CONSTRAINED OPTIMIZATION VIA GENETIC ALGORITHMS

This paper presents an application of genetic algorithms (GAs) to nonl inear constrained optimization. GAs are general purpose optimization a lgorithms which apply the rules of natural genetics to explore a given search space. When GAs are applied to nonlinear constrained problems, constraint handling becomes an important issue. The proposed search a lgorithm is realized by GAs which utilize a penalty function in the ob jective function to account for violation. This extension is based on systematic multi-stage assignments of weights in the penalty method as opposed to single-stage assignments in sequential unconstrained minim ization. The experimental results are satisfactory and agree well with those of the gradient type methods.