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Development of Evolutionary Algorithms based on a Picture of Evolution of Probability Distribution

基于概率分布演化图的演化算法的发展

基本信息

批准号：
14084211
负责人：
KITA Hajime
金额：
$ 7.74万
依托单位：
Kyoto University (2003-2005)National Institution for Academic Degrees and University Evaluation (2002)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research on Priority Areas
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2005
项目状态：
已结题

项目摘要

In this study, we aimed at Genetic Algorithms (GA) as optimization methods utilizing only function values to be optimized. We have examined the GA that uses population of search points with a picture of evolution of probability distribution, carried out comparison study of similar method called Estimation of Distribution Algorithms (EDA), and improved GA considering their applications to practical engineering problems.First, concerning comparative study between GA and EDA, we have proposed Pseudo-mutation and Pseudo-crossover as evaluation criteria for population-based probabilistic search algorithms. Then, using these criteria, we have evaluated GAs such as Simple GA, Spin Glass GA and Thermo-Dynamical GA and Bayesian Optimization Algorithm (BOA), a representative implementation of EDA.Further, from the viewpoint of evolution of distribution, we devised extension of real-coded GA for optimization of periodic function which is often appears in applications in engineering. It is based on the idea of embedding hyper sphere in the Euclidian space and applying the crossover in real-coded GA. Numerical experiments shows effectiveness of the proposed method.Since GA is applicable to optimization problems that involve noise, we have also applied GA to simulation-based optimization using random numbers. As a practical application, it has been applied optimization of group controller of elevator systems successfully. It can be also effective optimization tool for experiment-based optimization. In application of GA to elevator controller, implementation of controller requires decision making mechanisms and we have developed an exampler-based policy representation whose parameters are searched by GA. As well as simpler benchmarking problems, the exampler-based approach combined with GA works well in elevator control problem.

在本研究中，我们的目标是遗传算法（GA）作为优化方法，只利用函数值进行优化。本文研究了基于概率分布演化图的搜索点总体遗传算法，对类似的分布估计算法（EDA）进行了比较研究，并结合其在实际工程问题中的应用对遗传算法进行了改进。首先，在遗传算法和EDA算法的比较研究中，我们提出了伪变异和伪交叉作为基于群体的概率搜索算法的评价标准。然后，利用这些标准，我们评估了简单遗传算法、自旋玻璃遗传算法和热力学遗传算法以及贝叶斯优化算法（BOA），这是EDA的代表实现。进一步，从分布演化的角度出发，对工程中常见的周期函数优化问题进行了实编码遗传算法的扩展。它基于在欧几里德空间中嵌入超球的思想，并将交叉应用于实数编码遗传算法中。数值实验证明了该方法的有效性。由于遗传算法适用于涉及噪声的优化问题，我们也将遗传算法应用于使用随机数的基于仿真的优化。作为实际应用，该方法已成功地应用于电梯系统群控制器的优化。它也可以作为基于实验优化的有效优化工具。在将遗传算法应用于电梯控制器中，由于控制器的实现需要决策机制，我们开发了一种基于实例的策略表示，该策略表示的参数由遗传算法搜索。除了简单的基准问题外，基于实例的方法结合遗传算法在电梯控制问题中也有很好的效果。