Estimation of distribution algorithm based on probabilistic grammar with latent annotations
Estimation of distribution algorithm based on probabilistic grammar with latent annotations
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DOI：
10.1109/cec.2007.4424585
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Yoshihiko Hasegawa;H. Iba
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文献类型：
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作者：
Yoshihiko Hasegawa;H. Iba
遗传程序设计（GeneticProgramming，GP）模仿自然界的进化过程来优化函数和程序，已被应用于许多问题。近年来，人们从分布估计的角度来看待进化算法。许多基于概率技术的分布估计算法已经被提出。虽然概率上下文无关文法（PCFG）通常用于函数和程序演化，但它假设产生式规则之间的独立性。有了这个简单的PCFG，它不能从有前途的解决方案中诱导出构建块。我们提出了一种新的功能进化算法的基础上PCFG使用潜在的注释，削弱了独立性假设。两个主题（皇家树问题和DMAX问题）的计算实验表明，我们的新方法是非常有效的，与以前的方法相比。
Genetic Programming (GP) which mimics the natural evolution to optimize functions and programs, has been applied to many problems. In recent years, evolutionary algorithms are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithms) based on probabilistic techniques have been proposed. Although probabilistic context free grammar (PCFG) is often used for the function and program evolution, it assumes the independence among the production rules. With this simple PCFG, it is not able to induce the building-blocks from promising solutions. We have proposed a new function evolution algorithm based on PCFG using latent annotations which weaken the independence assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches.