Extension of the Estimation of Distribution Algorithms for Applicatin Areas and Individual Rrepresentation

本课题提出了两种新的分布估计算法（EDAs）。第一个EDA是用于求解强化学习算法的分布估计算法，称为EDA- rl。针对该算法，我们研究了EDA-RL中滑移状态的检测和概率模型结构的估计。另一个被提议的EDA是图形相关问题的EDA。该算法可以处理用图表示的个体。通过使用图核，我们不必考虑由单个表示引起的粗糙的适应度景观。在项目期间，我们发表了期刊文章和国际会议论文。此外，我们有两次邀请会谈的机会，包括在台湾的一次会谈。

This research project has proposed two sorts of novel Estimation of Distribution Algorithms (EDAs). The first EDA is the Estimation of Distribution ALgorithms for solving Reinforcement Learning Algorithms, called EDA-RL. For this algorithm, we have investigated the detection of slip states, and the estimation of the structure of the probabilistic model in the EDA-RL. Another proposed EDA is the EDA for graph-related problems. This algorithm can cope with individuals represented by graphs. By using graph kernels, we do not have to take care of ruggede fitness landscpaes caused by individual representations. During the project peirod, we have published journal articles and international coneference papers. Moreover, we have two oppotunity of the invited talks including a talk in Taiwan.