An Adaptive Algorithm for constrained optimization problems

Several methods have been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems. The most widely used are those based on penalty function, thanks to their simplicity. In this paper, we propose a new adaptative penalty approach for solving constrained optimization problems, based on the amount of feasible individuals in the population. This method uses specific selection and recombination operators adapted to the penalisation strategy. We demonstrate the power of this approach on the eleven test cases presented in the litterature