Self-adaptation in Genetic Algorithms applied to structural optimization

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 using additional information related with the behavior of state and design variables of the structural problem. At each generation the self-adaptation of genetic parameters to evolutionary conditions aims to improve the efficiency of genetic search. The introduction of adaptive rules occurs at three levels: (i) when defining the search domain at each generation; (ii) considering a crossover operator based 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. Numerical examples showing these benefits are presented