Peña J M, Lozano J A, Larrañaga P
Computational Biology, Dept. of Physics and Measurement Technology, Linköping University, Sweden.
Evol Comput. 2005 Spring;13(1):43-66. doi: 10.1162/1063656053583432.
Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.
许多优化问题属于所谓的全局多模态问题，即它们存在多个全局最优解。不幸的是，对于大多数分布估计算法而言，这是困难的主要来源，由于遗传漂移，会导致其有效性和效率下降。为了克服离散全局多模态问题优化中的这些缺点，本文引入并评估了一种基于贝叶斯网络无监督学习的新的分布估计算法。我们报告了针对对称二元优化问题的实验所取得的令人满意的结果。
