Implementation of a probabilistic model- building co-evolutionary algorithm

作     者：Otani, Takahiro Arita, Takaya 

作者机构：Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan 

出 版 物：《ARTIFICIAL LIFE AND ROBOTICS》 (人工生命与机器人)

年 卷 期：2011年第16卷第3期

页      面：373-377页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：Grants-in-Aid for Scientific Research [11J06532] Funding Source: KAKEN 

主　　题：Co-evolutionary algorithm Probabilistic model-building genetic algorithm Intransitive numbers game 

摘      要：We propose an extended co-evolutionary algorithm (CA) with probabilistic model building (CA-PMB) in order to improve the search performance of the CA. This article specifically describes an implementation of CA-PMB called a co-evolutionary algorithm with population-based incremental learning (CA-PBIL), and analyzes the behavior of the algorithm through computational experiments using an intransitive numbers game as a benchmark problem. The experimental results show that desirable co-evolution may be inhibited by the over-specialization effect, and that the algorithm shows complex dynamics caused by the game s intransitivity. However, further experiments show that the intransitivity encourages desirable coevolution when a different learning rate is set for each population.