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Titlebook: Parallel Problem Solving from Nature, PPSN XI; 11th International C Robert Schaefer,Carlos Cotta,Günter Rudolph Conference proceedings 2010

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书目名称Parallel Problem Solving from Nature, PPSN XI
副标题11th International C
编辑Robert Schaefer,Carlos Cotta,Günter Rudolph
视频video
概述Fast conference proceedings.Up to date results.State-of-the-art report
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Parallel Problem Solving from Nature, PPSN XI; 11th International C Robert Schaefer,Carlos Cotta,Günter Rudolph Conference proceedings 2010
出版日期Conference proceedings 2010
关键词agents; algorithms; evolution; evolutionary algorithm; evolutionary computation; grammar; intelligence; lea
版次1
doihttps://doi.org/10.1007/978-3-642-15871-1
isbn_softcover978-3-642-15870-4
isbn_ebook978-3-642-15871-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

书目名称Parallel Problem Solving from Nature, PPSN XI影响因子(影响力)




书目名称Parallel Problem Solving from Nature, PPSN XI影响因子(影响力)学科排名




书目名称Parallel Problem Solving from Nature, PPSN XI网络公开度




书目名称Parallel Problem Solving from Nature, PPSN XI网络公开度学科排名




书目名称Parallel Problem Solving from Nature, PPSN XI被引频次




书目名称Parallel Problem Solving from Nature, PPSN XI被引频次学科排名




书目名称Parallel Problem Solving from Nature, PPSN XI年度引用




书目名称Parallel Problem Solving from Nature, PPSN XI年度引用学科排名




书目名称Parallel Problem Solving from Nature, PPSN XI读者反馈




书目名称Parallel Problem Solving from Nature, PPSN XI读者反馈学科排名




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Parallel Problem Solving from Nature, PPSN XI978-3-642-15871-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Bioreactor Control by Genetic ProgrammingGenetic programming is applied to the problem of bioreactor control. This highly nonlinear problem has previously been suggested as one of the challenging benchmarks to explore new ideas for building automatic controllers. It is shown that the derived control law is successful in a number of test cases.
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A Novel Smart Multi-Objective Particle Swarm Optimisation Using Decompositionposed in MOEA/D, whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle
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