分布估计算法中的子结构小生境
Sub-structural Niching in Estimation of Distribution Algorithms
摘要：
我们提出了一个子结构小生境方法，充分利用了问题的分解能力的联系学习方法，如估计的分布算法，并集中在保持多样性的子结构层次。该方法包括三个关键部分：（1）问题分解和子结构识别，（2）子结构适应度估计，（3）子结构生态位保持。子结构的小生境方法相比，限制比赛选择（RTS）-小生境方法用于分层贝叶斯优化算法-特别强调持续保存的多个全局解的一类有界困难，可加性分离的多峰问题。结果表明，子结构小生境成功地保持了多个全局最优在大量的代，这样做的人口显着少于RTS。此外，与RTS相比，每个利基的市场份额更接近子结构利基的预期水平。
英文摘要：
We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS.
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