Applications of Evolutionary Computation: EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, Proceedings
Cecilia Di Chio, Stefano Cagnoni, Carlos Cotta, Marc Ebner, Aniko Ekart, Anna I. Esparcia-Alcázar, Juan J. Merelo, Ferrante Neri, Mike Preuss, Hendrik Richter, Georgios N. Yannakakis, Julian Togelius
Springer Science & Business Media, Apr 19, 2011 - Computers - 367 pages
This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.
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actions adaptive applied approach attractors average behaviour benchmark Berlin Heidelberg 2011 Boolean networks brain-computer interface Chio complex Computational Intelligence covered radius crossover dataset defined Differential Evolution distribution DTSP dynamic environments encoding evaluation EvoApplications 2011 Evolution Strategies evolutionary algorithms Evolutionary Computation evolved experiments fitness function fitness landscape fitness-distance genetic algorithm global global optimum Heidelberg heuristic hyper-heuristics IEEE implementation individuals initial input instances iteration L-System learning LNCS metaheuristics method MIACO move multi-model mutation nodes NPCs obtained operator optimisation optimization problems P-ACO paper parameters particle swarm particle swarm optimization performance pixel player playing population Proceedings proposed PSO-DE random randomly reinforcement learning robot S-ACO s+ s+ s+ sampling search space Section segmentation selection simulations solutions solve Springer Stragotiator subgroup swarm optimization target techniques tion topology tournament track University update values variable