Impact of the replacement heuristic in a grouping genetic algorithm

Abstract

The grouping genetic algorithm (GGA), developed by Emmanuel Falkenauer, is a genetic algorithm whose encoding and operators are tailored to suit the special structure of grouping problems. In particular, the crossover operator for a GGA involves the development of heuristic procedures to restore group membership to any entities that may have been displaced by preceding actions of the operator. In this paper, we present evidence that the success of a GGA is heavily dependent on the replacement heuristic used as a part of the crossover operator. We demonstrate this by comparing the performance of a GGA that uses a naı̈ve replacement heuristic (GGA 0) to a GGA that includes an intelligent replacement heuristic (GGA CF). We evaluate both the naı̈ve and intelligent approaches by applying each of the two GGAs to a well-known grouping problem, the machine-part cell formation problem. The algorithms are tested on problems from the literature as well as randomly generated problems. Using two measures of effectiveness, grouping efficiency and grouping efficacy, our tests demonstrate that adding intelligence to the replacement heuristic enhances the performance of a GGA, particularly on the larger problems tested. Since the intelligence of the replacement heuristic is highly dependent on the particular grouping problem being solved, our research brings into question the robustness of the GGA. Scope and purpose Our research investigates the significance of the replacement heuristic used as a part of the crossover operator in a grouping genetic algorithm (GGA). We test two GGAs and compare their replacement heuristics using test problems from the well-known machine-part cell formation domain. The purpose of our research is three-fold. First, we compare and contrast the GGA with standard GA to improve understanding of how they differ in problem representation and operation. Second, we provide evidence that GGA is limited not only to problems where the objective is to form groups, but also to problems where it is practical to incorporate a substantial amount of problem-specific information. Third, we estimate the impact that the GGA replacement heuristic has on performance. Results indicate that GGA performs up to 40% worse when problem-specific knowledge is not incorporated into the replacement heuristic.