Implementation of a probabilistic model-building co-evolutionary algorithm
Implementation of a probabilistic model-building co-evolutionary algorithm
复制标题
概率模型构建协同进化算法的实现
DOI：
10.1007/s10015-011-0954-4
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发表时间：
2011
影响因子：
0.9
通讯作者：
Takaya Arita
中科院分区：
文献类型：
--
作者：
Takahiro Otani;Takaya Arita
我们提出了一种带有概率模型构建的扩展协同进化算法（CA）（CA-PMB），以提高 CA 的搜索性能。本文具体描述了一种名为基于群体增量学习的协同进化算法（CA-PBIL）的 CA-PMB 实现，并通过使用不及物数字游戏作为基准问题的计算实验来分析该算法的行为。实验结果表明，过度专业化效应可能会抑制理想的共同进化，并且该算法表现出由博弈的不及物性引起的复杂动态。然而，进一步的实验表明，当为每个群体设置不同的学习率时，不及物性会鼓励理想的共同进化。
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 co-evolution when a different learning rate is set for each population.