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 focus on one of them | decision making. More speciically, we address the following questions: given two diierent methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum eeciency? These are fundamental questions that come to mind when we think about hybridization and here they are investigated in detail. We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching. Essentially, it can be viewed as a stochastic learning automata 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 are needed to solve the problem eeciently.