Active components of metaheuristics in cellular genetic algorithms

A cellular genetic algorithm (cGA) is a powerful metaheuristic that has been successfully used since its creation to solve optimization problems. Over the past few years, interest in hybrid metaheuristics has also grown considerably. Research into cross fertilization between algorithms has provided extremely efficient search techniques in the past. In this paper we present a new way of hybridizing a metaheuristic through active components of other metaheuristics. We also introduce a novel methodology for identifying what an active component is. The active components detected are later inserted in a host metaheuristic so as to enhance its performance with regards to efficiency and accuracy (computational symbiosis). In the approach presented here we enhance a cGA, the host metaheuristic, with identified active components of other metaheuristics. After using this computational symbiosis, we analyze the performance of the new resulting algorithms by evaluating them on a set of different well-known discrete problems. The results obtained are objectively satisfactory in efficacy and efficiency.