An Empirical Performance Comparison of Niching Methods for Genetic Algorithms(Regular Section)

スポンサーリンク

概要

• 論文の詳細を見る

• Various niching methods have been developed to maintain the population diversity. The feature of these methods is to prevent the proliferation of similar individuals in the niche (subpopulation) based on the similarity measure. This paper demonstrates that they are effective to avoid premature convergence in a case where only one global optimum in multimodal functions is searched. The performance of major niching methods in such a case is investigated and compared by experiments using seven benchmark functions. The niching methods tested in this paper are deterministic crowding, probabilistic crowding, restricted tournament selection, clearing procedure and diversity-control-oriented genetic algorithm (DCGA). According to the experiment, each method shows a fairly good global-optimum-searching capability. However, no method can completely avoid premature convergence in all functions. In addition, no method shows a better searching capability than the other methods in all functions.

• 一般社団法人電子情報通信学会の論文
• 2002-11-01

著者

• Shimodaira Hisashi

  The Faculty Of Information And Communications Bunkyo University

• SHIMODAIRA Hisashi

  the Faculty of Information and Communications, Bunkyo University

関連論文

• Methods for Reinitializing the Population to Improve the Performance of a Diversity-Control-Oriented Genetic Algorithm
• An Empirical Performance Comparison of Niching Methods for Genetic Algorithms(Regular Section)

スポンサーリンク