Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways

Two methods of hybridizing genetic algorithms (GA) with hill-climbing for global optimization are investigated. The first one involves two interwoven levels of optimization-evolution (GA) and individual learning (hill-climbing)-which cooperate in the global optimization process. The second one consists of modifying a GA by the introduction of new genetic operators or by the alteration of traditional ones in such a way that these new operators capture the basic mechanisms of hill-climbing. The simplex-GA is one of the possibilities explained and tested. These two methods are applied and compared for the maximization of complex functions defined in high-dimensional real space