エリート解の集中的な交叉メカニズムを持つ分散遺伝的アルゴリズムのTSPにおける解探索性能の検討

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  • Distributed Genetic Algorithm with Centralized Multiple Crossovers Applied to Traveling Salesman Problems
  • エリートカイ ノ シュウチュウテキ ナ コウサ メカニズム オ モツ ブンサン イデンテキ アルゴリズム ノ TSP ニ オケル カイタンサク セイノウ ノ ケントウ

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This paper proposes a new method of genetic algorithms (GAs) for discrete optimization problems. For continuous optimization problems, it has been reported that distributed genetic algorithms (DGAs) show the higher performance than conventional GAs. However, for discrete optimization problems, the performance of DGAs has not been clear so far. In this paper, we propose a new approach in DGAs to discrete optimization problems. The proposed method is based on the multiple crossovers applied to the population consists of offsprings from elite individuals in distributed subpopulations (Centralized Multiple Crossover : CMX). We examine the performence of a conventional GA, DGA and proposed method for a typical discrete optimization problem, the Traveling Salesman Problem (TSP). The experiments showed that the proposed method provides better performance than the conventional DGA.

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