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Titlebook: Parallel Problem Solving from Nature - PPSN VII; 7th International Co Juan Julián Merelo Guervós,Panagiotis Adamidis,Jos Conference proceed

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书目名称Parallel Problem Solving from Nature - PPSN VII
副标题7th International Co
编辑Juan Julián Merelo Guervós,Panagiotis Adamidis,Jos
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Parallel Problem Solving from Nature - PPSN VII; 7th International Co Juan Julián Merelo Guervós,Panagiotis Adamidis,Jos Conference proceed
描述We are proud to introduce the proceedings of the Seventh International C- ference on Parallel Problem Solving from Nature, PPSN VII, held in Granada, Spain, on 7–11 September 2002. PPSN VII was organized back-to-back with the Foundations of Genetic Algorithms (FOGA) conference, which took place in Torremolinos, Malaga, Spain, in the preceding week. ThePPSNseriesofconferencesstartedinDortmund,Germany[1].Fromthat pioneering meeting, the event has been held biennially, in Brussels, Belgium [2], Jerusalem, Israel [3], Berlin, Germany [4], Amsterdam, The Netherlands [5], and Paris, France [6]. During the Paris conference, several bids to host PPSN 2002 were put forward; it was decided that the conference would be held in Granada with Juan J. Merelo Guerv´ os as General Chairman. The scienti?c content of the PPSN conference focuses on problem-solving paradigms gleaned from natural models, with an obvious emphasis on those that display an innate parallelism, such as evolutionary algorithms and ant-colony optimization algorithms. The majority of the papers, however, concentrate on evolutionary and hybrid algorithms, as is shown in the contents of this book and itspredecessors.Thiseditionof
出版日期Conference proceedings 2002
关键词Algorithms; Automat; algorithm; automata; coevolution; evolution; evolutionary algorithm; learning; optimiza
版次1
doihttps://doi.org/10.1007/3-540-45712-7
isbn_softcover978-3-540-44139-7
isbn_ebook978-3-540-45712-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2002
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Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming of each parent. We test Complementary Phenotype Selection in three genetic programming domains: Boolean 6-Multiplexer, Intertwined Spirals Classification, and Sunspot Prediction. We demonstrate significant performance gains over the control methods in all of them and present a preliminary analysis of these results.
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Binary Representations of Integers and the Performance of Selectorecombinative Genetic Algorithmsbinative GAs using binary representations of integers is determined by the resulting building blocks and not by the structure of the search space resulting from the Hamming distances between the individuals. Therefore, the performance difference between the encodings can be explained by analyzing the fitness of the resulting schemata.
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