Constraint handling through a multiobjective optimization technique


Even when genetic algorithms (GAs) have been quite successful in wide range of applications [6,1], their use in constrained optimization problems raises several issues to which a considerable amount of research has been devoted in the last few years. From these issues, one of the most important ones is how to incorporate constraints of any sort (linear, non-linear, equality or inequality) into the fitness function as to guide the seach properly. Due to the nature of the problems for which the GA is more suitable, it is normally quite difficult (or even impossible) to know the shape of the search space, and therefore is not easy to produce special operators and/or to explore it efficently, unless we severely constraint the range of applications for which such approach will be useful.