An Imanishism-based genetic algorithm for sampling various Pareto-optimal solutions: an application to the multiobjective resource division problem

摘要

For sampling various solutions from the entire Pareto front of the multiobjective resource division problem, a new Genetic Algorithm (GA) based on an evolutionary theory advocated by Kinji Imanishi is proposed. First, two types of distance between two individuals, namely, structural and functional distances, are introduced and used to define four types of relation between them, namely, homogeneous, heterogeneous, homologous, and analogous species. Then, for keeping a variety of species within a population as far as possible, a new generation alternation model with variable population size is presented. In order to find Pareto-optimal solutions effectively, a new genetic operation that combines conventional harmonic crossover with a local optimization algorithm is also proposed. Finally, the advantage of the Imanishism-based GA is demonstrated through computational experiments conducted on two- and three-objective problem instances.