Self-adaptation procedures in genetic algorithms applied to the optimal design of composite structures

It is recognized that the efficiency of Genetic Algorithms improves if some adaptive rules are included. In this work, adaptive properties in Genetic Algorithms applied to structural optimization are studied. The adaptive rules work by using additional information related to the behavior of state and design variables of the structural problem. At each generation, the self-adaptation of the genetic parameters to evolutionary conditions attempts to improve the efficiency of the genetic search. The introduction of adaptive rules occurs at three levels: (i) when defining the search domain in 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.