A Segregated Genetic Algorithm for Constrained Structural Optimization

The problem of minimizing by genetic algorithms the weight of a composite laminate subjected to various failure constraints is considered. Constraints are accounted for through penalty functions. The amount of penalty for each constraint violation is typically controlled by a penalty parameter that has a crucial innuence on the performance of the genetic algorithm. An optimal value of each penalty parameter exists. It is larger than the smallest value of the penalty for which the global optimum is feasible. A gen-erational elitist genetic algorithm is found to be less eecient for laminate optimization than a genetic algorithm with a more conservative selection procedure (a \superelitist" algorithm). A segregated genetic algorithm is proposed that uses a double penalty strategy and is superelitist. The segregated genetic algorithm performs as well as the supereli-tist genetic algorithm for optimal amounts of penalty. In addition, the segregated genetic algorithm is less sensitive to the choice of penalty parameters.