Self-adaptation in genetic algorithms applied to structural optimization

Abstract

It is recognized that Genetic Algorithms efficiency improves clearly if some adaptive rules are included. In the present work,adaptive properties in Genetic Algorithms applied to structural optimization are studied. Here, adaptive rules perform usingadditional information related with the behavior of state and design variables of the structural problem. At each generation the selfadaptationof genetic parameters to evolutionary conditions aims to improve the efficiency of genetic search. The introduction ofadaptive rules occurs at three levels: (i) when defining the search domain at each generation; (ii) considering a crossover operatorbased on commonality and local improvements; and (iii) by controlling mutation including behavioral data.Self-adaptation has proved to be highly beneficial in automatically and dynamically adjusting evolutionary parameters. Numericalexamples showing these benefits are presented.