Genetic algorithms and tabu search: Hybrids for optimization

Abstract

Genetic algorithms and tabu search have a number of significant differences. They also have some common bonds, often unrecognized. We explore the nature of the connections between the methods, and show that a variety of opportunities exist for creating hybrid approaches to take advantage of their complementary features. Tabu search has pioneered the systematic exploration of memory functions in search processes, while genetic algorithms have pioneered the implementation of methods that exploit the idea of combining solutions. There is also another approach, related to both of these, that is frequently overlooked. The procedure called scatter search, whose origins overlap with those of tabu search (and roughly coincide with the emergence of genetic algorithms) also proposes mechanisms for combining solutions, with useful features that offer a bridge between tabu search and genetic algorithms. Recent generalizations of scatter search concepts, embodied in notions of structured combinations and path relinking, have produced effective strategies that provide a further basis for integrating GA and TS approaches. A prominent TS component called strategic oscillation is susceptible to exploitation by GA processes as a means of creating useful degrees of diversity and of allowing effective transitions between feasible and infeasible regions. The independent success of genetic algorithms and tabu search in a variety of applications suggests that each has features that are valuable for solving complex problems. The thesis of this paper is that the study of methods that may be created from their union can provide useful benefits in diverse settings.