}nio Manuel Rodrigues Manso and Lu\'{\i}s Miguel Parreira Correia}, title = {A multiset genetic algorithm for real coded problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {153--154}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001944}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Multiset Genetic Algorithm (MuGA) was adapted to real coded problems, tested in benchmark functions, and compared to competitive algorithms. Genetic operators were adapted to take into account the multiset representation of the population, which is the main distinctive feature and advantage of MuGA. The new operators extend existing ones, incorporating the influence of the number of copies each multi-individual has. Preliminary results obtained, without particular tuning efforts, position MuGA close to the best results obtained by other approaches. Future work will improve limitations found in maintaining a high genetic diversity.}, notes = {Also known as \cite{2001944} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Goldman:2011:GECCOposter, author = {Brian W. Goldman and Daniel R. Tauritz}, title = {Meta-evolved empirical evidence of the effectiveness of dynamic parameters}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {155--156}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001945}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Traditional evolutionary algorithms (EAs) are powerful problem solvers that have several fixed parameters which require tuning. An increasing body of evidence suggests that the optimal values of some, if not all, EA parameters change during the course of executing an evolutionary run. This paper investigates the potential benefits of dynamic parameters by applying a Meta-EA to evolving optimal dynamic parameter values for population size, offspring size, n in n-point crossover, Gaussian mutation's step size, bit flip mutation's mutation rate, parent selection tournament size, and survivor selection tournament size. Each parameter was optimised both as the only dynamic parameter, and with all parameters dynamic. The most effective two parameters when acting independently were also allowed to optimise in tandem. The results were compared with a Meta-EA tuned EA using static parameters on the DTrap, NK, Rastrigin, and Rosenbrock benchmark problems. Results support that all tested parameters have the potential to improve solution fitness by changing dynamically, and using multiple dynamic parameters was more effective than using each independently.}, notes = {Also known as \cite{2001945} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Son:2011:GECCOcomp, author = {Yu-Min Son and Byung-Ro Moon}, title = {Raising mutation rate in the context of hybrid genetic algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {157--158}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001946}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A typical genetic algorithm uses a constant mutation rate or reduces the mutation rate over the generation. Generally, the degree of perturbation by crossover operators gets weaker as the generations go by. Hybrid GAs, which use a local optimization heuristic, strongly drive the offspring to a chromosome similar to or the same as one of the parents. We suspect that one needs to raise the degree of mutation in the late stages of a hybrid GA, contrary to the practice. The experimental results supported our suspection, by showing performance improvement over two philosophically representative mutations: the traditional fixed-rate mutation and the non-uniform mutation. We used two representative NP-hard problems in the experiments: the graph bisection problem and the travelling salesman problem.}, notes = {Also known as \cite{2001946} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zhong:2011:GECCOcomp, author = {Jing-hui Zhong and Jun Zhang}, title = {A multi-objective memetic algorithm for relay node placement in wireless sensor network}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {159--160}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001947}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a multi-objective memetic algorithm, namely Mem-NSGA-II, to optimise the relay node placement problem. The network lifetime and the number of relay nodes are two objectives to be optimised. In Mem-NSGA-II, three new local search (LS) operations are designed and incorporated into the fast non-dominated genetic algorithm II (NSGA-II). The first LS operation inserts relay nodes into solutions for extending the network lifetime. The second LS operation aims to reduce the number of relay nodes to save cost. The third LS operation fine-tunes positions of the relay nodes for finding better solutions. The performance of Mem-NSGA-II is compared with a deterministic two-phased method and NSGA-II. Simulation results on five networks reveal that Mem-NSGA-II yields much better performance than the two algorithms.}, notes = {Also known as \cite{2001947} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Castelli:2011:GECCOcomp, author = {Mauro Castelli and Luca Manzoni and Leonardo Vanneschi}, title = {The effect of selection from old populations in genetic algorithms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetic algorithms: Poster}, pages = {161--162}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001948}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper a method to increase the optimisation ability of genetic algorithms (GAs) is proposed. To promote population diversity, a fraction of the worst individuals of the current population is replaced by individuals from an older population. To experimentally validate the approach we have used a set of well-known benchmark problems of tunable difficulty for GAs, including trap functions and NK landscapes. The obtained results show that the proposed method performs better than standard GAs without elitism for all the studied test problems and better than GAs with elitism for the majority of them.}, notes = {Also known as \cite{2001948} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Peralta-Donate:2011:GECCOcompT, author = {Juan {Peralta Donate} and Paulo Cortez and Araceli {Sanchis de Miguel} and German {Gutierrez Sanchez}}, title = {Evolving time-lagged feedforward neural networks for time series forecasting}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {163--164}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001950}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Time Series Forecasting (TSF) is an important tool to support both individual and organizational decisions. In this work, we propose a novel automatic Evolutionary Time-Lagged Feedforward Network (ETLFN) approach for TSF, based on an Estimation Distribution Algorithm (EDA) that evolves not only Artificial Neural Network (ANN) parameters but also which set of time lags are fed into the forecasting model. Such approach is compared with similar strategy that only selects ANN parameter and the conventional TSF ARIMA methodology. Several experiments were held by considering six time series from distinct domains. The obtained multi-step ahead forecasts were evaluated using SMAPE error criteria. Overall, the proposed ETLFN method obtained the best forecasting results. Moreover, it favours simpler neural network models, thus requiring less computational effort.}, notes = {Also known as \cite{2001950} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Shimada:2011:GECCOcomp, author = {Kaoru Shimada and Kotaro Hirasawa}, title = {Evolving associative classifier for incomplete database using genetic network programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {165--166}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001951}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An evolving classification method for incomplete database has been proposed as an extension of Genetic Network programming (GNP) based rule extraction. An incomplete database includes missing values, however, the method can extract class association rules and build a classifier. The proposed method evolves the classifier using the labelled instances by itself as acquired information. We have evaluated the performance of the proposed method using artificial incomplete data set. The results showed that the proposed method has a potential of gathering useful information for classification through its evolutionary process.}, notes = {Also known as \cite{2001951} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Preen:2011:GECCOcomp, author = {Richard J. Preen and Larry Bull}, title = {Fuzzy dynamical genetic programming in XCSF}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Genetics based machine learning: Poster}, pages = {167--168}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001952}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems.}, notes = {Also known as \cite{2001952} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Loiacono:2011:GECCOcomp, author = {Daniele Loiacono}, title = {Fast prediction computation in learning classifier systems using CUDA}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {169--170}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001953}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computing the system prediction is one of the most important and computationally expensive tasks in Learning Classifier Systems. In this paper, we provide a parallel solution to the problem of computing the prediction array in XCS using the NVIDIA's Compute Unified Device Architecture (CUDA). We performed several experiments to test our parallel solution using two different types of GPUs and to study how performances are affected by (i) the problem size, (ii) the number of problem actions, and (iii) the number of classifiers in the population. Our experimental results show a speedup that ranges from slightly less than 2X up to 32X.}, notes = {Also known as \cite{2001953} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Castillo-Ortega:2011:GECCOcomp, author = {Rita Castillo-Ortega and Nicol\'{a}s Mar\'{\i}n and Daniel S\'{a}nchez and Andrea G.B. Tettamanzi}, title = {A multi-objective memetic algorithm for the linguistic summarization of time series}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {171--172}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001954}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Time series in time domains with a hierarchical structure may be summarised by means of sets of quantified fuzzy sentences of the form 'Q of D is A', where Q is a quantifier, D is a linguistic time interval, and A is a linguistic value. Finding concise and accurate summaries that cover the whole time domain is a hard optimisation problem, that we solve by proposing a multi-objective memetic algorithm based on NSGA-II with the addition of a number of intelligent mutation operators that apply heuristics to improve solutions.}, notes = {Also known as \cite{2001954} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{DeStefano:2011:GECCOcomp, author = {Claudio {De Stefano} and Gianluigi Folino and Francesco Fontanella and Alessandra {Scotto di Freca}}, title = {Using bayesian networks for selecting classifiers in GP ensembles}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {173--174}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001955}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present a novel approach for combining GP-based ensembles by means of a Bayesian Network. The proposed system is able to effectively learn decision tree ensembles using two different strategies: decision trees ensembles are learnt by means of boosted GP algorithm; the responses of the learned ensembles are combined using a Bayesian network, which also implements a selection strategy that reduces the size of the built ensembles.}, notes = {Also known as \cite{2001955} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Trujillo:2011:GECCOcomp, author = {Leonardo Trujillo and Yuliana Mart\'{\i}nez and Patricia Melin}, title = {How many neurons?: a genetic programming answer}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Genetics based machine learning: Poster}, pages = {175--176}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001956}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The goal of this paper is to derive predictive models that take as input a description of a problem and produce as output an estimate of the optimal number of hidden nodes in an Artificial Neural Network (ANN). We call such computational tools Direct Estimators of Neural Network Topology (DENNT), an use Genetic Programming (GP) to evolve them. The evolved DENNTs take as input statistical and complexity descriptors of the problem data, and output an estimate of the optimal number of hidden neurons.}, notes = {Also known as \cite{2001956} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Niekum:2011:GECCOcomp, author = {Scott Niekum and Lee Spector and Andrew Barto}, title = {Evolution of reward functions for reinforcement learning}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Genetics based machine learning: Poster}, pages = {177--178}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001957}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In some problem domains, however, alternative reward functions may allow systems to learn more quickly or more effectively. Here we describe work on the use of genetic programming to find novel reward functions that improve learning system performance. We briefly present the core concepts of our approach, our motivations in developing it, and reasons to believe that the approach has promise for the production of highly successful adaptive technologies. Experimental results are presented and analysed in our full report [3].}, notes = {Also known as \cite{2001957} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ye:2011:GECCOcomp, author = {Fengming Ye and Shingo Mabu and Kotaro Hirasawa}, title = {A memory scheme for genetic network programming with adaptive mutation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, genetic network programming, Genetics based machine learning: Poster}, pages = {179--180}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001958}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently, a new approach named Genetic Network Programming (GNP) has been proposed for especially solving complex problems in dynamic environments. In this paper, we propose a memory scheme for GNP to enhance the performance of GNP and use SARSA learning based adaptive mutation mechanism to guide the GNP evolution process.}, notes = {Also known as \cite{2001958} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Kukenys:2011:GECCOcomp, author = {Ignas Kukenys and Will N. Browne and Mengjie Zhang}, title = {Confusion matrices for improving performance of feature pattern classifier systems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Genetics based machine learning: Poster}, pages = {181--182}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001959}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning Classifier Systems (LCS) have not been widely applied to image recognition tasks due to the very large search space of pixel data. Assimilating the image domain's Haar-like features into the XCS framework, the feature pattern classifier system (FPCS) has produced promising results in the numeral recognition task. However for large multi-class image classification problems the training rates can be unacceptably slow, whilst performance does not match supervised learning approaches. This is partially due to the fact that traditional LCS only retain limited information about the problem examples. Confusion Matrices show the classes that a learning technique has difficulty separating, but require supervised knowledge. This paper shows that the knowledge in a confusion matrix is beneficial in directing learning. Most importantly the work shows that confusion matrices can be beneficially adapted to non-supervisory learning.}, notes = {Also known as \cite{2001959} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Fagan:2011:GECCOposter, author = {David Fagan and Miguel Nicolau and Erik Hemberg and Michael O'Neill and Anthony Brabazon}, title = {Dynamic ant: introducing a new benchmark for genetic programming in dynamic environments}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {183--184}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001961}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present a new variant of the Ant Problem in the Dynamic Problem Domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.}, notes = {Also known as \cite{2001961} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Rodriguez-Rafael:2011:GECCOcomp, author = {Glen D. {Rodriguez Rafael} and Carlos Javier {Solano Salinas}}, title = {Empirical study of surrogate models for black box optimizations obtained using symbolic regression via genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {185--186}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001962}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A black box model is a numerical simulation that is used in optimisation. It is computationally expensive, so it is convenient to replace it with surrogate models obtained by simulating only a few points and then approximating the original black box. Here, a recent approach, using Symbolic Regression via Genetic Programming, is compared experimentally to neural network based surrogate models, using test functions and electromagnetic models. The accuracy of the model obtained by Symbolic Regression is proved to be good, and the interpretability of the function obtained is useful in reducing the optimisation's search space.}, notes = {Also known as \cite{2001962} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gardner:2011:GECCOcomp, author = {Marc-Andr\'{e} Gardner and Christian Gagn\'{e} and Marc Parizeau}, title = {Bloat control in genetic programming with a histogram-based accept-reject method}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {187--188}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001963}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalisation (DynOpEq) aim at modifying the tree size distribution in a population of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic Programming (GP) trees, and then rejecting most of them, leading to an important waste of computational resources. We are proposing a method that makes a histogram-based model of current GP tree size distribution, and uses the so-called accept-reject method for generating a population with the desired target size distribution, in order to make a stochastic control of bloat in the course of the evolution. Experimental results show that the method is able to control bloat as well as other state-of-the-art methods, with minimal additional computational efforts compared to standard tree-based GP.}, notes = {Also known as \cite{2001963} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Camargo-Bareno:2011:GECCOcomp, author = {Carlos Ivan {Camargo Bareno} and Cesar Augusto {Pedraza Bonilla} and Luis Fernado Nino and Jose Ignacio {Martinez Torre}}, title = {Intrinsic evolvable hardware for combinatorial synthesis based on SoC+FPGA and GPU platforms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, GPU: Poster}, pages = {189--190}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001964}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a novel a parallel genetic programming (PGP) Boolean synthesis implementation on a low cost cluster of an embedded open platform called SIE. Some tasks of the PGP have been accelerated through a hardware coprocessor called FCU, that allows to evaluate individuals onchip as intrinsic evolution. Results have been compared with GPU and HPC implementations, resulting in speedup values up to approximately 2 and 180 respectively.}, notes = {Also known as \cite{2001964} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{O'Neill:2011:GECCOcomp, author = {Michael O'Neill and Miguel Nicolau and Anthony Brabazon}, title = {Dynamic environments can speed up evolution with genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {191--192}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001965}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental varying goals. An empirical investigation comparing these three types of varying goals on dynamic symbolic regression benchmarks reveals an advantage for goals which vary in terms of increasing structural complexity. This provides evidence to support the added difficulty variable length representations incur due to their requirement to search structural and parametric space concurrently, and how directing search through varying structural goals with increasing complexity can speed up search with genetic programming.}, notes = {Also known as \cite{2001965} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Augusto:2011:GECCOcomp, author = {Douglas A. Augusto and Helio J.C. Barbosa and Andre M.S. Barreto and Heder S. Bernardino}, title = {A new approach for generating numerical constants in grammatical evolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {193--194}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001966}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant creation are performed. By varying the number of bits to represent a constant, we can increase our method's precision to the desired level of accuracy, overcoming by a large margin the other approaches.}, notes = {Also known as \cite{2001966} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Winkler:2011:GECCOposter, author = {Stephan M. Winkler and Michael Affenzeller and Stefan Wagner}, title = {Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {195--196}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001967}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we use a formalism for estimating the structural similarity of formulae for measuring the genetic diversity among GP populations. As we show in the results section of this paper, population diversity differs a lot in the test runs depending on the selection schemata used; especially the use of strict offspring selection has a significant effect on the progress of the population's diversity.}, notes = {Also known as \cite{2001967} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Harding:2011:GECCOcompQ, author = {Simon Harding and Julian F. Miller and Wolfgang Banzhaf}, title = {SMCGP2: finding algorithms that approximate numerical constants using quaternions and complex numbers}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming: Poster}, pages = {197--198}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001968}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Self Modifying Cartesian Genetic Programming 2 (SMCGP2) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. Using a combination of computational functions and special functions that can modify the phenotype at runtime, it has been employed to find general solutions to a number of computational problems. Here, we apply the new SMCGP technique to find mathematical relationships between well known mathematical constants (i.e. pi, e, phi, omega etc) using a variety of functions sets. Some of formulae obtained are distinctly unusual and may be unknown in mathematics.}, notes = {Also known as \cite{2001968} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Agapitos:2011:GECCOcomp, author = {Alexandros Agapitos and Michael O'Neill and Anthony Brabazon}, title = {Stateful program representations for evolving technical trading rules}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {199--200}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001969}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.}, notes = {Also known as \cite{2001969} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Downey:2011:GECCOcomp, author = {Carlton Downey and Mengjie Zhang}, title = {Caching for parallel linear genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {201--202}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001970}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Parallel Linear Genetic Programming (PLGP) is an exciting new approach to Linear Genetic Programming (LGP) which decreases building block disruption and significantly improves performance by the introduction of a parallel architecture. We introduce a caching algorithm for PLGP which exploits this parallel architecture to avoid the majority of instruction executions. This allows PLGP programs to be executed an order of magnitude faster than LGP programs with an equal number of instructions.}, notes = {Also known as \cite{2001970} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tuite:2011:GECCOcomp, author = {Cl\'{\i}odhna Tuite and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon}, title = {Early stopping criteria to counteract overfitting in genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution: Poster}, pages = {203--204}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001971}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Early stopping typically stops training the first time validation fitness disimproves. This may not be the best strategy given that validation fitness can subsequently increase or decrease. We examine the effects of stopping subsequent to the first disimprovement in validation fitness, on symbolic regression problems. Stopping points are determined using criteria which measure generalisation loss and training progress. Results suggest that these criteria can improve the generalistion ability of symbolic regression functions evolved using Grammar-based GP.}, notes = {Also known as \cite{2001971} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{langdon:2011:gecco, author = {William B. Langdon}, title = {Generalisation in genetic programming}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming: Poster}, pages = {205--206}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001972}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic programming can evolve large general solutions using a tiny fraction of possible fitness test sets. Just one test may be enough.}, notes = {Also known as \cite{2001972} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Soule:2011:GECCOcomp, author = {Terence Soule and Robert B. Heckendorn}, title = {Developmental scalable hierarchies for multi-agent swarms}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Generative and developmental systems: Poster}, pages = {207--208}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001974}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Hierarchical control structures for multi-agent systems represent a promising middle ground between fully-distributed systems and centralised control. In this paper we present a developmental approach for evolving hierarchical control structures for large (100-800 member), multi-agent swarms. The results show that this approach can successfully generate control hierarchies that improve the performance of fully distributed swarms and that scale well.}, notes = {Also known as \cite{2001974} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Harper:2011:GECCOcomp, author = {Robin Harper}, title = {Dynamic l-systems in GE}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, Generative and developmental systems: Poster}, pages = {209--210}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001975}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, I describe how to use Grammatical Evolution to implement a parametrised Lindenmayer System (L-System), where the number of production rules of the L-System is determined by the genome of the individual, rather than being determined by the user before hand. This leaves the number of production rules as a free parameter and allows the underlying topology of the system to be optimised by the evolutionary algorithm.}, notes = {Also known as \cite{2001975} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zhan:2011:GECCOcomp, author = {Zhi-Hui Zhan and Jun Zhang}, title = {Co-evolutionary differential evolution with dynamic population size and adaptive migration strategy}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Parallel evolutionary systems: Poster}, pages = {211--212}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001977}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {As the performance of differential evolution (DE) is significantly affected by its mutation schemes and parameter settings when solving different problems, this paper proposes a simple yet efficient co-evolutionary DE (CEDE) to enhance the algorithm performance. The CEDE algorithm uses multiple populations to optimise the problem cooperatively, with each population using different operators and/or different parameters. Moreover, as different populations may show different performance on the same problem, we further design an efficient adaptive migration strategy (AMS) to dynamically control the population size of different populations. The CEDE algorithm is tested and compared on four benchmark functions. Experimental results demonstrate the good performance of CEDE when compared with conventional DEs using different operators and/or parameters.}, notes = {Also known as \cite{2001977} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Porta:2011:GECCOcomp, author = {Juan Porta and Jorge Parapar and Guillermo L. Taboada and Ram\'{o}n Doallo and Francisco F. Rivera and In\'{e}s Sant\'{e} and Marcos Su\'{a}rez and Marcos Boull\'{o}n and Rafael Crecente}, title = {A Java-based parallel genetic algorithm for the land use planning problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Parallel evolutionary systems: Poster}, pages = {213--214}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001978}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this work, the application of genetic algorithms to the elaboration of land use plans is studied. These plans follow the national legal rules and experts' considerations. Two optimisation criteria are applied: aptitude and compactness. As the number of affected plots can be large and, consequently, the execution time of the algorithm can be potentially high, the work is focused on the implementation and analysis of different parallel paradigms: multi-core parallelism, cluster parallelism and the combination of both.}, notes = {Also known as \cite{2001978} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Malhotra:2011:GECCOcomp, author = {Abhinav Malhotra and Varun Aggarwal}, title = {HIER-HEIR: an evolutionary system with hierarchical representation \&\#38; contextual operators applied to fashion design}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Real world applications: Poster}, pages = {215--216}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001980}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {There has been considerable interest in using evolutionary algorithms based techniques to design creative systems. However, these techniques are either too 'creative' and violate design constraints of the domain, or, those catering to a limited search space, but operating within design constraints. Our new evolutionary system 'HIER-HEIR', is not only creative(searches a large space effectively), but creates only such designs which are valid with respect to the design domain. Inspired by human design methodology, the representation is a hierarchy of components and the variation is contextual acting at all levels of the hierarchy intelligently, facilitating effective search in the design space with explicit control over exploitation and exploration. We have explained our technique with the metaphor of automatic design of a fashion dress in this paper. The experimental results validate our hypotheses with regard to the system. With regard to previous work, our technique is new both with regard to previously published hierarchical systems and those designed for evolving fashion designs.}, notes = {Also known as \cite{2001980} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Yu:2011:GECCOcomp, author = {Jeongmin Yu and Sang-Goog Lee and Moongu Jeon}, title = {Medical image segmentation by hybridizing ant colony optimization and fuzzy clustering algorithm}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {217--218}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001981}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Possibilistic c-means (PCM) algorithm was proposed to overcome the noise sensitivity of fuzzy c-means (FCM). However, the performance of PCM depends heavily on the initialisation, and often deteriorates due to the coincident clustering problem. To overcome these problems, we propose a new hybrid clustering algorithm that incorporates an ACO-based clustering into PCM, namely ACOPCM for noisy image segmentation. Our ACOPCM solves the coincident clustering problem using pre-classified pixel information and provides the near optimal initialisation of the number of clusters and their centroids. Experimental results demonstrate that our proposed approach achieves higher segmentation accuracy than PCM and hybrid fuzzy clustering approaches.}, notes = {Also known as \cite{2001981} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Peralta-Donate:2011:GECCOcomp, author = {Juan {Peralta Donate} and Paulo Cortez and Araceli {Sanchis de Miguel} and German {Gutierrez Sanchez}}, title = {Evolving sparsely connected neural networks for multi-step ahead forecasting}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {219--220}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001982}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Time Series Forecasting (TSF) is an important tool to support decision making. Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlinear learning and noise tolerance. However, the search for the best ANN is a complex task that highly affects the forecasting performance. In this paper, we propose a novel Sparsely connected Evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead forecasts. This approach is compared with a similar strategy but that only evolves fully connected ANNs (FEANN) and a conventional TSF method (i.e. ARIMA methodology). A set of six time series, from different real-world domains, was used in the comparison. Overall, the obtained results reveal the proposed SEANN approach as the best forecasting method, optimising more simpler structures and requiring less computational effort when compared with the fully connected evolutionary ANN strategy.}, notes = {Also known as \cite{2001982} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Song:2011:GECCOcomp, author = {Andy Song and Feng Xie}, title = {Evolving automatic frame splitters}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, Real world applications: Poster}, pages = {221--222}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001983}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper extends the application of Genetic Programming into a new area, automatically splitting video frames based on the content. Compared with human written video splitting programs, GP generated splitters are more accurate. Moreover these video splitting programs have high tolerance to noises. They can still achieve reasonable performance even when the noisy videos are not easily recognisable by human eyes.}, notes = {Also known as \cite{2001983} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Banzi:2011:GECCOcomp, author = {Adam S. Banzi and Aurora T.R. Pozo and r., Elias P. Duarte}, title = {Bio-inspired event dissemination in dynamic and decentralized networks}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {223--224}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001984}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents a strategy based on swarm intelligence for spreading events in dynamic and decentralised networks. An event is defined as a state transition of a node or link. Ants, which correspond to mobile agents, spread event information throughout the network. A node which detects an event in its neighbourhood starts disseminating the new information. Pheromones are used to both control the ant population and help to define the paths that the agents take. An empirical study was performed, in which the proposed strategy was compared with flooding and gossip algorithms. Obtained results show that the proposed strategy presents a good trade-off between the time required to disseminate information and the overhead in terms of the number of messages.}, notes = {Also known as \cite{2001984} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Sanchez:2011:GECCOcomp, author = {Ernesto Sanchez and Giovanni Squillero and Alberto Tonda}, title = {Evolutionary failing-test generation for modern microprocessors}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, SBSE, Real world applications: Poster}, pages = {225--226}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001985}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The incessant progress in manufacturing technology is posing new challenges to microprocessor designers. Nowadays, comprehensive verification of a chip can only be performed after tape-out, when the first silicon prototypes are available. Several activities that were originally supposed to be part of the pre-silicon design phase are migrating to this post-silicon time as well. The short paper describes a post-silicon methodology that can be exploited to devise functional failing tests. Such tests are essential to analyse and debug speed paths during verification, speed-stepping, and other critical activities. The proposed methodology is based on the Genetic Programming paradigm, and exploits a versatile toolkit named muGP. The paper demonstrates that an evolutionary algorithm can successfully tackle a significant and still open industrial problem. Moreover, it shows how to take into account complex hardware characteristics and architectural details of such complex devices.}, notes = {Also known as \cite{2001985} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Besada-Portas:2011:GECCOcomp, author = {Eva Besada-Portas and Luis {de la Torre} and Alejandro Moreno and Jose Luis Risco-Martin}, title = {Performance analysis of multiobjective bio-inspired UAV path planners}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {227--228}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001986}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work aims to provide developers of bio-inspired UAV planners with a methodology to perform a systematic analysis of the results of their planners and support their algorithm parameter tuning based on this analysis. With that purpose, we propose to use some generic metrics capable of dealing with different dominance definitions and others that consider the final preferences of the experts. We apply them to a particular problem and show how to use them to identify the best planners within a big set.}, notes = {Also known as \cite{2001986} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hochreiter:2011:GECCOcomp, author = {Ronald Hochreiter and Christoph Waldhauser}, title = {Evolved election forecasts: using genetic algorithms in improving election forecast results}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {229--230}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001987}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we apply a genetic algorithm to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be taken in seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provide an argument to tighten the intersection between computer science and social science, especially political science, further. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.}, notes = {Also known as \cite{2001987} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Stonedahl:2011:GECCOcomp, author = {Forrest Stonedahl and David Anderson and William Rand}, title = {When does simulated data match real data?}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {231--232}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001988}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Agent-based models can replicate real-world patterns, but finding parameters that achieve the best match can be difficult. To validate a model, a real-world dataset is often divided into a training set (to calibrate the parameters) and a test set (to validate the calibrated model). The difference between the training and test data and the simulated data is determined using an error measure. In the context of evolutionary computation techniques, the error measure also serves as a fitness function, and thus affects evolutionary search dynamics. We survey the effect of five different error measures on both a toy problem and a real world problem of matching a model to empirical online news consumption behaviour. We use each error measure separately for calibration on the training dataset, and then examine the results of all five error measures on both the training and testing datasets. We show that certain error measures sometimes serve as better fitness functions than others, and in fact using one error measure may result in better calibration (on a different measure) than using the different measure directly. For the toy problem, the Pearson's correlation measure dominated all other measures, but no single error measure was Pareto dominant for the real world problem.}, notes = {Also known as \cite{2001988} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Boudjeloud:2011:GECCOcomp, author = {Lydia Boudjeloud and Hanane Azzag}, title = {A cooperative biomimetic approach for high dimensional data mining}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {233--234}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001989}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose in this paper an original alternative to solve the problem of search space visualisation to discover the complex structure of data, while respecting topology. Our cooperative approach provided a multi-dimensional visualization from the data. The first method is the subspace selection from whole data space. This selection is obtained by a genetic algorithm reducing the data dimension space by simply determining the most relevant dimensions evaluated by a distribution measure. Once a subspace selected we construct a neighbourhood graph using artificial ants algorithm.}, notes = {Also known as \cite{2001989} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Roy:2011:GECCOcomp, author = {Subhrajit Roy and Minhazul Izlam and Saurav Ghosh and Swagatam Das and Ajith Abraham and Pavel Kromer}, title = {A modified differential evolution for autonomous deployment and localization of sensor nodes}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {235--236}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001990}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The performance of a wireless sensor network (WSN) is largely influenced by the optimal deployment and accurate localisation of sensor nodes. This article considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). This kind of deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. In this article we propose an improved variant of an important evolutionary algorithm Differential Evolution for image segmentation and for distributed localization of the deployed nodes. Simulation results show that the proposed algorithm ADE_pBX performs image segmentation faster than both types of algorithm for optimal thresholds. Moreover in case of localization it gives more accurate results than the compared algorithms.}, notes = {Also known as \cite{2001990} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Letnes:2011:GECCOcomp, author = {Paul Anton Letnes and Ingar Stian Nerb\o and Lars Martin Sandvik Aas and P\aal Gunnar Ellingsen and Morten Kildemo}, title = {Genetic invention of fast and optimal broad-band stokes/mueller polarimeter designs}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Real world applications: Poster}, pages = {237--238}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001991}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We have applied a genetic algorithm to generate optimal polarimeter designs for a selected wavelength interval, assuming known dispersion relations of the components. Our results are improvements on previous patented designs based on ferroelectric liquid crystals.}, notes = {Also known as \cite{2001991} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Parque:2011:GECCOcomp, author = {Victor Parque and Shingo Mabu and Kotaro Hirasawa}, title = {Genetic network programming with changing structures for a novel stock selection model}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, genetic network programming, Real world applications: Poster}, pages = {239--240}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001992}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Stock selection involves the continuous quest for the margin of safety, or a favourable difference between the stock price and its intrinsic value. Although this variable might not be quantified with exact precision, it may be approximated through the underlying relationships in financial markets and the real economy. We propose Genetic Network Programming with changing structures(GNP-cs), a novel evolutionary based algorithm to approximate these relationships through graph networks, and build asset selection models to identify the prospective stocks in the context of changing environments. GNP-cs uses functionally distributed systems to monitor the change of the economic environment and execute the strategy for stock selection adaptively. The comparison shows that the proposed scheme outperforms the standard stock selection styles using the stocks listed in the Russell 3000 Index. This paper suggests that the use of evolutionary computing techniques is an excellent tool to tackle the stock selection problem, whose advantages imply the usefulness to manage the risk and safeguard investments.}, notes = {Also known as \cite{2001992} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{delSagrado:2011:GECCOcomp, author = {Jos\'{e} {del Sagrado} and Isabel M. \'{A}Aguila and Francisco J. Orellana}, title = {Requirements interaction in the next release problem}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Search-based software engineering: Poster}, pages = {241--242}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001994}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The selection of a set of requirements between all those proposed by the customers is an important process in software development, that can be addressed using heuristic optimisation techniques. Dependencies or interactions between requirements can be defined to denote common situations in software development: requirements that follow an order of precedence, requirements exclusive of each other, requirements that must be included at the same time, etc. This paper shows how requirements interactions affect the search space explored by optimisation algorithms. Three search techniques, i.e. a greedy randomised adaptive search procedure (GRASP), a genetic algorithm (GA) and an ant colony system (ACS), have been adapted to the requirements selection problem considering interaction between requirements. We describe the adaptation of the three meta-heuristic algorithms to solve this problem and compare their performance.}, notes = {Also known as \cite{2001994} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wilkerson:2011:GECCOcomp, author = {Josh L. Wilkerson and Daniel R. Tauritz}, title = {Scalability of the coevolutionary automated software correction system}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, SBSE, Search-based software engineering: Poster}, pages = {243--244}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001995}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Coevolutionary Automated Software Correction system addresses in an integral and fully automated manner the complete cycle of software artifact testing, error location, and correction phases. It employs a coevolutionary approach where software artifacts and test cases are evolved in tandem. The test cases evolve to better find flaws in the software artifacts and the software artifacts evolve to better behave to specification when exposed to the test cases, thus causing an evolutionary arms race. Experimental results are presented which demonstrate the scalability of the Coevolutionary Automated Software Correction system by establishing correlations between program size and both success rate and estimated convergence rate that are at most linear.}, notes = {Also known as \cite{2001995} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Faunes:2011:GECCOcomp, author = {Martin Faunes and Marouane Kessentini and Houari Sahraoui}, title = {Software clustering by example}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Search-based software engineering: Poster}, pages = {245--246}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001996}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We model software clustering problems in a setting, where elements of a software system form a graph to be partitioned in order to derive high-level abstractions. We extend this formulation in a way that the graph partitioning solutions are evaluated by the degree of their conformance with past clustering cases given as examples. We provide a concrete illustration of this formulation with the problem of object identification in procedural code, for which we obtained better results than a clustering approach.}, notes = {Also known as \cite{2001996} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Papadakis:2011:GECCOcomp, author = {Mike Papadakis and Nicos Malevris}, title = {Automatic mutation based test data generation}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Search-based software engineering: Poster}, pages = {247--248}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001997}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper proposes a search based test data generation approach for mutation testing. The proposed approach uses a novel dynamic execution scheme in order to both introduce mutants and to effectively guide the search process towards generating test cases able to kill those mutants. A novel fitness function and its integration with a dynamically adjusted mechanism are also proposed. Preliminary experimentation with a hill climbing search based approach reveals its power when compared against a previously proposed one and random testing.}, notes = {Also known as \cite{2001997} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Raiha:2011:GECCOcomp, author = {Outi R\"{a}ih\"{a} and Kai Koskimies and Erkki M\"{a}kinen}, title = {Multi-objective genetic synthesis of software architecture}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Search-based software engineering: Poster}, pages = {249--250}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001998}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A possible approach to partly automated software architecture design is the application of heuristic search methods like genetic algorithms. In order to take into account conflicting quality requirements, the use of Pareto optimality is proposed. This technique is studied in the presence of two central quality attributes of software architectures, modifiability and efficiency. The technique produces a palette of architecture proposals, and has been implemented and evaluated using an example system. The results demonstrate that Pareto optimality has potential for producing a sensible set of architectures in the efficiency-modifiability space.}, notes = {Also known as \cite{2001998} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bauersfeld:2011:GECCOcomp, author = {Sebastian Bauersfeld and Stefan Wappler and Joachim Wegener}, title = {An approach to automatic input sequence generation for GUI testing using ant colony optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Search-based software engineering: Poster}, pages = {251--252}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2001999}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Testing applications with a graphical user interface (GUI) is an important, though challenging and time consuming task. The state of the art in the industry are still capture and replay tools, which greatly simplify the recording and execution of input sequences, but do not support the tester in finding fault-sensitive test cases. While search-based test case generation strategies, such as evolutionary testing, are well researched for various areas of testing, relatively little work has been done on applying these techniques to an entire GUI of an application. This paper presents an approach to finding input sequences for GUIs using ant colony optimisation and a relatively new metric called maximum call stacks for use within the fitness function.}, notes = {Also known as \cite{2001999} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Turkey:2011:GECCOcomp, author = {Mikdam Turkey and Riccardo Poli}, title = {Social adaptive groups: a new approach for evolutionary optimisation based on social behaviour evolution}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {253--254}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002001}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a new approach for building evolutionary optimisation algorithms inspired by concepts borrowed from evolution of social behaviour. The proposed approach uses a set of behaviours used as operators that work on a population of individuals. These behaviours are used and evolved by groups of individuals to enhance the group adaptation to the environment and to other groups as well. Each group has two sets of behaviours: one for intra-group interactions and one for inter-group interactions. These behaviours are evolved using mathematical models from the field of evolutionary game theory. This paper describes the proposed paradigm and starts studying its characteristics by building a new evolutionary algorithm and studying its behaviour.}, notes = {Also known as \cite{2002001} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ortiz-Bayliss:2011:GECCOcompM, author = {Jos\'{e} Carlos Ortiz-Bayliss and Hugo Terashima-Mar\'{\i}n and Ender \"{O}zcan and Andrew J. Parkes}, title = {On the idea of evolving decision matrix hyper-heuristics for solving constraint satisfaction problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {255--256}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002002}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {When solving a Constraint Satisfaction Problem (CSP), the order in which the variables are selected to be instantiated has implications in the complexity of the search. This work presents the first ideas for evolving hyper-heuristics as decision matrices where the elements in the matrix represent the variable ordering heuristic to apply according to the constraint density and tightness of the current instance.}, notes = {Also known as \cite{2002002} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lopez-Camacho:2011:GECCOcomp, author = {Eunice L\'{o}pez-Camacho and Hugo Terashima-Mar\'{\i}n and Peter Ross}, title = {A hyper-heuristic for solving one and two-dimensional bin packing problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {257--258}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002003}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The idea behind hyper-heuristics is to discover rules that relate different problem states with the best single heuristic to apply. This investigation works towards extending the problem domain in which a given hyper-heuristic can be applied and implements a framework to generate hyper-heuristics for a wider range of bin packing problems. We present a GA-based method that produces general hyper-heuristics that solve a variety of instances of one- and two dimensional bin packing problem without further parameter tuning. The two-dimensional problem instances considered deal with rectangles, convex and non-convex polygons.}, notes = {Also known as \cite{2002003} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Brendel:2011:GECCOcomp, author = {M\'{a}ty\'{a}s Brendel and Marc Schoenauer}, title = {Instance-based parameter tuning for evolutionary AI planning}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {259--260}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002004}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learn-and-Optimise (LaO) is a generic surrogate based method for parameter tuning combining learning and optimization. In this paper LaO is used to tune Divide-and-Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate to unknown instances in the same domain. Moreover, the learnt model is used as a surrogate-model to accelerate the search for the optimal parameters. The proposed implementation of LaO uses an Artificial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimisation. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited learning time and amount of meaningful features that are available. However, it is demonstrated that the learned model is capable of generalisation in the domain to unknown instances.}, notes = {Also known as \cite{2002004} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ortiz-Bayliss:2011:GECCOcomp, author = {Jos\'{e} Carlos Ortiz-Bayliss and Hugo Terashima-Mar\'{\i}n and Peter Ross and Santiago Enrique Conant-Pablos}, title = {Evolution of neural networks topologies and learning parameters to produce hyper-heuristics for constraint satisfaction problems}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {261--262}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002005}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a model which constructs hyper-heuristics for variable ordering within Constraint Satisfaction Problems (CSPs) by running a genetic algorithm that evolves the topology of neural networks and some learning parameters.}, notes = {Also known as \cite{2002005} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Branke:2011:GECCOcomp, author = {Juergen Branke and Jawad Elomari}, title = {Simultaneous tuning of metaheuristic parameters for various computing budgets}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Self-* search: Poster}, pages = {263--264}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002006}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many heuristics require a number of parameters to be tuned. One way to do this is meta-optimization: a higher level heuristic searches for the best parameter settings of a lower level heuristic which solves the optimization problem. However, the optimal parameter settings depend on the computational budget or running time available to the lower level heuristic. In this paper, we present a new meta-optimisation approach to identify the best parameter settings simultaneously for various computational budgets.}, notes = {Also known as \cite{2002006} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Qian:2011:GECCOcomp, author = {Chao Qian and Yang Yu and Zhi-Hua Zhou}, title = {Collisions are helpful for computing unique input-output sequences}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Theory: Poster}, pages = {265--266}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002008}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computing unique input-output sequences (UIOs) from finite state machines (FSMs) is important for conformance testing in software engineering, where evolutionary algorithms (EAs) have been found helpful. Previously, by using a fitness function called W-fitness, (1+1)-EA was theoretically shown to be superior to random search on some FSM instances. Motivated by the observation that many plateaus exist in the fitness landscape of the W-fitness function, in this paper, we propose a new fitness function called C-fitness which is able to override the plateaus through exploiting collisions among the states of FSMs. We theoretically analyse the running time of (1+1)-EA on two problem classes. Our results show that the performance of (1+1)-EA using C-fitness is generally better and never worse than that using W-fitness in our studied cases, implying the importance of exploiting problem structures.}, notes = {Also known as \cite{2002008} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Schoenauer:2011:GECCOcomp, author = {Marc Schoenauer and Fabien Teytaud and Olivier Teytaud}, title = {Simple tools for multimodal optimization}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Theory: Poster}, pages = {267--268}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002009}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We compare various approaches for multimodal optimisation; we focus on comparing restart and more sophisticated approaches, and on the use of quasi-random numbers.}, notes = {Also known as \cite{2002009} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Smit:2011:GECCOcomp, author = {Selmar K. Smit and Zoltan Szl\'{a}avik and Agoston E. Eiben}, title = {Population diversity index: a new measure for population diversity}, booktitle = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = {2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger}, isbn13 = {978-1-4503-0690-4}, keywords = {Theory: Poster}, pages = {269--270}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002010}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A number of diversity measures used in evolutionary computing suffer from 'mis-measuring' the diversity of populations. In this paper, we identify and demonstrate this problem using a carefully engineered test suite of six differently arranged populations. We also propose a new measure, called Population Diversity Index (PDI), that solves the problem. We show that sorting the test configurations by their PDI value we obtain a correct ranking (i.e., a natural one, conformant with the human-perceived order). PDI also allows for a comparison between populations of different sizes and genome-dimensions, and its relation to the uniform distribution makes the calculated diversity values easy to interpret.}, notes = {Also known as \cite{2002010} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cox:2011:GECCOcomp, author = {Ian Cox}, title = {Exploring opportunity spaces efficiently for fun and profit}, booktitle = {GECCO 2011 Evolutionary computation in practice}, year = {2011}, editor = {Joern Mehnen and Thomas Bartz-Beielstein and David Davis}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {271--284}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002012}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002012} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hatton:2011:GECCOcomp, author = {Donagh Hatton and Diarmuid P. O'Donoghue}, title = {Explorations on template-directed genetic repair using ancient ancestors and other templates}, booktitle = {GECCO 2011 Evolutionary computation techniques for constraint handling}, year = {2011}, editor = {Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {325--332}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002014}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Handling constraints for combinatorial optimisation problems is a classic challenge faced by genetic and evolutionary algorithms. This paper explores a naturally inspired genetic repair process to enforce constraints on evolutionary search. Lolle et al. (2005) controversially claim that the model plant Arabidopsis thaliana appears to repair genetic errors using information inherited from ancestors other than the immediate parents [10] (i.e. non-Mendelian inheritance). We adapt this natural template-driven genetic repair process (GeneRepair) to help solve constraint problems. Building upon previous results [6][7][8] this paper explores repair templates that originate across a range of ancestors, between one and many thousands of generations old. The fitness of resulting populations are presented and compared to a benchmark technique using a random repair template [9]. The results show that very ancient (ancestral) repair templates perform best for larger problems, significantly outperforming the benchmark. The impact of background mutation rates on solution quality is also explored. Results suggest that ancestral repair is a good general-purpose constraint handling technique - helping to explain why this strategy might have evolved in nature.}, notes = {Also known as \cite{2002014} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Holena:2011:GECCOcomp, author = {Martin Hole\v{n}a and David Linke and Luk\'{a}\v{s} Bajer}, title = {Case study: constraint handling in evolutionary optimization of catalytic materials}, booktitle = {GECCO 2011 Evolutionary computation techniques for constraint handling}, year = {2011}, editor = {Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {333--340}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002015}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The paper presents a case study in an industrially important application domain the optimization of catalytic materials. Though evolutionary algorithms are the by far most frequent approach to optimization tasks in that domain, they are challenged by mixing continuous and discrete variables, and especially by a large number of constraints. The paper describes the various kinds of encountered constraints, and explains constraint handling in GENACAT, one of evolutionary optimization systems developed specifically for catalyst optimization. In particular, it is shown that the interplay between cardinality constraints and linear equality and inequality constraints allows GENACAT to efficienlty determine the set of feasible solutions, and to split the original optimization task into a sequence of discrete and continuous optimization. Finally, the genetic operations employed in the discrete optimization are sketched, among which crossover is based on an assumption about the importance of the choice of sets of continuous variables in the cardinality constraints.}, notes = {Also known as \cite{2002015} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Fernandez:2011:GECCOcomp, author = {Antonio Fernandez and Consolacion Gil and Antonio Lopez Marquez and Raul Banos and Maria Gil Montoya and Maria Parra}, title = {A memetic algorithm for two-dimensional multi-objective bin-packing with constraints}, booktitle = {GECCO 2011 Evolutionary computation techniques for constraint handling}, year = {2011}, editor = {Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {341--346}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002016}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Over recent years, a number of independent researchers have shown that meta-heuristics are effective strategies for solving hard combinatorial optimization problems. In particular, Memetic Algorithms (MA) are population-based meta-heuristic search methods that are inspired by Darwinian principles of natural selection and Dawkins' notion of meme that have successfully been applied to single- and multi-objective optimisation problems. The two-dimensional bin-packing problem (2D-BPP) [1] with rotations is an important optimisation problem which has a large number of practical applications. It consists of the non-overlapping placement of a set of rectangular pieces in the lowest number of bins of a homogenous size, with the edges of these pieces always parallel to the sides of bins, and with free 90 degrees rotation. Bin-packing problems are complex combinatorial optimization problems included in the category of NP-hard problems of fundamental importance in industry, transportation, computer systems, machine scheduling, etc. The multi-objective two-dimensional bin-packing problem considers other objectives to optimise, such as the imbalance of the objects according to a centre of gravity of the bin. The balance in the bin loads has important applications in container loading, tractor trailer trucks, pallet loading and cargo airplanes. This paper analyses the performance of a Pareto-based memetic algorithm, which operators have been specially designed to solve this problem while considering some constraints. Results obtained in some test problems show the good performance of this approach in comparison with multi-objective Particle Swarm Optimisation.}, notes = {Also known as \cite{2002016} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zhang:2011:GECCOcomp, author = {Xue-Feng Zhang and Miyuki Koshimura and Hiroshi Fujita and Ryuzo Hasegawa}, title = {Combining PSO and local search to solve scheduling problems}, booktitle = {GECCO 2011 Evolutionary computation techniques for constraint handling}, year = {2011}, editor = {Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {347--354}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002017}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Intelligent manufacturing is associated with a large number of complex optimisation problems and for this reason has got a considerable research attention over the last decades. Most of these problems are of combinatorial nature and have been proved to be NP-complete. This paper deals with the flow shop scheduling problem (FSSP) and the Job Shop Scheduling Problem (JSSP). The objective of these problems is to find an appropriate sequence to minimise the makespan, which are defined as the time for completing a final operation. One major challenging issue is how to obtain the high-quality global optimum. In order to refrain from the premature convergence and being easily trapped into local optimum, we are motivated to find high-quality solutions in a reasonable computation time by exploiting Particle Swarm Optimisation (PSO), Tabu Search (TS) and Simulated Annealing (SA). We propose a new multi-structural hybrid evolutionary framework, and derive HPTS algorithm as its extension. Extensive experiments on different scale benchmarks validate the effectiveness of our approaches, compared with other well-established methods. The experimental results show that new upper bounds of the unsolved problems are achieved in a relatively reasonable time. For example, in 30 Tailland's and 43 OR-Library benchmarks, 7 new upper bounds and 6 new upper bounds are obtained by the HPTS algorithm, respectively.}, notes = {Also known as \cite{2002017} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ahmadi-Abhari:2011:GECCOcomp, author = {Kaveh {Ahmadi Abhari} and Ali Hamzeh and Sattar Hashemi}, title = {Voting based learning classifier system for multi-label classification}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {355--360}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002019}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learning Classifier Systems (LCSs) are rule-based systems with a discovery mechanism to find additional meaningful rules according to the results of its previous experiments. LCSs were designed to deal with both single and multistep problems. In the first category, almost all major studies focus on the single-label classification problems. However, there are more complex problems that require multi-label classification. The aim of this study is to take advantage of the power and ability of LCSs for solving multi-label classification problems. The main idea behind this research is to guide the discovery mechanism by a prior knowledge. This prior knowledge is defined as a voting mechanism that realises the quality of the existing rules and is used in discovering new rules. Our proposed system is called Voting Based LCS (VLCS). The experimental results show the proposed method has potential for future research and progress.}, notes = {Also known as \cite{2002019} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Behdad:2011:GECCOcomp, author = {Mohammad Behdad and Tim French and Luigi Barone and Mohammed Bennamoun}, title = {PCA for improving the performance of XCSF in classification of high-dimensional problems}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {361--368}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002020}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {XCSR is an accuracy-based learning classifier system (LCS) which can handle classification problems with real-value features. However, as the number of features increases, a high classification accuracy comes at the cost of more resources: larger population sizes and longer computational running times. In this research, we present a PCA-enhanced LCS, which uses principal component analysis (PCA) as a preprocessing step for XCSR, and examine how it performs on complex multi-dimensional real-world data. The experiments show that this technique, in addition to significantly reducing the computational resources and time requirements of XCSR, maintains its high accuracy and even occasionally improves it. In addition to that, it reduces the required population size needed by XCSR.}, notes = {Also known as \cite{2002020} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Urbanowicz:2011:GECCOcomp, author = {Ryan Urbanowicz and Nicholas Sinnott-Armstrong and Jason Moore}, title = {Random artificial incorporation of noise in a learning classifier system environment}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {369--374}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002021}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Effective rule generalisation in learning classifier systems (LCSs) has long since been an important consideration. In noisy problem domains, where attributes do not precisely determine class, overemphasis on accuracy without sufficient generalization leads to over-fitting of the training data, and a large discrepancy between training and testing accuracies. This issue is of particular concern within noisy bioinformatic problems such as complex disease, gene association studies. In an effort to promote effective generalisation we introduce and explore a simple strategy which seeks to discourage over-fitting via the probabilistic incorporation of random noise within training instances. We evaluate a variety of noise models and magnitudes which either specify an equal probability of noise per attribute, or target higher noise probability to the attributes which tend to be more frequently generalized. Our results suggest that targeted noise incorporation can reduce training accuracy without eroding testing accuracy. In addition, we observe a slight improvement in our power estimates (i.e. ability to detect the true underlying model(s)).}, notes = {Also known as \cite{2002021} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Iqbal:2011:GECCOcomp, author = {Muhammad Iqbal and Mengjie Zhang and Will Browne}, title = {Automatically defined functions for learning classifier systems}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, 20mux}, pages = {375--382}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002022}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work introduces automatically defined functions (ADFs) for learning classifier systems (LCS). ADFs had been successfully implemented in genetic programming (GP)for various domain problems such as multiplexer and even-odd parity, but they have never been attempted in LCS research field before. ADFs in GP contract program trees and shorten training times whilst providing resilience to destructive genetic operators. We have implemented ADFs in Wilson's accuracy based LCS, known as XCS [14]. This initial investigation of ADFs in LCS shows that the multiple genotypes to a phenotype issue in feature rich encodings disables the subsumption deletion function. The additional methods and increased search space also leads to much longer training times. This is compensated by the ADFs containing useful knowledge, such as the importance of the address bits in the multiplexer problem. The ADFs also create masks that autonomously subdivide the search space into areas of interest and uniquely, areas of not interest. The next stage of this work is to implement simplification methods and then determine methods by which ADFs can facilitate scaling for more complex problems within the same problem domain.}, notes = {Also known as \cite{2002022} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Butz:2011:IWLCS, author = {Martin V. Butz and Olivier Sigaud}, title = {{XCSF} with local deletion: preventing detrimental forgetting}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {383--390}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002023}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The XCSF classifier system solves regression problems iteratively online with a population of overlapping, local approximators. We show that problem solution stability and accuracy may be lost in particular settings - mainly due to XCSF's global deletion. We introduce local deletion, which prevents these detrimental effects to large extents. We show experimentally that local deletion can prevent forgetting in various problems - particularly where the problem space is non-uniformly or non-independently sampled. While we use XCSF with hyperellipsoidal receptive fields and linear approximations herein, local deletion can be applied to any XCS version where locality can be similarly defined. For future work, we propose to apply XCSF with local deletion to unbalanced, non-uniformly distributed, locally sampled problems with complex manifold structures, within which varying target error values may be reached selectively.}, notes = {Also known as \cite{2002023} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Santos:2011:GECCOcomp, author = {Manuel Filipe Santos and Wesley Mathew and Henrique Dinis Santos}, title = {Grid data mining by means of learning classifier systems and distributed model induction}, booktitle = {Fourteenth international workshop on learning classifier systems}, year = {2011}, editor = {Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {391--398}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002024}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Different methods of merging data mining models generated at different distributed sites are explored. Centralised Data Mining (CDM) is a conventional method of data mining in distributed data. In CDM, data that is stored in distributed locations have to be collected and stored in a central repository before executing the data mining algorithm. CDM method is reliable; however it is expensive (computational, communicational and implementation costs are high). Alternatively, Distributed Data Mining (DDM) approach is economical but it has limitations in combining local models. In DDM, the data mining algorithm has to be executed at each one of the sites to induce a local model. Those induced local models are collected and combined to form a global data mining model. In this work six different tactics are used for constructing the global model in DDM: Generalized Classifier Method (GCM); Specific Classifier Method (SCM); Weighed Classifier Method (WCM); Majority Voting Method (MVM); Model Sampling Method (MSM); and Centralized Training Method (CTM). Preliminary experimental tests were conducted with two synthetic data sets (eleven multiplexer and monks3) and a real world data set (intensive care medicine). The initial results demonstrate that the performance of DDM methods is competitive when compared with the CDM methods.}, notes = {Also known as \cite{2002024} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cupertino:2011:GECCOcomp, author = {Leandro F. Cupertino and Cleomar P. Silva and Douglas M. Dias and Marco Aur\'{e}lio C. Pacheco and Cristiana Bentes}, title = {Evolving CUDA PTX programs by quantum inspired linear genetic programming}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, GPU}, pages = {399--406}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002026}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks. In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU implementation.}, notes = {Also known as \cite{2002026} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{YujiSato:2011:GECCOcomp, author = {Yuji Sato and Naohiro Hasegawa and Mikiko Sato}, title = {Acceleration of genetic algorithms for sudoku solution on many-core processors}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {407--414}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002027}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we use the problem of solving Sudoku puzzles to demonstrate the possibility of achieving practical processing time through the use of many-core processors for parallel processing in the application of genetic computation. To increase accuracy, we propose a genetic operation that takes building-block linkage into account. As a parallel processing model for higher performance, we use a multiple-population coarse-grained GA model to counter initial value dependence under the condition of a limited number of individuals. The genetic manipulation is also accelerated by the parallel processing of threads. In an evaluation using even very difficult problems, we show that execution times of several tens of seconds and several seconds can be obtained by parallel processing with the Intel Corei7 and NVIDIA GTX460, respectively, and that a correct solution rate of 100percent can be achieved in either case.}, notes = {Also known as \cite{2002027} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{langdon:2011:cigpu, author = {William B. Langdon}, title = {Debugging {CUDA}}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {415--422}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002028}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {During six months of intensive nVidia CUDA C programming many bugs were created. We pass on the software engineering lessons learnt, particularly those relevant to parallel general-purpose computation on graphics hardware GPGPU.}, notes = {Also known as \cite{2002028} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{langdon:2011:cigpu2, author = {William B. Langdon}, title = {Performing with {CUDA}}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {423--430}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002029}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Recently a GPGPU application had to be redesigned to overcome performance problems. A number of software engineering lessons were learnt from this and other projects. We describe those about obtaining high performance from nVidia GPUs and practical aspects of CUDA C software development.}, notes = {Also known as \cite{2002029} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pospichal:2011:GECCOcomp, author = {Petr Pospichal and Eoin Murphy and Michael O'Neill and Josef Schwarz and Jiri Jaros}, title = {Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution, GPU}, pages = {431--438}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002030}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken. This paper deals with using mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimisation details are discussed and the NVCC compiler is analysed. We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main memory transactions. This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX 480 together with a GE library written in JAVA, GEVA. Results indicate that our algorithm offers the same convergence, and it is suitable for a larger number of regression points where GPU is able to reach speedups of up to 39 times faster when compared to GEVA on a serial CPU code written in C. In conclusion, properly used, GPU can offer an interesting performance boost for GE tackling symbolic regression.}, notes = {Also known as \cite{2002030} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pedemonte:2011:GECCOcomp, author = {Mart\'{\i}n Pedemonte and Enrique Alba and Francisco Luna}, title = {Bitwise operations for GPU implementation of genetic algorithms}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {439--446}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002031}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Research on the implementation of evolutionary algorithms in graphics processing units (GPUs) has grown in recent years since it significantly reduces the execution time of the algorithm. A relevant aspect, which has received little attention in the literature, is the impact of the memory space occupied by the population in the performance of the algorithm, due to limited capacity of several memory spaces in the GPUs. In this paper we analyse the differences in performance of a binary Genetic Algorithm implemented on a GPU using a Boolean data type or packing multiple bits into a non Boolean data type. Our study considers the influence on the performance of single point and double point crossover for solving the classical One-Max problem. The results obtained show that packing bits for storing binary strings can reduce the execution time up to 50percent.}, notes = {Also known as \cite{2002031} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lewis:2011:CIGPU, author = {Tony E. Lewis and George D. Magoulas}, title = {Identifying similarities in {TMBL} programs with alignment to quicken their compilation for {GPUs}}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, GPU}, pages = {447--454}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002032}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The most impressive accelerations of Genetic Programming (GP) using the Graphics Processing Unit (GPU) have been achieved by dynamically compiling new GPU code for each batch of individuals to be evaluated. This approach suffers an overhead in compilation time. We aim to reduce this penalty by pre-processing the individuals to identify and draw out their similarities, hence reducing duplication in compilation work. We use this approach with Tweaking Mutation Behaviour Learning (TMBL), a form focused on long term fitness growth. For individuals of 300 instructions, the technique is found to reduce compilation time 4.817 times whilst only reducing evaluation speed by 3.656percent.}, notes = {Also known as \cite{2002032} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lewis:2011:CIGPU2, author = {Tony E. Lewis and George D. Magoulas}, title = {{TMBL} kernels for {CUDA GPUs} compile faster using {PTX}}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, GPU}, pages = {455--462}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002033}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many of the most effective attempts to harness the power of the Graphics Processing Unit (GPU) to accelerate Genetic Programming (GP) have dynamically compiled code for individuals as they are to be evaluated. This approach executes very quickly on the GPU but is slow to compile, hence only vast data-sets fully reap its rewards. To reduce compilation time, we generate and compile code in the lower-level language PTX. We investigate this in the context of implementing Tweaking Mutation Behaviour Learning (TMBL) on the GPU. We find that for programs of 300 instructions, using PTX reduces the compile time 5.861 times and even increases the evaluation speed by 23.029percent.}, notes = {Also known as \cite{2002033} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Harding:2011:GECCOcomp, author = {Simon Harding and Wolfgang Banzhaf}, title = {Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET}, booktitle = {GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)}, year = {2011}, editor = {Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming, GPU}, pages = {463--470}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002034}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called 'GPU.NET' for implementing a Genetic Programming fitness evaluator. We find that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based interpreter. With its code reuse and compact representation, implementing CGP efficiently on the GPU required several innovations. Further, we tested the system on a very large data set, and showed that CGP is also suitable for use as a classifier.}, notes = {Also known as \cite{2002034} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Soh:2011:GECCOcomp, author = {Harold Soh and Yiannis Demiris}, title = {Multi-reward policies for medical applications: anthrax attacks and smart wheelchairs}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {471--478}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002036}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Medical decisions are often difficult; they involve uncertain information, multiple-objectives and debatable outcomes. In this work, we discuss the application of the multi-reward partially-observable Markov decision process (MR-POMDP) and NSGA2-LS, a hybridised multi-objective evolutionary solver, to two problems in the medical domain: anthrax response and smart-wheelchair control. For the first problem, we use a discrete model and analyse the trade-offs between the best solutions (in the form of finite-state controllers) found by our evolutionary algorithm. For the second, we contribute an extension of our method to the continuous space and optimising recurrent neural networks (RNNs) for use on medical robots such as smart wheelchairs.}, notes = {Also known as \cite{2002036} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cruz-Ramirez:2011:GECCOcomp, author = {Manuel Cruz-Ramirez and Juan Carlos {Fernandez Caballero} and Francisco {Fernandez Navarro} and Javier Briceno and Manuel {de la Mata} and Cesar Hervas-Martinez}, title = {Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {479--486}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002037}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In liver transplantation, matching donor and recipient is a problem that can be solved using machine learning techniques. In this paper we consider a liver transplant dataset obtained from eleven Spanish hospitals, including the patient survival or the rejection in liver transplantation one year after it. To tackle this problem, we use a multi-objective evolutionary algorithm for training generalised radial basis functions neural networks. The obtained models provided medical experts with a mathematical value to predict survival rates allowing them to come up with a right decision according to the principles of justice, efficiency and equity.}, notes = {Also known as \cite{2002037} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ugolotti:2011:GECCOcomp, author = {Roberto Ugolotti and Pablo Mesejo and Stefano Cagnoni and Mario Giacobini and Ferdinando {Di Cunto}}, title = {Automatic hippocampus localization in histological images using PSO-based deformable models}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {487--494}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002038}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localisation of the structures is the first step to carry on this comparison in an automatic way. In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases. The first phase, called best reference slice selection, is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located. The second phase, the proper hippocampus localisation, is based on a method that combines Particle Swarm Optimisation (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image. Experiments on a test set of 120 images yielded a perfect or good localisation in 89.2percent of cases.}, notes = {Also known as \cite{2002038} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Dehuri:2011:GECCOcomp, author = {Satchidananda Dehuri and Rahul Roy and Sung-Bae Cho}, title = {An adaptive binary PSO to learn bayesian classifier for prognostic modeling of metabolic syndrome}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {495--502}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002039}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The metabolic syndrome is a combination of medical disorders that have become a significant problem in Asian countries due to the change in lifestyle and food habits. Thus a prognostic model can help the medical experts in diagnosis of the disease. Learnable Bayesian classifier by Adaptive Binary Particle Swarm Optimisation (ABPSO) provides a robust formalism for probabilistic modelling that can be used as a predictive tool in medical domain. In this paper, we adopt an ABPSO for adapting the weights of the learnable Bayesian classifier that provides a maximum prediction accuracy and can exhibit an improved capability of removing spurious or little important attributes and help the medical experts in identifying the basis for the disease. Experiments have been conducted with the dataset obtained in Yonchon Country of Korea, and the proposed model provides better performance than the other models.}, notes = {Also known as \cite{2002039} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Winkler:2011:GECCOcomp, author = {Stephan M. Winkler and Michael Affenzeller and Witold Jacak and Herbert Stekel}, title = {Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {503--510}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002040}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper we present results of empirical research work done on the data based identification of estimation models for cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumours we have trained mathematical models for estimating cancer diagnoses. Several data based modelling approaches implemented in HeuristicLab have been applied for 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. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81percent, 74percent, and 91percent of the analysed test cases, respectively; without tumour markers up to 75percent, 74percent, and 87percent of the test samples are correctly estimated, respectively.}, notes = {Also known as \cite{2002040} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Dorfer:2011:GECCOcomp, author = {Viktoria Dorfer and Stephan M. Winkler and Thomas Kern and Sophie A. Blank and Gerald Petz and Patrizia Faschang}, title = {On the performance of evolutionary algorithms in biomedical keyword clustering}, booktitle = {GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)}, year = {2011}, editor = {Stephen L. Smith and Stefano Cagnoni and Robert Patton}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {511--518}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002041}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the field of life sciences it often turns out to be a challenge to quickly find the desired information due to the huge amount of available data. The research area of information retrieval (IR) addresses this problem and tries to provide suitable solutions. One of the approaches used in IR is query extension based on keyword or document clusters. In this paper we present a deep analysis of a keyword clustering approach using four different kinds of evolutionary algorithms, namely evolution strategy (ES), genetic algorithm (GA), genetic algorithm with strict offspring selection (OSGA), and the multi-objective elitist non-dominated sorting genetic algorithm (NSGA-II). We have identified features that characterise solution candidates for the keyword clustering problem, e.g., the number of documents covered and how well the identified clusters of keywords match with the occurrence of keywords in the given set of documents. The use of these features and how evolutionary algorithms can be used to solve the optimisation of keyword clusters is shown in this paper. To test the here presented approach we used a real world data set provided within the TREC-9 conference; this data collection includes information about approximately 36,000 documents collected from the PubMed database. In the results section we compare the performance of the here tested evolutionary algorithms and see that especially ES and NSGA-II produce meaningful results for this documents collection. This approach based on evolutionary algorithms shall be used further on in automated query extension for biomedical information retrieval in PubMed.}, notes = {Also known as \cite{2002041} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Galib:2011:GECCOcomp, author = {Syed Md. Galib and Irene Moser}, title = {Road traffic optimisation using an evolutionary game}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {519--526}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002043}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In a commuting scenario, drivers expect to arrive at their destinations on time. Drivers have an expectation as to how long it will take to reach the destination. To this end, drivers make independent decisions regarding the routes they take. Independent decision-making is uncoordinated and unlikely to lead to a balanced usage of the road network. However, a well-balanced traffic situation is in the best interest of all drivers, as it minimises their travel times on average over time. This study investigates the possibility of using an Evolutionary Game, Minority Game (MG), to achieve a balanced usage of a road network through independent decisions made by drivers assisted by the MG algorithm. The experimental results show that this simple game-theoretic approach can achieve a near-optimal distribution of traffic in a network. An optimal distribution can be assumed to lead to equitable travel times which are close to the possible minimum considering the number of cars in the network.}, notes = {Also known as \cite{2002043} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Turkey:2011:ECoMASS, author = {Mikdam Turkey and Riccardo Poli}, title = {A social behaviour evolution approach for evolutionary optimisation}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {527--534}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002044}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms were originally designed to locate basins of optimum solutions in a stationary environment. Therefore, additional techniques and modifications have been introduced to deal with further requirements such as handling dynamic fitness functions or finding multiple optima. In this paper, we present a new approach for building evolutionary algorithms that is based on concepts borrowed from social behaviour evolution. Algorithms built with the proposed paradigm operate on a population of individuals that move in the search space as they interact and form groups. The interaction follows a set of social behaviours evolved by each group to enhance its adaptation to the environment (and other groups) and to achieve different desirable goals such as finding multiple optima, maintaining diversity, or tracking a moving peak in a changing environment. Each group has two sets of behaviours: one for intra-group interactions and one for inter-group interactions. These behaviours are evolved using mathematical models from the field of evolutionary game theory. This paper describes the proposed paradigm and starts studying it characteristics by building a new evolutionary algorithm and studying its behaviour. The algorithm has been tested using a benchmark problem generator with promising initial results, which are also reported.}, notes = {Also known as \cite{2002044} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gluckman:2011:GECCOcomp, author = {Gideon M. Gluckman and Joanna J. Bryson}, title = {An agent-based model of the effects of a primate social structure on the speed of natural selection}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {535--542}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002045}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The rate of speciation is in most mammals an order of magnitude faster than in most other vertebrates. It is faster still in the social mammals. The apparent association between complex modes of sociality and high rates of evolutionary change might provide an answer to the question of why these rates differ so markedly. Using an individual based model of a population with a social structure mimicking the one common to cercopithecine primates and a simple model ecology, we investigate the effects of social structures on the rates at which natural selection operates. The results of the model indicate that the specific social structure modelled does affect the rate at which natural selection operates within the modelled population.}, notes = {Also known as \cite{2002045} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Shafiee:2011:GECCOcomp, author = {M. Ehsan Shafiee and Emily M. Zechman}, title = {Sociotechnical simulation and evolutionary algorithm optimization for routing siren vehicles in a water distribution contamination event}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {543--550}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002046}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Water distribution contamination incidents occur when a poisonous chemical or pathogen is introduced intentionally or accidentally to the pipe network that delivers potable water to the residents of a municipality. These events pose a challenge to decision makers, who should quickly identify a threat and the most effective response actions for protection of public health. In these events, the dynamic interactions among consumers, utility managers, public health officials, and the water distribution pipe network affect the emergent exposure of consumers. An Agent-Based Modelling (ABM) approach is used to simulate the interactions among agents and flow conditions in the water distribution system to provide an understanding of effects of dynamic and adaptive behaviours on public health. While utility operators can protect consumers using a wide range of protective and mitigative responses, routing of siren vehicles can be effective as consumers are warned about a contaminant in the water system and respond by stopping different water activities, such as drinking water. Development of crisis management routing strategies, which are a set of routes to best warn and protect consumers from exposure, is enabled through a new simulation-optimisation framework. A genetic algorithm and the ABM are coupled to find routes for siren vehicles that minimise the number of consumers who are exposed to contaminated tap water. The framework is demonstrated for an illustrative case study, a mid-sized virtual city, to identify efficient routes for protecting public health.}, notes = {Also known as \cite{2002046} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Junges:2011:GECCOcomp, author = {Robert Junges and Franziska Kl\"{u}gl}, title = {Evolution for modeling: a genetic programming framework for sesam}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {551--558}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002047}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Developing a valid agent-based simulation model is not always straight forward, but involves a lot of prototyping, testing and analysing until the right low-level behaviour is fully specified and calibrated. Our aim is to replace the try and error search of a modeller by adaptive agents which learn a behaviour that then can serve as a source of inspiration for the modeler. In this contribution, we suggest to use genetic programming as the learning mechanism. For this aim we developed a genetic programming framework integrated into the visual agent-based modeling and simulation tool SeSAm, providing similar easy-to-use functionality.}, notes = {Also known as \cite{2002047} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Guo:2011:GECCOcomp, author = {Wei Guo and Wolfgang Jank and William Rand}, title = {Estimating functional agent-based models: an application to bid shading in online markets format}, booktitle = {GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop}, year = {2011}, editor = {William Rand and Forrest Stonedahl}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {559--566}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002048}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Bid shading is a common strategy in online auctions to avoid the winner's curse. While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows us to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, we propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. We calibrate that model (and hence estimate properties about the propensity and degree of shaded bids) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because we match functional draws from simulated and real price processes. We propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. We apply our model to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results.}, notes = {Also known as \cite{2002048} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Barros:2011:GECCOcomp, author = {Rodrigo C. Barros and M\'{a}rcio P. Basgalupp and Andr\'{e} C.P.L.F. {de Carvalho} and Alex A. Freitas}, title = {Towards the automatic design of decision tree induction algorithms}, booktitle = {GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms}, year = {2011}, editor = {Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {567--574}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002050}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user.}, notes = {Also known as \cite{2002050} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Goldman:2011:GECCOcomp, author = {Brian W. Goldman and Daniel R. Tauritz}, title = {Self-configuring crossover}, booktitle = {GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms}, year = {2011}, editor = {Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {575--582}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002051}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem often is a time consuming manual process. Even then a custom crossover operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run. This paper introduces the Self-Configuring Crossover operator encoded with linear genetic programming which addresses these shortcomings while relieving the user from the burden of crossover configuration. To demonstrate its general applicability, the novel crossover operator was applied without any problem specific tuning. Results are presented showing it to outperform the traditional crossover operators arithmetic crossover, uniform crossover, and n-point crossover on the Rosenbrock, Rastrigin, Offset Rastrigin, DTrap, and NK Landscapes benchmark problems.}, notes = {Also known as \cite{2002051} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Woodward:2011:GECCOcomp, author = {John Robert Woodward and Jerry Swan}, title = {Automatically designing selection heuristics}, booktitle = {GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms}, year = {2011}, editor = {Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {583--590}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002052}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In a standard evolutionary algorithm such as genetic algorithms (GAs), a selection mechanism is used to decide which individuals are to be chosen for subsequent mutation. Examples of selection mechanisms are fitness-proportional selection, in which individuals are chosen with a probability directly in proportion to their fitness value, and rank selection, in which individuals are selected with a probability in proportion to their ordinal ranking by fitness. These two human-designed selection heuristics implicitly assume that fitter individuals produce fitter offspring. Whilst one might invest human ingenuity in the construction of alternative selection heuristics, the approach adopted in this paper is to represent a generic family of selection heuristics which are applied via an algorithmic framework. We then generate instances of selection heuristics and test their performance in an evolutionary algorithm (which in this paper tackles a variety of bitstring optimization problems). The representation we use for the program space is a register machine (a set of real-valued registers on which a program is executed). Fitness-proportional and rank selection can be expressed as one-line programs, and more sophisticated selection heuristics may also be expressed. The result is a system which produces selection heuristics that outperform either of the original selection heuristics.}, notes = {Also known as \cite{2002052} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Brendel:2011:ecdga, author = {M\'{a}ty\'{a}s Brendel and Marc Schoenauer}, title = {Instance-based parameter tuning for evolutionary AI planning}, booktitle = {GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms}, year = {2011}, editor = {Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {591--598}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002053}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Learn-and-Optimise (LaO) is a generic surrogate based method for parameter tuning combining learning and optimisation. In this paper LaO is used to tune Divide-and-Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate this knowledge to unknown instances in the same domain. Moreover, the learned relation is used as a surrogate-model to accelerate the search for the optimal parameters. It hence becomes possible to solve intra-domain and extra-domain generalisation in a single framework. The proposed implementation of LaO uses an Artificial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimisation. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited amount of meaningful features that are available to describe the instances. However, the learnt model reaches almost the same performance on the test instances, which means that it is capable of generalisation.}, notes = {Also known as \cite{2002053} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Comarela:2011:GECCOcomp, author = {Giovanni Comarela and K\^{e}nia Gon\c{c}alves and Gisele Lobo Pappa and Jussara Almeida and Virg\'{\i}lio Almeida}, title = {Robot routing in sparse wireless sensor networks with continuous ant colony optimization}, booktitle = {Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011)}, year = {2011}, editor = {Maria J. Blesa and Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {599--606}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002055}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Sparse wireless sensor networks are characterised by the distances the sensors are from each other. In this type of network, gathering data from all sensors in a point of interest might be a difficult task, and in many cases a mobile robot is used to travel along the sensors and collect data from them. In this case, we need to provide the robot with a route that minimises the travelled distance and allows data collection from all sensors. This problem can be modelled as the classic Travelling Salesman Problem (TSP). However, when the sensors have an influence area bounded by a circle, for example, it is not necessary that the robot touches each sensor, but only a point inside the covered area. In this case, the problem can be modeled as a special case TSP with Neighbourhoods (TSPN). This work presents a new approach based on continuous Ant Colony Optimisation (ACO) and simple combinatorial technique for TSP in 0order to solve that special case of TSPN. The experiments performed indicate that significant improvements are obtained with the proposed heuristic when compared with other methods found in literature.}, notes = {Also known as \cite{2002055} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ruela:2011:GECCOcomp, author = {Andre Siqueira Ruela and Andre Luiz Lins Aquino and Frederico Gadelha Guimaraes}, title = {A cooperative coevolutionary algorithm for the design of wireless sensor networks: track name: bio-inspired solutions for wireless sensor networks}, booktitle = {Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011)}, year = {2011}, editor = {Maria J. Blesa and Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {607--614}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002056}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work proposes a cooperative coevolutionary algorithm for the design of a wireless sensor network considering complex network metrics. It is proposed an heuristic based on cooperative coevolution to find a network configuration such that its communication structure presents a small value for the average shortest path length and a high cluster coefficient. This configuration considers a cluster based network, where the cluster heads have two communication radii. The mathematical model of the cluster head location problem was developed to determine the nodes which will be configured as cluster heads. This model was adopted within the coevolutionary algorithm. We describe how the problem can be partitioned and how the fitness computation can be divided such that the cooperative coevolution model is feasible. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with two specifics complex network measurements allowing the reduction of energy consumption and the data transmission delay.}, notes = {Also known as \cite{2002056} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hernandez:2011:GECCOcomp, author = {Hugo Hern\'{a}ndez and Christian Blum}, title = {Implementing a model of Japanese tree frogs' calling behavior in sensor networks: a study of possible improvements}, booktitle = {Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011)}, year = {2011}, editor = {Maria J. Blesa and Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {615--622}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002057}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Male Japanese tree frogs exhibit a self-organised behaviour for the desynchronization of their calls. This property has evolved because female frogs are not able to correctly localize the male frogs when their calls are too close in time. A model for this behaviour has been proposed in the literature. However, its use in technical applications is, so far, quite limited. In this paper we implement the originally proposed model in sensor networks, with the aim of desynchronizing neighbouring nodes as much as possible. Moreover, we propose extensions of the original model. Experimental results show that the proposed extensions improve the desynchronization capabilities of the original model.}, notes = {Also known as \cite{2002057} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Stijven:2011:GECCOcomp, author = {Sean Stijven and Wouter Minnebo and Katya Vladislavleva}, title = {Separating the wheat from the chaff: on feature selection and feature importance in regression random forests and symbolic regression}, booktitle = {3rd symbolic regression and modeling workshop for GECCO 2011}, year = {2011}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {623--630}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002059}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Feature selection in high-dimensional data sets is an open problem with no universal satisfactory method available. In this paper we discuss the requirements for such a method with respect to the various aspects of feature importance and explore them using regression random forests and symbolic regression. We study 'conventional' feature selection with both methods on several test problems and a case study, compare the results, and identify the conceptual differences in generated feature importances. We demonstrate that random forests might overlook important variables (significantly related to the response) for various reasons, while symbolic regression identifies all important variables if models of sufficient quality are found. We explain the results by the fact that variable importance obtained by these methods have different semantics.}, notes = {Also known as \cite{2002059} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Kronberger:2011:GECCOcomp, author = {Gabriel Kronberger and Michael Kommenda and Michael Affenzeller}, title = {Overfitting detection and adaptive covariant parsimony pressure for symbolic regression}, booktitle = {3rd symbolic regression and modeling workshop for GECCO 2011}, year = {2011}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {631--638}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002060}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Covariant parsimony pressure is a theoretically motivated method primarily aimed to control bloat. In this contribution we describe an adaptive method to control covariant parsimony pressure that is aimed to reduce overfitting in symbolic regression. The method is based on the assumption that overfitting can be reduced by controlling the evolution of program length. Additionally, we propose an overfitting detection criterion that is based on the correlation of the fitness values on the training set and a validation set of all models in the population. The proposed method uses covariant parsimony pressure to decrease the average program length when over fitting occurs and allows an increase of the average program length in the absence of overfitting. The proposed approach is applied on two real world datasets. The experimental results show that the correlation of training and validation fitness can be used as an indicator for overfitting and that the proposed method of covariant parsimony pressure adaption alleviates overfitting in symbolic regression experiments with the two datasets.}, notes = {Also known as \cite{2002060} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hemberg:2011:GECCOcomp, author = {Erik Hemberg and Lester Ho and Michael O'Neill and Holger Claussen}, title = {A symbolic regression approach to manage femtocell coverage using grammatical genetic programming}, booktitle = {3rd symbolic regression and modeling workshop for GECCO 2011}, year = {2011}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {639--646}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002061}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present a novel application of Grammatical Evolution to the real-world application of femtocell coverage. A symbolic regression approach is adopted in which we wish to uncover an expression to automatically manage the power settings of individual femtocells in a larger femtocell group to optimise the coverage of the network under time varying load. The generation of symbolic expressions is important as it facilitates the analysis of the evolved solutions. Given the multi-objective nature of the problem we hybridise Grammatical Evolution with NSGA-II connected to tabu search. The best evolved solutions have superior power consumption characteristics than a fixed coverage femtocell deployment.}, notes = {Also known as \cite{2002061} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Torres-Trevino:2011:GECCOcomp, author = {Luis M. Torres-Trevino}, title = {Symbolic regression using {\$\alpha\$}- {\$\beta\$} operators and estimation of distribution algorithms: preliminary results}, booktitle = {3rd symbolic regression and modeling workshop for GECCO 2011}, year = {2011}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {647--654}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002062}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Modelling processes is an important task in engineering; however, the generation of models using only experimental data is not a straightforward problem. Linear regression, neural networks, and other approaches have been used for this purpose; nevertheless, a mathematical description is desirable specially when an optimisation is required. Symbolic regression has been used for generating equations considering only experimental data. In this paper, two new operators are proposed to represent a mathematical model of a process. These operators simplified the way for representing equations making possible its use as a symbolic regression. The correct model is generated selecting the appropriate operators and parameters using an evolutionary algorithm like the estimation of distribution algorithms. As a preliminary results, three cases are used to illustrated the performance of the proposed approach. The results indicates that the use of these alpha, beta operators are a promising way to apply symbolic regression to model complex process.}, notes = {Also known as \cite{2002062} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Holladay:2011:GECCOcomp, author = {Kenneth L. Holladay and John Marshall Sharp and Marc Janssens}, title = {Automatic pyrolysis mass loss modeling from thermo-gravimetric analysis data using genetic programming}, booktitle = {3rd symbolic regression and modeling workshop for GECCO 2011}, year = {2011}, editor = {Steven Gustafson and Ekaterina Vladislavleva}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {655--662}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002063}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Modelling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models ... Mode ling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.}, notes = {Also known as \cite{2002063} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bosman:2011:GECCOcomp, author = {Peter A.N. Bosman and Dirk Thierens}, title = {The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective}, booktitle = {Optimization by building and using probabilistic models (OBUPM-2011)}, year = {2011}, editor = {Mark Hauschild and Martin Pelikan}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {663--670}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002065}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The inclusion of local search (LS) techniques in evolutionary algorithms (EAs) is known to be very important in order to obtain competitive results on combinatorial and real-world optimisation problems. Often however, an important source of the added value of LS is an understanding of the problem that allows performing a partial evaluation to compute the change in quality after only small changes were made to a solution. This is not possible in a Black-Box Optimisation (BBO) setting. Here we take a closer look at the added value of LS when combined with EAs in a BBO setting. Moreover, we consider the interplay with model building, a technique commonly used in Estimation-of-Distribution Algorithms (EDAs) in order to increase robustness by statistically detecting and exploiting regularities in the optimization problem. We find, using two standardised hard BBO problems from EA literature, that LS can play an important role, especially in the interplay with model building in the form of what has become known as substructural LS. However, we also find that optimal mixing (OM), which indicates that operations in a variation operator are directly checked whether they lead to an improvement, is a superior combination of LS and EA.}, notes = {Also known as \cite{2002065} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Janikow:2011:GECCOcomp, author = {Cezary Z. Janikow and John W. Aleshunas and Mark W. Hauschild}, title = {Second order heuristics in ACGP}, booktitle = {Optimization by building and using probabilistic models (OBUPM-2011)}, year = {2011}, editor = {Mark Hauschild and Martin Pelikan}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {671--678}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002066}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising capabilities using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure.}, notes = {Also known as \cite{2002066} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Santana:2011:OBUPM, author = {Roberto Santana}, title = {Estimation of distribution algorithms: from available implementations to potential developments}, booktitle = {Optimization by building and using probabilistic models (OBUPM-2011)}, year = {2011}, editor = {Mark Hauschild and Martin Pelikan}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {679--686}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002067}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design strategies used for their implementations are discussed. The paper also reviews different directions to improve current EDA implementations. A number of lines for further expanding the areas of application for EDAs software are proposed.}, notes = {Also known as \cite{2002067} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Santucci:2011:GECCOcomp, author = {Valentino Santucci and Alfredo Milani}, title = {Covariance-based parameters adaptation in differential evolution}, booktitle = {GECCO 2011 Scaling behaviours of landscapes, parameters and algorithms}, year = {2011}, editor = {Ender Ozcan and Andrew J. Parkes and Jonathan Rowe}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {687--690}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002069}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Differential Evolution (DE) is a popular and efficient optimisation technique for real-valued spaces based on the concepts of Darwinian evolution. Its main peculiarity is the use of a differential mutation operator that allows DE to automatically adjust the exploration/exploitation balance of its search moves. The major DE drawback is the need of a preliminary tuning of some numerical parameters. Although, recently some parameters adaptive schemes have been proposed, none of these takes into account the side effects introduced by changing two or more parameters at the same time. In this paper we introduce a DE self-adaptive scheme that takes into account the parameters dependencies by means of a multivariate probabilistic technique based on an Estimation of Distribution Algorithm working on the parameters space. Experiments have been performed on a set of commonly adopted benchmark problems and the obtained results show the competitiveness of our approach with respect to other adaptive DE schemes. Moreover, our scheme could be potentially adopted not only in DE but also in any other Evolutionary Algorithm or meta-heuristic technique presenting parameters that regulate the behaviour of the search.}, notes = {Also known as \cite{2002069} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Salto:2011:GECCOcomp, author = {Carolina Salto and Enrique Alba and Francisco Luna}, title = {Using landscape measures for the online tuning of heterogeneous distributed gas}, booktitle = {GECCO 2011 Scaling behaviours of landscapes, parameters and algorithms}, year = {2011}, editor = {Ender Ozcan and Andrew J. Parkes and Jonathan Rowe}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {691--694}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002070}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Tuning distributed genetic algorithms (dGAs) increases even more the task of finding an appropriate parametrisation, since the migration operator adds, at least, five additional values that have to be set up. This work is a preliminary approach on using a landscape measure (the Fitness Distance Correlation) to dynamically adjust one of these five parameters, in particular, the migration period. The results have shown that, by using this information, the quality of the solutions is competitive with those obtained by the algorithms with the pre-tuned migration period, but with a saving of more than 100 hours of preliminary experimentation.}, notes = {Also known as \cite{2002070} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Caamano:2011:GECCOcomp, author = {Pilar Caamano and Jose A. Becerra and Francisco Bellas and Richard J. Duro}, title = {Are evolutionary algorithm competitions characterizing landscapes appropriately}, booktitle = {GECCO 2011 Scaling behaviours of landscapes, parameters and algorithms}, year = {2011}, editor = {Ender Ozcan and Andrew J. Parkes and Jonathan Rowe}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {695--702}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002071}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Currently, researchers in the field of Evolutionary Algorithms (EAs) are very interested in competitions where new algorithm implementations are evaluated and compared. Usually, EA users perform their algorithm selection by following the results published in these competitions, which are typically focused on average performance measures over benchmark sets. These sets are very complete but the functions within them are usually classified into binary classes according to their separability and modality. Here we show that this binary classification could produce misleading conclusions about the performance of the EAs and, consequently, it is necessary to consider finer grained features so that better conclusions can be obtained about what scenarios are adequate or inappropriate for an EA. In particular, new elements are presented to study separability and modality in more detail than is usually done in the literature. The need for such detail in order to understand why things happen the way they do is made evident over three different EAs.}, notes = {Also known as \cite{2002071} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Liao:2011:GECCOcomp, author = {Tianjun Liao and Marco A. {Montes de Oca} and Thomas St\"{u}tzle}, title = {Tuning parameters across mixed dimensional instances: a performance scalability study of Sep-G-CMA-ES}, booktitle = {GECCO 2011 Scaling behaviours of landscapes, parameters and algorithms}, year = {2011}, editor = {Ender Ozcan and Andrew J. Parkes and Jonathan Rowe}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {703--706}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002072}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Sep-G-CMA-ES is a variant of G-CMA-ES with lower time complexity. In this paper, we evaluate the impact that various ways of tuning have on the performance of Sep-G-CMA-ES on scalable continuous benchmark functions. We have extracted seven parameters from Sep-G-CMA-ES and tuned them across training functions with different features using an automatic algorithm configuration tool called Iterated F-Race. The best performance of Sep-G-CMA-ES was obtained when it was tuned using functions of different dimensionality (a strategy that we call mixed dimensional). Our comparative study on scalable benchmark functions also shows that the default Sep-G-CMA-ES outperforms G-CMA-ES. Moreover, the tuned version of Sep-G-CMA-ES significantly improves over both G-CMA-ES and default Sep-G-CMA-ES.}, notes = {Also known as \cite{2002072} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Corne:2011:GECCOcomp, author = {David Corne and Alan Reynolds}, title = {Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems}, booktitle = {GECCO 2011 Scaling behaviours of landscapes, parameters and algorithms}, year = {2011}, editor = {Ender Ozcan and Andrew J. Parkes and Jonathan Rowe}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {707--710}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002073}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this extended abstract, we look at the common practice of using optimisation problem test suites to develop and/or evaluate optimisation algorithms, and bring to bear on this practice a number of results available from computational learning theory. This enables optimisation algorithm developers to express principled quantitative bounds on the likely performance of their algorithms on unseen problem instances, on the basis of details of their experimental design and empirical results on training or test instances. We first recap some relevant results from computational learning theory, and then describe how optimisation development practice can be suitably recast in a way that enables these results to be applied. We then briefly discuss some related implications. An updated version of this article and associated material, including statistical tables relating to generalisation bounds, are provided at http://is.gd/evalopt.}, notes = {Also known as \cite{2002073} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gil:2011:GECCOcomp, author = {Consolaci\'{o}n Gil and Pedro S\'{a}nchez and Francisco G. Montoya and Antonio L. M\'{a}rquez}, title = {Open source tool for energy saving and efficient system management}, booktitle = {GECCO 2011 GreenIT evolutionary computation}, year = {2011}, editor = {Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {711--718}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002075}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In order to improve power quality (PQ) techniques, efforts are made to develop smart sensors that can report near real-time data. Proprietary software and hardware on dedicated computers or servers processes these data and shows relevant information through tables or graphics. In this situation, interoperability, compatibility and scalability are not possible because of the lack of open protocols. This paper presents a new open source solution focused on optimisation of power quality and monitoring for low voltage power systems. For that, an open source platform has been developed for computing, storing and managing all of the information generated from smart sensors. We apply the most up-to-date algorithms developed for PQ, event detection, and harmonic analysis or power metering. A plugin implementing the S-transform is being developed for the system. To obtain the best input values to this plugin we are developing optimisation algorithms to detect the most of well-known disturbances. Our system makes use of cutting-edge web technologies such as HTML5, CSS3 and Javascript to provide user-friendly interaction and powerful capabilities for the analysis, measurement and monitoring of power systems.}, notes = {Also known as \cite{2002075} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Toutouh:2011:GECCOcomp, author = {Jamal Toutouh and Enrique Alba}, title = {An efficient routing protocol for green communications in vehicular ad-hoc networks}, booktitle = {GECCO 2011 GreenIT evolutionary computation}, year = {2011}, editor = {Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {719--726}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002076}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Vehicular ad-hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art there is a lack of studies on Green Communications (energy-efficiency) in VANETs. However, due to the possible interaction with devices that are fed with different electrical sources and the proliferation of electrical vehicles, the power consumption by the wireless communications might become a major concern in VANET design. In this paper, we study the energy-efficiency of a quality-of-service optimised version of OLSR by means of Differential Evolution (DE-OLSR). We have conducted a series of VANET simulations aiming at analysing the power consumption and the QoS in order to compare DE-OLSR with the standard version of OLSR. An extensive performance evaluation shows that DE-OLSR clearly outperforms the standard version in terms of energy consumption, while offering a competitive QoS.}, notes = {Also known as \cite{2002076} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Fialho:2011:GECCOcomp, author = {Alvaro Fialho and Youssef Hamadi and Marc Schoenauer}, title = {Optimizing architectural and structural aspects of buildings towards higher energy efficiency}, booktitle = {GECCO 2011 GreenIT evolutionary computation}, year = {2011}, editor = {Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {727--732}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002077}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this on-going work, we aim at contributing to the issue of energy consumption by proposing tools to automatically define some aspects of the architectural and structural design of buildings. Our framework starts with a building design, and automatically optimizes it, providing to the architect many variations that minimize, in different ways, both energy consumption and construction costs. The optimization stage is done by the combination of an energy consumption simulation program, EnergyPlus, with a state-of-the-art multi-objective evolutionary algorithm, Hype. The latter explores the design search space, automatically generating new feasible design solutions, which are then evaluated by the energy simulation software. Preliminary results are presented, in which the proposed framework is used to optimize the orientation angle of a given commercial building and the materials used for the thermal insulation of its walls.}, notes = {Also known as \cite{2002077} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tantar:2011:GECCOgreen, author = {Alexandru-Adrian Tantar and Emilia Tantar and Pascal Bouvry}, title = {Load balancing for sustainable ICT}, booktitle = {GECCO 2011 GreenIT evolutionary computation}, year = {2011}, editor = {Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {733--738}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002078}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The herein paper addresses the issue of providing a model and guidelines for constructing a sustainable ICT environment at the University of Luxembourg. A particular context is thus considered, based on a real-life project that has as aim to provide a sustainable environment for the ICT infrastructure of the university. According to the different environment constraints and requirements, the objectives are to minimise electricity consumption by employing vitalisation techniques and also to reduce carbon emissions by creating a load balanced charge of the computers that build the infrastructure. The quality of service is also addressed by provisioning factors. A multi-objective dynamic approach is considered in order to cope with the simultaneous optimisation of the mentioned objectives and the dynamic nature of the system.}, notes = {Also known as \cite{2002078} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Benbassat:2011:GECCOcomp, author = {Amit Benbassat and Moshe Sipper}, title = {Evolving board-game players with genetic programming}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {739--742}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002080}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.}, notes = {Also known as \cite{2002080} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Endler:2011:GECCOcomp, author = {Anke Endler and Martin V. Butz and G\"{u}nter Daniel Rey}, title = {Extracting adaptation strategies for e-learning programs with XCS}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {743--746}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002081}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper investigates XCS performance on a scarce and noisy artificial and a real-world data set. The real-world data set is derived from an E-Learning study, in which motivation was correlated with the adaptation of difficulty. The artificial data set was generated to evaluate if XCS can be expected to mine information from the real-world data set. By adding sparsity and noise to the artificial data set, mimicking the properties of the real-world data set, we show that XCS can handle scarce and noisy data well. We furthermore show that the extracted structure contains problem-relevant information, and that revealed structures in the real-world data correspond to actual psychological learning theories. Thus, the contributions of the paper are twofold: (1) We show that XCS can mine highly scarce and noisy data; and (2) the results suggest that the current motivational state of the user may be used to adapt an E-Learning program for improving learning progress.}, notes = {Also known as \cite{2002081} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Turkey:2011:GECCOgsw, author = {Mikdam Turkey and Riccardo Poli}, title = {Investigating a new paradigm for designing evolutionary optimisation algorithms using social behaviour evolution}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {747--750}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002082}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper describes a new approach for building evolutionary optimisation algorithms inspired by concepts borrowed from evolution of social behaviour. The proposed approach uses a set of behaviours used as operators that work on a population of individuals. These behaviours are used and evolved by groups of individuals to enhance a group adaptation to the environment and to other groups. Each group has two sets of behaviours: one for intra-group interactions and one for inter-group interactions. These behaviours are evolved using mathematical models from the field of evolutionary game theory. This paper describes the proposed paradigm and starts studying its characteristics by building a new evolutionary algorithm and studying its behaviour. The algorithm has been tested using a benchmark problem generator with promising initial results, which are also reported. We conclude the paper by identifying promising directions for the continuation of this research.}, notes = {Also known as \cite{2002082} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Dovgan:2011:GECCOcomp, author = {Erik Dovgan and Matja\v{z} Gams and Bogdan Filipi\v{c}}, title = {A multiobjective optimization algorithm for discovering driving strategies}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {751--754}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002083}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a deterministic multiobjective optimization algorithm for discovering driving strategies. The goal is to find a set of nondominated driving strategies with respect to two conflicting objectives: time and fuel consumption. The presented multiobjective algorithm is based on the breadth-first search algorithm and Nondominated Sorting Genetic Algorithm (NSGA-II). Experiments on a 10-km route show that the results significantly depend on the discrimination of the search space.}, notes = {Also known as \cite{2002083} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Kherlopian:2011:GECCOcomp, author = {Armen R. Kherlopian and Francis A. Ortega and David J. Christini}, title = {Cardiac myocyte model parameter sensitivity analysis and model transformation using a genetic algorithm}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {755--758}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002084}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cardiac arrhythmia is the disruption of the normal electrical rhythm of the heart and is a leading cause of mortality around the world. To study arrhythmogenesis, mathematical models of cardiac myocytes and tissues have been effectively employed to investigate cardiac electrodynamics. However, among individual myocytes, there is phenotypic variability that is dependent on factors such as source location in the heart, genetic variation, and even different experimental protocols. Thus, established cardiac myocyte models constrained by experimental data are often untuned to new phenomena under investigation. In this study, we show direct links to parameter changes and differing electrical phenotypes. First, we present results exploring model sensitivity to physiological parameters underpinning electrical activity. Second, we outline a genetic algorithm based approach for tuning model parameters to fit cardiac myocyte behaviour. Third, we use a genetic algorithm to transform one model type to another, relating simulation to experimental data. This model transformation demonstrates the potential of genetic algorithms to extend the utility of cardiac myocyte models by comparing different functional regions in the heart.}, notes = {Also known as \cite{2002084} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tsarev:2011:GECCOcomp, author = {Fedor Tsarev and Kirill Egorov}, title = {Finite state machine induction using genetic algorithm based on testing and model checking}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {759--762}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002085}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we describe the method of finite state machine (FSM) induction using genetic algorithm with fitness function, cross-over and mutation based on testing and model checking. Input data for the genetic algorithm is a set of tests and a set of properties described using linear time logic. Each test consists of an input sequence of events and the corresponding output action sequence. In previous works testing and model checking were used separately in genetic algorithms. Usage of such an approach is limited because the behaviour of system usually cannot be described by tests only. So, additional validation or verification is needed. Calculation of fitness function based only on verification do not perform well because there are very few possible values of fitness function (verification gives only yes or no answer). The approach described is tested on the problem of finite state machine induction for elevator doors controlling. Using tests only the genetic algorithm constructs the finite machine working improperly in some cases. Usage of verification allows to induct the correct finite state machine.}, notes = {Also known as \cite{2002085} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Buzdalov:2011:GECCOcomp, author = {Maxim Buzdalov}, title = {Generation of tests for programming challenge tasks using evolution algorithms}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, SBSE}, pages = {763--766}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002086}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based on genetic algorithms. The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions passed the generated set of tests.}, notes = {Also known as \cite{2002086} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Nichele:2011:GECCOcomp, author = {Stefano Nichele}, title = {Discrete dynamics of cellular machines: specification and interpretation}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {767--770}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002087}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents research on discrete dynamics of cellular machines, their specification and interpretation. It gives an overview of the fundamental issues related to the classification of Cellular Automata (CA) classes. In particular, the possible locations of various CA capable to achieve different degrees of complex behaviours are described. This work is mainly focused on the correlation between CA behaviour and cellular regulative properties. A possible minimalistic experimental setup is presented, together with some preliminary results and ideas that can be investigated in future work.}, notes = {Also known as \cite{2002087} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zapotecas-Martinez:2011:GECCOcomp, author = {Saul {Zapotecas Martinez} and Carlos A. {Coello Coello}}, title = {Swarm intelligence guided by multi-objective mathematical programming techniques}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {771--774}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002088}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Since the early days of multi-objective particle swarm optimisers (MOPSOs), researchers have looked for appropriate mechanisms to define the set of leaders (or global best set) from the swarm. At the beginning, leaders were randomly selected from the set of nondominated solutions currently available. However, over the years, researchers realised that random selection schemes were not the best choice, and additional information was incorporated in the leader selection mechanism (namely, information related to density estimation). Here, we study the use of mathematical programming techniques for defining the leader selection mechanism of a MOPSO. The proposed approach decomposes a multi-objective optimisation problem (MOP) into several single objective optimisation problems by using traditional multi-objective mathematical programming techniques. Our preliminary results indicate that our proposed approach is a viable choice for solving MOPs, since it is able to outperform a state-of-the-art multi-objective evolutionary algorithm (MOEA).}, notes = {Also known as \cite{2002088} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Alexandrov:2011:GECCOcomp, author = {Anton Alexandrov and Alexey Sergushichev and Sergey Kazakov and Fedor Tsarev}, title = {Genetic algorithm for induction of finite automata with continuous and discrete output actions}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {775--778}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002089}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper, we describe a genetic algorithm for induction of finite automata with continuous and discrete output actions. Input data for the algorithm is a set of tests. Each test consists of two sequences: input events and output actions. In previous works output actions were discrete, i.e. selected from the finite set, in this work output actions can also be continuous, i.e. represented by real numbers. Only the structure of automaton transitions graph is evolved by the genetic algorithm. Values of output actions are found using transition labelling algorithm, which aim is to maximise the value of fitness function. New transition labelling algorithm is proposed. It also works with continuous output actions and is based on equations system solving. In case of proper selection of fitness function, equations in this system are linear and it can be solved by the Gaussian elimination method. The unmanned airplane performing the loop is considered as an example of the controlled object.}, notes = {Also known as \cite{2002089} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Murphy:2011:GECCOcomp, author = {Eoin Murphy}, title = {Examining grammars and grammatical evolution in dynamic environments}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {779--782}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002090}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper is concerned with the effect of the grammar type on grammatical evolution when evolving in dynamic environments. Both representation and dynamic environments have been recognised as important open issues in the field of genetic programming. This paper outlines the need for further study on both topics in the context of grammatical evolution, suggesting further inspiration be taken from nature in an attempt to improve the representations available to grammatical evolution. The research undertaken to date is listed, along with the future work to be completed.}, notes = {Also known as \cite{2002090} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Fagan:2011:GECCOcomp, author = {David Fagan}, title = {Genotype-phenotype mapping in dynamic environments with grammatical evolution}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, grammatical evolution}, pages = {783--786}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002091}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The application of a genotype-phenotype mapping in Evolutionary Computation is not a new idea, however, how this mapping process is interpreted, and implemented varies wildly. In the majority of cases a very simple abstraction of the biological genotype-phenotype mapping is used, but as our understanding of this process increases, the deficiencies in current approaches become more evident. In this paper, an outline of what approaches have been taken in the investigation of the genotype-phenotype map in Grammatical Evolution are presented and an outline of proposed future work is introduced.}, notes = {Also known as \cite{2002091} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bandaru:2011:GECCOcomp, author = {Sunith Bandaru and Kalyanmoy Deb}, title = {Design knowledge extraction in multi-objective optimization problems}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {787--790}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002092}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work concerns the post-optimal analysis of the trade-off front of a multi-objective optimisation problem to extract useful design knowledge pertaining to these high-performing solutions. The expected knowledge basically consists of statistically significant relationships between the objective functions and decision variables. These relationships are represented in an intuitive and easy-to-use mathematical form. Since a number of such relationships may exist, for efficiency it is desirable that they are obtained in a single knowledge extraction step. Further, problem knowledge can be explored at two levels: lower and higher. At the lower-level, our interest is in finding a subset of the trade-off solutions to which the obtained relationships are exclusive. The higher-level knowledge addresses the effect of varying the problem parameters (that are kept constant in one run) on the trade-off front and therefore on the relationships. These concepts are explained through different examples.}, notes = {Also known as \cite{2002092} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Manso:2011:GECCOgsw, author = {Ant\'{o}nio Manuel R. Manso and Lu\'{\i}s Miguel P. Correia}, title = {MuGA: multiset genetic algorithm}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {791--794}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002093}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The traditional representation of populations used in evolutionary algorithms raises two types of problems: the loss of genetic diversity during the evolutionary process and evaluation of redundant individuals. To minimise these problems we developed MuGA, whose most distinctive feature is the representation of populations by multisets, and adapted the evolutionary process to handle the new representation. In this paper we present MuGA algorithm and explore its capacity to preserve the genetic diversity and find many optima using the Knapsack problem. Next we adapted genetic operators for the application of MuGA to real coded problems. The results obtained in a set of benchmark functions of these classes of problems, when compared with competitive algorithms, support our conviction that the multisets are an efficient representation of populations. Future work will focus on identifying limitations and subsequent improvements to the algorithm and its application to other classes of problems.}, notes = {Also known as \cite{2002093} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Salinas-Gutierrez:2011:GECCOcomp, author = {Rogelio Salinas-Guti\'{e}rrez and Arturo Hern\'{a}ndez-Aguirre and Enrique R. Villa-Diharce}, title = {Estimation of distribution algorithms based on copula functions}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {795--798}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002094}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The main objective of this doctoral research is to study Estimation of Distribution Algorithms (EDAs) based on copula functions. This new class of EDAs has shown that it is possible to incorporate successfully copula functions in EDAs.}, notes = {Also known as \cite{2002094} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Helmuth:2011:GECCOcomp, author = {Thomas Helmuth and Lee Spector and Brian Martin}, title = {Size-based tournaments for node selection}, booktitle = {GECCO 2011 Graduate students workshop}, year = {2011}, editor = {Miguel Nicolau}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {799--802}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002095}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In genetic programming, the reproductive operators of crossover and mutation both require the selection of nodes from the reproducing individuals. Both unbiased random selection and Koza 90/10 mechanisms remain popular, despite their arbitrary natures and a lack of evidence for their effectiveness. It is generally considered problematic to select from all nodes with a uniform distribution, since this causes terminal nodes to be selected most of the time. This can limit the complexity of program fragments that can be exchanged in crossover, and it may also lead to code bloat when leaf nodes are replaced with larger new subtrees during mutation. We present a new node selection method that selects nodes based on a tournament, from which the largest participating subtree is selected. We show this method of size-based tournaments improves performance on three standard test problems with no increases in code bloat as compared to unbiased and Koza 90/10 selection methods.}, notes = {Also known as \cite{2002095} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Gill:2011:GECCOcomp, author = {Zann Gill}, title = {Collaborative intelligence in living systems: algorithmic implications of evo-devo debates}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {803--804}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002097}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces the A-PR Hypothesis (Autonomy and Pattern Recognition), exploring potential to develop next generation crowd-sourcing and recommender systems that apply collaborative intelligence principles to multi-agent distributed systems. If capacity for autonomy and pattern recognition that marks the threshold when non-life becomes alive also characterises the spectrum from sensor networks to crowd-sourcing menial tasks, to next generation crowd-sourcing with intelligent integration for problem-solving, then unique players with unique capacities for pattern recognition and interpretation could comprise a synergetic system with behaviour of the whole unpredicted by individual behaviours of its component agents.}, notes = {Also known as \cite{2002097} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Zechman:2011:GECCOcomp, author = {Emily Michelle Zechman and Marcio H. Giacomoni and M. Ehsan Shafiee}, title = {A multi-objective niching co-evolutionary algorithm (MNCA) for identifying diverse sets of non-dominated solutions}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {805--806}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002098}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many engineering design problems must optimism multiple objectives. While many objectives are explicit and can be mathematically modelled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative Pareto fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies a set of Pareto-optimal solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of the Pareto front. MNCA is demonstrated for a set of multi-modal multi-objective test problems to identify a set of Pareto fronts with maximum difference in decision vectors.}, notes = {Also known as \cite{2002098} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Cape:2011:GECCOcomp, author = {David Andrew Cape and Daniel R. Tauritz}, title = {Probabilistically interpolated rational hypercube landscape evolutionary algorithm}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {807--808}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002099}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms are powerful function optimisers, but suffer from premature convergence. Quantum-Inspired Evolutionary Algorithm (QEA) has been shown to be less prone to this on an important class of binary encoded problems. QEA uses Q-bits in place of ordinary bits, introducing a rational parameter into an otherwise binary search space. The essential feature of QEA is that the fitness of individuals in the population is defined stochastically by sampling from discrete points in the landscape. The probability of a particular point being sampled is based on the proximity of an individual to that point, where the individual represents a point in the solid hypercube spanned by the possible discrete solutions. This paper presents Probabilistically Interpolated Rational Hypercube Landscape Evolutionary Algorithm (PIRHLEA), which generalises QEA by relaxing its two vestigial quantum mechanical attributes: quadratic and angular parametrisation of probabilities and using single samples to determine fitness estimates of individuals. This is accomplished by replacing each Q-bit with a rational parameter between zero and one. Compared to QEA, PIRHLEA is simpler to code, more computationally efficient, and easier to visualise. PIRHLEA also permits multiple samples from points in the landscape to determine individuals' fitness.}, notes = {Also known as \cite{2002099} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Singh:2011:GECCOcomp, author = {Sameer Kumar Singh and Ruppa K. Thulasiram and Parimala Thulasiraman}, title = {Pricing transmission rights using ant colony optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {809--810}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002100}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We propose a novel idea for pricing Transmission Rights (which are similar to financial options) using a nature inspired meta heuristic algorithm, Ant Colony Optimisation (ACO). ACO has been used extensively in combinatorial optimisation problems and recently in dynamic applications such as mobile ad-hoc networks. Specifically, the proposed ACO algorithm have been applied to totally different application, Transmission Rights, in the current study.}, notes = {Also known as \cite{2002100} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{StephenChen:2011:GECCOcomp, author = {Stephen Chen and James Montgomery}, title = {A simple strategy to maintain diversity and reduce crowding in particle swarm optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {811--812}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002101}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Each particle of a swarm maintains its current location and its personal best location. It is useful to think of these personal best locations as a population of attractors. When this population of attractors converges, the explorative capacity of the swarm is reduced. The convergence of attractors can occur quickly since the personal best of a particle is broadcast to its neighbours. If a neighbouring particle comes close to this broadcasting attractor, it may update its own personal best to be near the broadcasting attractor. This convergence of attractors can be reduced by having particles update the broadcasting attractor rather than their own attractor/personal best. Through this simple change which incurs minimal computational costs, large performance improvements can be achieved in multi-modal search spaces.}, notes = {Also known as \cite{2002101} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tutum:2011:GECCOcomp, author = {Cem Celal Tutum and Zhun Fan}, title = {Automatic synthesis of MEMS devices using self-adaptive hybrid metaheuristics}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {813--814}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002102}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper introduces a multi-objective optimisation approach for layout synthesis of MEMS components. A case study of layout synthesis of a comb-driven micro-resonator shows that the approach proposed in this paper can lead to design results accommodating two design objectives, i.e. simultaneous minimisation of size and power input of a MEMS device, while investigating optimum geometrical configuration as the main concern. The major contribution of this paper is the application of self-adaptive memetic computing in MEMS design. An evolutionary multi-objective optimisation (EMO) technique, in particular non-dominated sorting genetic algorithm (NSGA-II), has been applied together with a pattern recognition statistical tool, i.e. Principal Component Analysis (PCA), to find multiple trade-off solutions in an efficient manner. Following this, a gradient-based local search, i.e. sequential quadratic programming (SQP), is applied to improve and speed up the convergence of the obtained Pareto-optimal front. In order to reduce the number of function evaluations in the local search procedure, the obtained non-dominated solutions are clustered in the objective space and consequently, a post-optimality study is manually performed to find out some common design principles among those solutions. Finally, two reasonable design choices have been offered based on manufacturability issues.}, notes = {Also known as \cite{2002102} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Regnier-Coudert:2011:GECCOcomp, author = {Olivier Regnier-Coudert and John McCall}, title = {Privacy-preserving approach to bayesian network structure learning from distributed data}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {815--816}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002103}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In many situations, data is scattered across different sites, making the modelling process difficult or sometimes impossible. Some applications could benefit from collaborations between organisations but data security or privacy policies often act as a barrier to data mining on such contexts. In this paper, we present a novel approach to learning Bayesian Networks (BN) structures from multiple datasets, based on the use of Ensembles and an Island Model Genetic Algorithm (IMGA). The proposed design ensures no data is shared during the process and can fit many applications.}, notes = {Also known as \cite{2002103} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Koos:2011:GECCOcomp, author = {Sylvain Koos and Jean-Baptiste Mouret}, title = {Online adaptation of locomotion with evolutionary algorithms: a transferability-based approach}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {817--818}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002104}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Wheel-legged hybrid robots are versatile machines that can employ several locomotion modes; however, automatically choosing the right locomotion mode is still an open problem in robotics. We here propose that the robot autonomously discovers its locomotion mode using a multi-objective evolutionary optimisation and a fixed internal model. Three objectives are optimised: (1) the displacement speed computed with the internal model, (2) the predicted expended energy and (3) the transferability score, which reflects how well the behaviour of the real robot is in agreement with the predictions of the internal model. This transferability function is actively learnt by conducting 20 experiments on the real robot during the optimisation. We tested this approach with a wheel-legged robot in three situations (flat ground, grass-like terrain, tunnel-like environment): in each case, the evolutionary algorithm found efficient controllers for forward locomotion in 1 to 2 minutes.}, notes = {Also known as \cite{2002104} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pinto:2011:GECCOcomp, author = {Jos\'{e} Pinto and Rui Ferreira Neves and Nuno Horta}, title = {Fitness function evaluation for MA trading strategies based on genetic algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {819--820}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002105}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This paper presents a new approach to optimise an investment strategy based on moving averages (MA). The proposed approach optimises the entry and exit points, for both long and short positions, using a genetic algorithm (GA) kernel. This approach outperforms B&H strategy and explores alternative functions to the classical absolute return fitness function. The approach is demonstrated for major market indexes, such as, S&P 500, FTSE100, DAX30, NIKKEI225.}, notes = {Also known as \cite{2002105} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Yan:2011:GECCOcomp, author = {Lisa Jing Yan and Nick Cercone}, title = {Bayesian networks learning for strategies in artificial life}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {821--822}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002106}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary algorithms have been used to effectively generate solutions to artificial life problems. However, this process may take a number of generations to complete. Research to accelerate evolutionary search has been reported, yet, insights into this evolving process have not been analysed nor why certain characteristics are more dominant than others. This paper provides a systematic and causal explanation for these findings and why certain genes are superior. We use Bayesian Networks (BNs) to learn a graphical model to represent the learning process in the Artificial Life environment. BAyesian Network ANAlysis (BANANA) is then developed, which gives visual representation of the inter-connections among these characteristics and provides information for further insight into genetic fitness.}, notes = {Also known as \cite{2002106} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{MikikoSato:2011:GECCOcomp, author = {Mikiko Sato and Yuji Sato and Mitaro Namiki}, title = {Acceleration experiment of genetic computations for sudoku solution on multi-core processors}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {823--824}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002107}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We focus on parallel-processing effect for Sudoku-solving and we show that diversifying initial values can reduce the Sudoku solution time. In an experiment using the commercially available Intel Corei7 multi-core processor, we show that a correct solution rate of 100percent can be achieved with an average execution time of several tens of seconds even for super-difficult problems.}, notes = {Also known as \cite{2002107} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Lee:2011:GECCOcomp, author = {Jong-Hyun Lee and Chang Wook Ahn}, title = {Improving energy efficiency based on behavioral model in a swarm of cooperative foraging robots}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {825--826}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002108}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organisation, robustness, and flexibility. However, high initial and operating cost are main barriers in operating multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behaviour model regarding role division in concert with the search space division.}, notes = {Also known as \cite{2002108} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Sousa:2011:GECCOcomp, author = {Pedro Sousa and Carla Duarte and Manuel Barros and Jorge Guilherme and Nuno Horta}, title = {Optimal OpAmp sizing based on a fuzzy-genetic kernel}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {827--828}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002109}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In this paper an innovative fuzzy-genetic approach is proposed to address the problem of analog circuit sizing. The proposed approach introduces a fuzzy mutation operator which models expert design knowledge and this way not only avoids local minima but also reduces the search dynamically the space. The proposed approach is compared against a state-of-the art genetic approach, for the optimal operational amplifier sizing, and presents a faster convergence rate.}, notes = {Also known as \cite{2002109} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Jaskowski:2011:GECCOcomp, author = {Wojciech Jaskowski and Krzysztof Krawiec}, title = {How many dimensions in co-optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {829--830}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002110}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Co-optimisation test-based problems is a class of tasks approached typically with coevolutionary algorithms. It was recently shown that such problems exhibit underlying objectives that form internal problem structure, which can be extracted and analysed in order to drive the search or design better algorithms. The number of underlying objectives is the dimension of the problem, which is of great importance, since it may be a predictor of problem's difficulty. In this paper, we estimate the number of dimensions for Tic Tac Toe and the Density Classification Task.}, notes = {Also known as \cite{2002110} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Caschera:2011:GECCOcomp, author = {Filippo Caschera and Martin Hanczyc and Steen Rasmussen}, title = {Machine learning for drug design, molecular machines and evolvable artificial cells}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {831--832}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002111}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {An artificial cell is a complex chemical system with many components fabricated and assembled in the laboratory. The molecular components can be designed to interlock in a variety of different way to achieve the emergence of minimal life [1][2]. One experimental design is composed of three modules or sub-systems: lipid vesicles, a metabolic system and a cell free expression system. Due to the high number of molecular species and their non-trivial interactions in an artificial cell any prediction of the emerging properties in this high dimensional space of compositions is extremely difficult. Previously we have developed and used a machine learning process Evo-DoE (Evolutionary Design of Experiments) coupled with a robotic workstation for liquid handling to optimise a liposomal drug formulation [3] as well as a cell free expression system for the synthesis of the GFP (green fluorescent protein in vitro) [4]. In addition we have results of vesicle fusion providing a protocol to design a life-cycle for evolvable artificial cells. Now we propose how our technologies could be used to optimise artificial cells.}, notes = {Also known as \cite{2002111} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Dimitrov:2011:GECCOcomp, author = {Todor Dimitrov and Michael Baumann}, title = {Genetic algorithm with genetic engineering technology for multi-objective dynamic job shop scheduling problems}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {833--834}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002112}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Genetic algorithms were intensively investigated in various modifications and in combinations with other algorithms for solving the NP-hard scheduling problem. This extended abstract describes a genetic algorithm approach for solving large job shop problems that uses hints from the schedule evaluation in the genetic operators. The result is a hybrid genetic algorithm with smaller randomness and more managed search to find better solutions in shorter processing time. The hybridised genetic algorithm was tested with data from wafer production with thousands of jobs and hundreds of machine alternatives. The hybridised genetic algorithm not only achieved smaller tardiness in shorter computation time but was also able to reduce the sequence dependent change-over times between jobs in comparison with the classical genetic algorithm.}, notes = {Also known as \cite{2002112} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Iclanzan:2011:GECCOcomp, author = {David Icl\u{a}nzan and P\'{e}ter Istv\'{a}n F\"{u}l\"{o}p and Camelia Chira and Anca Gog}, title = {Towards the efficient evolution of particle-based computation in cellular automata}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {835--836}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002113}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {A fast compression based technique is proposed, capable of detecting promising emergent space-time patterns of cellular automata (CA). This information can be used to automatically guide the evolutionary search toward more complex, better performing rules. Results are presented for the most widely studied CA computation problem, the Density Classification Task (DCT), where incorporation of the proposed method almost always pushes the search beyond the simple block-expanding rules.}, notes = {Also known as \cite{2002113} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Souza:2011:GECCOcomp, author = {Daniel Leal Souza and Glauber Duarte Monteiro and Tiago Carvalho Martins and Victor Alexandrovich Dmitriev and Ot\'{a}vio Noura Teixeira}, title = {PSO-GPU: accelerating particle swarm optimization in CUDA-based graphics processing units}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {837--838}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002114}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This work presents a PSO implementation in CUDA architecture, aiming to speed up the algorithm on problems which has large amounts of data. PSO-GPU algorithm was designed to customisation, in order to adapt for any problem that can be solved by a PSO algorithm. By implementing PSO using CUDA architecture, each processing core of the GPU will be responsible for a portion of the overall processing operation, where each one of these pieces are handled and executed in a massive parallel environment, opening the possibility for solving problems that require a large processing load in considerably less time. In order to evaluate the performance of PSO-GPU algorithm two functions were used, both global optimisation problems, where without constraints (Griewank function) and other considering constraints, the Welded Beam Design (WBD).}, notes = {Also known as \cite{2002114} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Goodman:2011:GECCOcomp, author = {Erik D. Goodman}, title = {Introduction to genetic algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {839--860}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002116}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002116} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{O'Reilly:2011:GECCOcomp, author = {Una-May O'Reilly}, title = {Genetic programming: a tutorial introduction}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {861--874}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002117}, 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 will explain how the powerful capability of genetic programming is derived from modular algorithmic components: executable representations - e.g. parse-tree, linear and graph-based variation operators that preserve syntax and explore a variable length, hierarchical solution space appropriately chosen programming functions fitness function specification}, notes = {Also known as \cite{2002117} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Baeck:2011:GECCOcomp, author = {Thomas B\"{a}ck}, title = {Evolution strategies: basic introduction}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {875--898}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002118}, 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 and application examples are given.}, notes = {Also known as \cite{2002118} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{DeJong:2011:GECCOcomp, author = {Kenneth {De Jong}}, title = {Evolutionary computation: a unified approach}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {899--912}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002119}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to see the relationships between them, assess strengths and weaknesses, and make good choices for new application areas. This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it. Finally, the framework is used to identify some important open issues that need further research.}, notes = {Also known as \cite{2002119} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Pelikan:2011:GECCOcomp, author = {Martin Pelikan}, title = {Probabilistic model-building genetic algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {913--940}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002120}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs). Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimisation enables the design of optimisation techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and scheduling. The tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.}, notes = {Also known as \cite{2002120} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Butz:2011:GECCOcomp, author = {Martin V. Butz}, title = {Learning classifier systems}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {941--962}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002121}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {In the 1970s, John H. Holland designed Learning Classifier Systems (LCSs) as highly adaptive, cognitive systems. Since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995 and the modular analysis of several LCSs thereafter, LCSs have become a state-of-the-art machine learning system. Various publications have shown that LCSs can effectively solve data-mining problems, reinforcement learning problems, other predictive problems, and even cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, it was shown that performance is competitive or even superior, dependent on the setup and problem. Advantages are that LCSs are learning online, are very plastic and flexible, are applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualised, or even used to focus the progressive search on particular interesting subspaces. The Learning Classifier System tutorial provides a gentle introduction to LCSs and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.}, notes = {Also known as \cite{2002121} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Blum:2011:GECCOcomp, author = {Christian Blum}, title = {Ant colony optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {963--990}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002122}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002122} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Hansen:2011:GECCOcomp, author = {Nikolaus Hansen and Anne Auger}, title = {CMA-ES: evolution strategies and covariance matrix adaptation}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {991--1010}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002123}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimisation procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be formulated in a unified and comparatively simple framework. This introductory tutorial focuses on the most relevant algorithmic question: how should the parameters of the sample distribution be chosen and, in particular, updated in the generation sequence? First, two common approaches for step-size control are reviewed, one-fifth success rule and path length control. Then, Covariance Matrix Adaptation (CMA) is discussed in depth: rank-one update, the evolution path, rank-mu update. Invariance properties and the interpretation as natural gradient descent are touched upon. In the beginning, general difficulties in solving non-linear, non-convex optimisation problems in continuous domain are revealed, for example non-separability, ill-conditioning and ruggedness. Algorithmic design aspects are related to these difficulties. In the end, the performance of the CMA-ES is related to other well-known evolutionary and non-evolutionary optimisation algorithms, namely BFGS, DE, PSO,...}, notes = {Also known as \cite{2002123} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Miikkulainen:2011:GECCOcomp, author = {Risto Miikkulainen}, title = {Evolving neural networks}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1011--1028}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002124}, 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, we 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{2002124} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Rowe:2011:GECCOcomp, author = {Jonathan E. Rowe}, title = {Genetic algorithm theory}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1029--1052}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002126}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002126} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Jansen:2011:GECCOcomp, author = {Thomas Jansen and Frank Neumann}, title = {Computational complexity and evolutionary computation}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1053--1080}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002127}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002127} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Spector:2011:GECCOcomp, author = {Lee Spector}, title = {Evolving quantum computer algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1081--1110}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002128}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Computer science will be radically transformed if ongoing efforts to build large-scale quantum computers eventually succeed and if the properties of these computers meet optimistic expectations. Nevertheless, computer scientists still lack a thorough understanding of the power of quantum computing, and it is not always clear how best to use the power that is understood. This dilemma exists because quantum algorithms are difficult to grasp and even more difficult to write. Despite large-scale international efforts, only a few important quantum algorithms are documented, leaving many essential questions about the potential of quantum algorithms unanswered. These unsolved problems are ideal challenges for the application of automatic programming technologies. Genetic programming techniques, in particular, have already produced several new quantum algorithms and it is reasonable to expect further discoveries in the future. These methods will help researchers to discover how additional practical problems can be solved using quantum computers, and they will also help to guide theoretical work on both the power and limits of quantum computing. This tutorial will provide an introduction to quantum computing and an introduction to the use of evolutionary computation for automatic quantum computer programming. No background in physics or in evolutionary computation will be assumed. While the primary focus of the tutorial will be on general concepts, specific results will also be presented, including human-competitive results produced by genetic programming. Follow-up material is available from the presenter's book, Automatic Quantum Computer Programming: A Genetic Programming Approach, published by Springer and Kluwer Academic Publishers.}, notes = {Also known as \cite{2002128} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Brockhoff:2011:GECCOcomp, author = {Dimo Brockhoff}, title = {Evolutionary multiobjective optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1111--1136}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002129}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Many optimisation problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimised simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives. Evolutionary Multiobjective Optimization (EMO) algorithms are widely used in practice for solving multiobjective optimisation problems due to several reasons. As randomised blackbox algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run---opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovation) and it has been shown that certain single-objective problems become easier to solve with randomised search heuristics if the problem is reformulated as a multiobjective one (multi objectivization). This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, I am going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovation and principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, I will present some of the most important research results in areas such as indicator-based EMO, preference articulation, and performance assessment. Though classified as advanced, this tutorial is intended for both novices and regular users of EMO. Those without any knowledge will learn about the foundations of multiobjective optimisation and the basic working principles of state-of-the-art EMO algorithms. Open questions, presented throughout the tutorial, can serve for all participants as a starting point for future research and/or discussions during the conference.}, notes = {Also known as \cite{2002129} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Coello-Coello:2011:GECCOcomp, author = {Carlos Artemio {Coello Coello}}, title = {Constraint-handling techniques used with evolutionary algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1137--1160}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002130}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary Algorithms (EAs), when used for global optimisation, can be seen as unconstrained optimisation techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimisation concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimisation, differential evolution, evolution strategies, etc.) will be also discussed (as time allows).}, notes = {Also known as \cite{2002130} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Krasnogor:2011:GECCOcomp, author = {Natalio Krasnogor}, title = {(Computational) synthetic biology}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1161--1190}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002131}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The ultimate goal of systems biology is the development of executable in silico models of cells and organisms. Systems biology attempts to provide an integrative methodology, which while able to cope with -on the one hand- the data deluge that is being generated through high throughput experimental technologies -and on the other hand- emerging technologies that produce scarce often noisy data, would allow to capture within human understandable models and simulations novel biological knowledge. In its more modest instantiations, Systems Biology seeks to *clarify* current biological understandings by formalising what the constitutive elements of a biological system are and how they interact with each other and also it seeks to aid in the *testing* of current understandings against experimental data. In its most ambitious incarnations, however, it aims at *predicting* the behaviour of biological systems beyond current understanding and available data thus shedding light onto possible new experimental routes that could lead to better theoretical insights. Synthetic biology, on the other hand, aims to implement, in vitro/vivo, organisms whose behaviour is engineered. The field of synthetic biology holds a great promise for the design, construction and development of artificial (i.e. man-made) biological (sub systems thus offering viable new routes to genetically modified organisms, smart drugs as well as model systems to examine artificial genomes and proteomes. The informed manipulation of such biological (sub)systems could have an enormous positive impact on our societies, with its effects being felt across a range of activities such as the provision of healthcare, environmental protection and remediation, etc. The basic premise of synthetic biology is that methods commonly used to design and construct non-biological systems, such as those employed in the computational sciences and the engineering disciplines, could also be used to model and program novel synthetic biosystems. Synthetic biology thus lies at the interface of a variety of disciplines ranging from biology through chemistry, physics, computer science, mathematics and engineering. In this tutorial I will provide an entry level understanding to Systems and Synthetic Biology, it goals, methods and limitations. Furthermore I will describe the many potential applications of evolutionary computation to these two fields. Indeed, I believe that the EC community has a beautiful new application domain in which its methods could be both valued and challenged.}, notes = {Also known as \cite{2002131} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Rothlauf:2011:GECCOcomp, author = {Franz Rothlauf}, title = {Representations for evolutionary algorithms}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1191--1212}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002132}, 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. Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance. These concepts are *locality and *redundancy. Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Furthermore, redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotypes. Finally, a bias need not be the result of the representation but can also be caused by the search operator. The tutorial gives a brief overview about existing guidelines for representation design, illustrates the different aspects of representations, gives a brief overview of theoretical models describing the different aspects, and illustrates the relevance of the aspects with practical examples. It is expected that the participants have a basic understanding of EA principles.}, notes = {Also known as \cite{2002132} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Friedrich:2011:GECCOcomp, author = {Tobias Friedrich and Frank Neumann}, title = {Foundations of evolutionary multi-objective optimization}, booktitle = {GECCO 2011 Late breaking abstracts}, year = {2011}, editor = {Christian Blum}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1213--1232}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002133}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002133} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Witt:2011:GECCOcomp, author = {Carsten Witt}, title = {Theory of randomized search heuristics in combinatorial optimization}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1233--1260}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002135}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The rigorous mathematical analysis of randomised search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS), the Metropolis Algorithm (MA), Simulated Annealing (SA), and Evolutionary Algorithms (EAs) as well as more recent approaches such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Such heuristics are often applied to problems whose structure is not known or if there are not enough resources such as time, money, or knowledge to obtain good specific algorithms. It is widely acknowledged that a solid mathematical foundation for such heuristics is needed. Most designers of RSHs, however, rather focused on mimicking processes in nature (such as evolution) rather than making the heuristics amenable to a mathematical analysis. This is different to the classical design of (randomized) algorithms which are developed with their theoretical analysis of runtime (and proof of correctness) in mind. Despite these obstacles, research from the last about 15 years has shown how to apply the methods for the probabilistic analysis of randomized algorithms to RSHs. Mostly, the expected runtime of RSHs on selected problems is analysed. Thereby, we understand why and when RSHs are efficient optimisers and, conversely, when they cannot be efficient. The tutorial will give an overview on the analysis of RSHs for solving combinatorial optimization problems. Starting from the first toy examples such as the OneMax function, we approach more realistic problems and arrive at analysis of the runtime and approximation quality of RSHs even for NP-hard problems. Our studies treat not only simple RLS algorithms and SA but also more complex population-based EAs. The combinatorial optimisation problems that we discuss include the maximum matching problem, the partition problem and, in particular, the minimum spanning tree problem as an example where Simulated Annealing beats the Metropolis algorithm in combinatorial optimisation. Important concepts of the analyses will be described as well.}, notes = {Also known as \cite{2002135} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Miller:2011:GECCOcomp, author = {Julian F. Miller and Simon L. Harding}, title = {GECCO 2011 tutorial: cartesian genetic programming}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming}, pages = {1261--1284}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002136}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. 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). The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.}, notes = {Also known as \cite{2002136} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bacardit:2011:GECCOcomp, author = {Jaume Bacardit and Xavier Llor\`{a}}, title = {Large scale data mining using genetics-based machine learning}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1285--1310}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002137}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {We are living in the peta-byte era. We have larger and larger data to analyse, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task. Recent advances in representations, learning paradigms, and theoretical modelling have show the competitiveness of non EC techniques in herding large scale data analysis. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented computational resources on the edge of petascale computing. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualising the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to shed light to the above mentioned questions, following a roadmap that starts exploring what large scale means, and why large is a challenge and opportunity for GBML methods. As we will show later, opportunity has multiple facets: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms, and alternative programming models, each of them helping to make GBML very attractive for large-scale data mining. Given these building blocks, we will continue to unfold how can we model the scalability of the components of GBML systems targeting a better engineering effort that will make embracing large datasets routine. Finally, we will illustrate how all these ideas fit by reviewing real applications of GBML systems and what further directions will require serious consideration.}, notes = {Also known as \cite{2002137} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Doerr:2011:GECCOcomp, author = {Benjamin Doerr}, title = {Drift analysis}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1311--1320}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002138}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Drift analysis, introduced to the field of evolutionary computation by He and Yao ten years ago, quickly became one of the strongest tools to prove upper and lower bounds on the run-times of evolutionary algorithms. It has, however, the reputation of being difficult to use, both because it relies on deeper mathematical tools and because it needs a clever guess of a potential function. In this tutorial, after presenting the classical results, I will focus on the recently developed multiplicative drift analysis method. It often is easier to employ and yields stronger results, e.g., run-time bounds that hold with high probability. I will end with a number of open problems of different difficulties. The intended audience of the tutorial has some basic experience in theory, though no particular prerequisites are required.}, notes = {Also known as \cite{2002138} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Ochoa:2011:GECCOcomp, author = {Gabriela Ochoa and Matthew V. Hyde and Edmund K. Burke}, title = {Automated heuristic design}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1321--1342}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002139}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {This tutorial will discuss state-of-the-art techniques for automating the design of heuristic search methods, in order to remove or reduce the need for a human expert in the process of designing an effective algorithm to solve a search problem. Using machine learning or meta-level search, several approaches have been proposed in computer science, artificial intelligence and operational research. The aim is to develop methodologies which can adapt to different environments without manually having to customise the search, or its parameters, for each particular problem domain. This can be seen as one of the drawbacks of many current metaheuristic and evolutionary implementations, which tend to have to be customised for a particular class of problems or even specific problem instances. We have identified two main types of approaches to this challenge: heuristic selection, and heuristic generation. In heuristic selection the idea is to automatically combine fixed pre-existing simple heuristics or neighbourhood structures to solve the problem at hand; whereas in heuristic generation the idea is to automatically create new heuristics (or heuristic components) suited to a given problem or class of problems. This latter approach is typically achieved by combining, through the use of genetic programming for example, components or building-blocks of human designed heuristics. This tutorial will go over the intellectual roots and origins of both automated heuristic selection and generation, before discussing work carried out to date in these two directions and then focusing on some observations and promising research directions.}, notes = {Also known as \cite{2002139} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Sekanina:2011:GECCOcomp, author = {Luk\'{a}\v{s} Sekanina}, title = {Evolution of digital circuits}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming, cartesian genetic programming}, pages = {1343--1360}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002140}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Since the early 1990's researchers have begun to apply evolutionary algorithms to design electronic circuits. Nowadays it is evident that the evolutionary design approach can automatically create efficient electronic circuits in many domains. This tutorial surveys fundamental concepts of evolutionary circuit design. It introduces relevant search algorithms and basics of digital circuit design principles. Several case studies will be presented to demonstrate strength and weakness of the method, including evolutionary synthesis of gate-level circuits, image filter evolution in FPGA and evolution of benchmark circuits for evaluation of testability analysis methods. FPGAs will be presented as accelerators for evolutionary circuit design and circuit adaptation. Finally, it will be shown how to cope with the so-called scalability problem of evolutionary design which has been identified as the most important problem from the point of view of applications.}, notes = {Also known as \cite{2002140} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Bartz-Beielstein:2011:GECCOcompT, author = {Thomas Bartz-Beielstein and Mike Preuss}, title = {Automatic and interactive tuning of algorithms}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1361--1380}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002141}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Providing tools for algorithm tuning (and the related statistical analysis) is the main topic of this tutorial. This tutorial provides the necessary background for performing algorithm tuning with state-of-the-art tools. We will discuss pros and cons of manual, interactive, and automatic tuning of randomised algorithms such as Genetic Algorithms, Differential Evolution, Particle Swarm, and Evolution Strategies. Moreover, we highlight the important components of experimental work such as proper setup, visualisation, and reporting and refer to the most prominent mistakes that may occur, giving examples for failed and successful experiments. The Sequential Parameter Optimisation Toolbox (SPOT) is introduced as an example, being freely available via CRAN (free R package server network), see http://cran.r-project.org/web/packages/SPOT/index.html Other tuning approaches such as F-Race, REVAC and ParamILS will be discussed as well.}, notes = {Also known as \cite{2002141} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Sudholt:2011:GECCOcomp, author = {Dirk Sudholt}, title = {Theory of swarm intelligence}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1381--1410}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002142}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The theory of swarm intelligence has made rapid progress in the last 5 years. Following a very successful line of research in evolutionary computation, various results on the computational complexity of swarm intelligence algorithms have appeared. These results shed light on the working principles of swarm intelligence algorithms, help to identify the impact of parameters and other design choices on performance, and contribute to a solid theoretical foundation of swarm intelligence. This tutorial will give a comprehensive overview of theoretical results on swarm intelligence algorithms, with an emphasis on their computational complexity. In particular, it will be shown how techniques for the analysis of evolutionary algorithms can be used to analyse swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The tutorial will be divided into a first, larger part on ant colony optimisation (ACO) and a second, smaller part on particle swarm optimization (PSO). For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimisation demonstrate that the choice of the pheromone update strategy and the evaporation rate has a drastic impact on the running time, even for very simple functions like ONEMAX. We will also elaborate on the effect of using local search within the ACO framework. In terms of combinatorial optimisation problems, we will look at the performance of ACO for minimum spanning trees, shortest path problems, and the TSP. For particle swarm optimisation, the tutorial will cover results on PSO for pseudo-Boolean optimisation as well as a discussion of theoretical results in continuous spaces.}, notes = {Also known as \cite{2002142} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Wagner:2011:GECCOcomp, author = {Stefan Wagner and Gabriel Kronberger}, title = {Algorithm and experiment design with heuristiclab: an open source optimization environment for research and education}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1411--1438}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002143}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The tutorial demonstrates how to apply and analyse metaheuristics using HeuristicLab, an open source optimisation environment. It will be shown how to parametrise and execute evolutionary algorithms to solve combinatorial optimisation problems (travelling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees will learn how to assemble different algorithms and parameter settings to a large scale optimization experiment and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results will be compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it will be sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modelled using the graphical algorithm designer. Additional details on HeuristicLab can be found at http://dev.heuristiclab.com.}, notes = {Also known as \cite{2002143} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Moraglio:2011:GECCOcompT, author = {Alberto Moraglio}, title = {Geometry of evolutionary algorithms}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1439--1468}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002144}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {The various flavors of Evolutionary Algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms. Ultimately, the origin of the differences of the various flavors of Evolutionary Algorithms is rooted in the solution representation and relative genetic operators. Are these differences only superficial? Is there a deeper unity encompassing all Evolutionary Algorithms beyond the specific representation? Is a general mathematical framework unifying search operators for all solution representations at all possible? The aim of the tutorial is to introduce a formal, but intuitive, unified point of view on Evolutionary Algorithms across representations based on geometric ideas, which provides a possible answer to the above questions. It also presents the benefits for both theory and practice brought by this novel perspective. The key idea behind the geometric framework is that search operators have a dual nature. The same search operator can be defined (i) on the underlying solution representations and, equivalently, (ii) on the structure of the search space by means of simple geometric shapes, like balls and segments. These shapes are used to delimit the region of space that includes all possible offspring with respect to the location of their parents. The geometric definition of a search operator is of interest because it can be applied - unchanged - to different search spaces associated with different representations. This, in effect, allows us to define exactly the same search operator across representations in a rigorous way. The geometric view on search operators has a number of interesting consequences of which this tutorial will give a comprehensive overview. These include (i) a straightforward view on the fitness landscape seen by recombination operators, (ii) a formal unification of many pre-existing search operators across representations, (iii) a principled way of designing crossover operators for new representations, (iv) a principled way of generalising search algorithms from continuous to combinatorial spaces, and (v) the potential for a unified theory of evolutionary algorithms across representations.}, notes = {Also known as \cite{2002144} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Tomassini:2011:GECCOcomp, author = {Marco Tomassini}, title = {Evolutionary games: the Darwin connection}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {}, pages = {1469--1480}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002145}, publisher = {ACM}, publisher_address = {New York, NY, USA}, abstract = {Evolutionary game theory has been introduced essentially by biologists in the seventies and has immediately diffused into economical and sociological circles. Today, it is a main pillar of the whole edifice of game theory and widely used both in theory and in applications. This tutorial aims at presenting evolutionary game theory in an easy, yet rigorous way, and to relate it with other approaches in game theory. The material presented does not require a previous acquaintance with standard game theory: these fundamentals will be developed in the first part of the tutorial, which is self-contained. In the second part the main concepts of the evolutionary and dynamical approach will be introduced, namely the concept of an evolutionarily stable strategy and the replicator dynamics. The analogies between Nash equilibria, evolutionarily stable strategies, and rest points of the dynamics will be explained. The concept of strategy errors and stochastically stable states as well as the relationships with mean dynamics and stability in the long run will also be briefly introduced. The main ideas will be illustrated using simple well known paradigmatic games such as the Prisoner's Dilemma, Hawks and Doves, and coordination games among others. Finally, some recent exciting trends in evolutionary games on networks will be introduced and discussed.}, notes = {Also known as \cite{2002145} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @inproceedings{Silva:2011:GECCOcomp, author = {Sara Silva}, title = {Handling bloat in GP}, booktitle = {GECCO 2011 Tutorials}, year = {2011}, editor = {Darrell Whitley}, isbn13 = {978-1-4503-0690-4}, keywords = {genetic algorithms, genetic programming}, pages = {1481--1508}, month = {12-16 July}, organisation = {SIGEVO}, address = {Dublin, Ireland}, doi = {doi:10.1145/2001858.2002146}, publisher = {ACM}, publisher_address = {New York, NY, USA}, notes = {Also known as \cite{2002146} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, } @proceedings(Bacardit:2011:GECCOcomp, title = {GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation}, year = 2011, editor = {Jaume Bacardit and Ivan Tanev and Joern Mehnen and Thomas Bartz-Beielstein and David Davis and Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen and Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz and Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis and Stephen L. Smith and Stefano Cagnoni and Robert Patton and William Rand and Forrest Stonedahl and Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward and Maria J. Blesa and Christian Blum and Steven Gustafson and Ekaterina Vladislavleva and Mark Hauschild and Martin Pelikan and Ender Ozcan and Andrew J. Parkes and Jonathan Rowe and Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro and Miguel Nicolau and Darrell Whitley}, address = {Dublin, Ireland}, publisher_address = {New York, NY, USA}, month = {12-16 July}, organisation = {SIGEVO}, keywords = {genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics, computational, systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of distribution algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and developmental systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Evolutionary computation in practice, Evolutionary computation techniques for constraint handling, Fourteenth international workshop on learning classifier systems, Computational intelligence on consumer games and graphics hardware (CIGPU), Medical applications of genetic and evolutionary computation (MedGEC), Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop, 1st workshop on evolutionary computation for designing generic algorithms, Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011), 3rd symbolic regression and modeling workshop for GECCO 2011, Optimization by building and using probabilistic models (OBUPM-2011), Scaling behaviours of landscapes, parameters and algorithms, GreenIT evolutionary computation, Graduate students workshop, Late breaking abstracts, Specialized techniques and applications, Tutorials}, ISBN13 = {978-1-4503-0690-4}, notes = {Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.}, ) WorkShop_count[Evolutionary computation in practice] not used enough. 1 WorkShop[Evolutionary computation in practice] not used.