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Titlebook: Introduction to Evolutionary Computing; A. E. Eiben,J. E. Smith Textbook 20031st edition Springer-Verlag Berlin Heidelberg 2003 Evolution

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书目名称Introduction to Evolutionary Computing
编辑A. E. Eiben,J. E. Smith
视频video
概述At present the only authored book that contains a complete overview of the field of evolutionary computing, treating all "dialects" and important algorithm variants: GAs, ES, EP, GP, LCS, MAs, MOEAs.P
丛书名称Natural Computing Series
图书封面Titlebook: Introduction to Evolutionary Computing;  A. E. Eiben,J. E. Smith Textbook 20031st edition Springer-Verlag Berlin Heidelberg 2003 Evolution
描述.Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research...This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied. ..Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields..
出版日期Textbook 20031st edition
关键词Evolution strategies; Evolutionary computation; Evolutionary computing; Evolutionary programming; algori
版次1
doihttps://doi.org/10.1007/978-3-662-05094-1
isbn_softcover978-3-642-07285-7
isbn_ebook978-3-662-05094-1Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightSpringer-Verlag Berlin Heidelberg 2003
The information of publication is updating

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Parameter Control in Evolutionary Algorithms, different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem.
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Learning Classifier Systems,where . between the population members (i.e., rules) is crucial. In this aspect LCS systems differ significantly from the other four members of the evolutionary algorithm family, where individuals strictly compete with each other.
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