Applications of Evolutionary Computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog
Springer Science & Business Media, Mar 14, 2008 - Computers - 701 pages
Evolutionary computation (EC) techniques are e?cient, nature-inspired pl- ning and optimization methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these me- ods can be used in the context of problem solving, optimization, and machine learning. A large and continuously increasing number of researchers and prof- sionals make use of EC techniques in various application domains. This volume presents a careful selection of relevant EC examples combined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and should help and - spire researchers and professionals to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoWorkshops 2008, which consisted of a range of workshops on application-oriented aspects of EC. Since 1998, EvoWorkshops has provided a unique opportunity for EC researchers to meet and discuss applicationaspectsofECandhasservedasanimportantlink between EC research and its application in a variety of domains. During these ten years new workshops have arisen, some have disappeared, while others have matured to become conferences of their own, such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO last year.
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adaptation agents applied approach artiﬁcial average behavior Berlin Heidelberg 2008 chromosome circuit classiﬁcation components conﬁguration constraints convergence cost crossover data set deﬁned detection diﬀerent distribution dynamic eﬀect eﬃcient environment evaluation evolution evolution strategies evolutionary algorithm Evolutionary Computation evolved EvoWorkshops 2008 experimental experiments ﬁnal ﬁnd ﬁnding ﬁrst ﬁtness function ﬁxed frequency fuzzy gene genetic algorithm genetic programming Giacobini graph Heidelberg IEEE implemented implied volatility individual initial input interactions iteration LNCS Machine Learning memory method minimal mutation Neural Networks node object obtained operator optimization problems output paper parameters Particle Swarm Optimization patterns performance pixels population proﬁt proposed random randomly reconﬁgurable represents robot runs scheduling search space Section selection sequence signiﬁcant simulation solution solve Sortino Ratio speciﬁc Springer Springer-Verlag Berlin Heidelberg strategy structure Table techniques tion traﬃc University variables vector volatility smile