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Genetic algorithms fitness evaluation

Representation requires that the designer of a typical evolutionary computation algorithm (EA) formulates one inadaptable blueprint for the solution of some problem, then present the variables of that blueprint in a form that is amenable to manipulation by the genetic operators of the EA. Fitness evaluation, on the other hand, has limited GA in two distinct ways (1) it has limited environmental feedback to the confines of a formula or algorithm, which reflects accurately and exclusively the quality of the complete candidate solution from the perspective of the human designer. In addition, (2) fitness evaluation has proven to be the most computationally costly part of a typical EA. Note that elaborate developmental mappings actually increase that computational cost. However, our interest here lies in the limiting effects of representation. [Pg.324]

See other pages where Genetic algorithms fitness evaluation is mentioned: [Pg.717]    [Pg.113]    [Pg.674]    [Pg.41]    [Pg.293]    [Pg.324]    [Pg.202]    [Pg.109]    [Pg.475]    [Pg.86]    [Pg.162]    [Pg.285]    [Pg.132]    [Pg.132]    [Pg.320]    [Pg.23]    [Pg.15]    [Pg.88]    [Pg.105]    [Pg.97]    [Pg.98]    [Pg.156]    [Pg.244]    [Pg.132]    [Pg.218]    [Pg.113]    [Pg.232]    [Pg.344]    [Pg.213]    [Pg.413]    [Pg.63]    [Pg.701]    [Pg.234]    [Pg.10]    [Pg.625]    [Pg.160]    [Pg.2034]    [Pg.2034]    [Pg.259]    [Pg.233]    [Pg.32]    [Pg.286]    [Pg.5]    [Pg.90]    [Pg.74]    [Pg.703]    [Pg.1059]    [Pg.339]    [Pg.348]   
See also in sourсe #XX -- [ Pg.2 , Pg.1118 , Pg.1121 ]



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