Decision making in a hybrid genetic algorithm

There are several issues that need to be taken In consideration when designing a hybrid problem solver. This paper focuses on one of them - decision making. More specifically, we address the following questions: given two different methods, how to get the mort out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridising genetic algorithms (GAs) bared on a concept that decision theorists call probability matching and we use It to combine an elitist selecto-recombinative GA with a simple hillclimber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC us needed to solve the problem efficiently.