Parameter control of metaheuristics with genetic fuzzy systems

Abstract

This paper introduces a genetic fuzzy system for parameter control of metaheuristics. Two basic metaheuristics have been considered as examples, genetic algorithm and tabu search. The controlled parameters of the tabu search are the short and long term memories. Parameters of the genetic algorithm under control are the mutation and reproduction rates. Fuzzy rule-based models offer a natural mechanism to describe global behavior as a combination of control rules. They also inherit a means to gradually shift between control rules which jointly defines a control strategy. They are a natural candidate to construct parameter control strategies because they provide a way to develop decision mechanisms based on the specific nature of search regions and transitions between their boundaries. An application example using the classic vehicle routing problem with time windows is included to evaluate the genetic fuzzy system performance. Experimental results show that GFS-controlled metaheuristics improve search behavior and solution quality when compared against standard, constant parameters genetic and tabu search approaches. It also provides reasonably good suboptimal solutions faster than specially tailored exact methods reported in the literature.