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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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Summary, that are hard to place into one of the historical categories. Thus after the introductory material in Chaps. 1 and 2, we proceeded to describe these four main dialects of EC in Chaps. 3–6. In Table 15.1 we give a brief summary of these dialects.
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Introduction, from the process of natural evolution- That some computer scientists have chosen natural evolution as a source of inspiration is not surprising, for the power of evolution in nature is evident in the diverse species that make up our world, with each tailored to survive well in its own niche. The fu
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Evolution Strategies,e a very useful feature in evolutionary computing: . of strategy parameters. In general, self-adaptivity means that some parameters of the EA are varied during a run in a specific manner: the parameters are included in the chromosomes and coevolve with the solutions. This feature is inherent in mode
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Evolutionary Programming,can be identified as being the “standard”, or typical, version of genetic algorithms, evolution strategies, or genetic programming. For EP such a standard version is hard to define for reasons discussed later in this chapter. The summary of EP in Table 5.1 is therefore a representative rather than a
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