Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar
Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar
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DOI：
10.1109/tevc.2009.2015574
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发表时间：
2009-08
影响因子：
14.3
通讯作者：
Yoshihiko Hasegawa;H. Iba
中科院分区：
文献类型：
--
作者：
Yoshihiko Hasegawa;H. Iba
分布估计算法是利用概率技术代替传统遗传算子的进化算法。近年来，概率技术在程序和函数进化中的应用受到越来越多的关注，这种方法有望为传统的遗传编程技术提供一种强有力的替代方案。虽然概率上下文无关文法（PCFG）是一种广泛使用的概率程序演化模型，传统的PCFG是不适合估计节点之间的相互作用，因为上下文自由的假设。在本文中，我们提出了一个新的进化算法命名为编程与注释语法估计的基础上PCFG与潜在的注释，这使得上下文自由的假设被削弱。通过将该算法应用于几个计算问题，它表明，我们的方法是明显更有效地估计积木比以前的方法。
Estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and this approach promises to provide a strong alternative to the traditional genetic programming techniques. Although a probabilistic context-free grammar (PCFG) is a widely used model for probabilistic program evolution, a conventional PCFG is not suitable for estimating interactions among nodes because of the context freedom assumption. In this paper, we have proposed a new evolutionary algorithm named programming with annotated grammar estimation based on a PCFG with latent annotations, which allows this context freedom assumption to be weakened. By applying the proposed algorithm to several computational problems, it is demonstrated that our approach is markedly more effective at estimating building blocks than prior approaches.