Genetic Algorithms and Neighbourhood Search

Genetic algorithms (GAs) have proved to be a versatile and eeective approach for solving combinatorial optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. These often involve incorporating other methods such as simulated annealing or local optimization as anàdd-on' extra to the basic GA strategy of selection and reproduction. Here, we explore an alternative perspective which views genetic algorithms as a generalization of neighbourhood search methods. It is not the intention to present a fully worked-out statement as to what sort of neighbourhood search a GA is. Rather, it is to investigate some of the parallels, and to suggest some areas for further research which may enhance our understanding of both neighbourhood search and genetic algorithms.