Titlebook: Representations for Genetic and Evolutionary Algorithms; Franz Rothlauf Book 2002 Physica-Verlag Heidelberg 2002 GEA.Theorey of representa

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书目名称Representations for Genetic and Evolutionary Algorithms影响因子(影响力)




书目名称Representations for Genetic and Evolutionary Algorithms影响因子(影响力)学科排名




书目名称Representations for Genetic and Evolutionary Algorithms网络公开度




书目名称Representations for Genetic and Evolutionary Algorithms网络公开度学科排名




书目名称Representations for Genetic and Evolutionary Algorithms被引频次




书目名称Representations for Genetic and Evolutionary Algorithms被引频次学科排名




书目名称Representations for Genetic and Evolutionary Algorithms年度引用




书目名称Representations for Genetic and Evolutionary Algorithms年度引用学科排名




书目名称Representations for Genetic and Evolutionary Algorithms读者反馈




书目名称Representations for Genetic and Evolutionary Algorithms读者反馈学科排名

Fallibility

Book 2002blem representation has often been taken as given. This monograph breaks with this tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The

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Time-Quality Framework for a Theory-Based Analysis and Design of Representations,formulate a theory describing the behavior of GEAs more precisely (Goldberg, 1989c; Bäck, Fogel, & Michalewicz, 1997). The existing elements of GEA theory explain quite accurately the influence of many important GEA parameters, as well as selection, recombination, or mutation methods on the performa

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CAMP

Analysis of Tree Representations,ary phenotypes, but also for problems where the phenotypes are integers. However, it is possible to go one step further and to look at problems where the phenotypes and the genotypes use completely different types of representations. One example for these type of problems are tree optimization probl

金哥占卜者

Design of Tree Representations,sented in chapter 4, the influence of redundant, exponentially scaled, or low locality representations on GEA performance. However, the presented framework can not only be used to analyze the performance of existing representations, but also to develop representations in a theory-guided manner.

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Performance of Genetic and Evolutionary Algorithms on Tree Problems,to the properties of representations were based on theory and helped us to understand what happens when GEAs use a specific representation. However, in practice, GEA users are often less interested in theory about representations but want simple instruments for a fast and rough prediction of the exp

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