Cooperative Learning Based Hybrid Evolutionary Algorithms for Neural Fuzzy System Design and Optimization of Multi-funnel Functions

• Main Authors: Cheng, Yi-Chang, 鄭逸章
• Other Authors: Lin, Sheng-Fuu
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/42477607448787537225

博士 === 國立交通大學 === 電控工程研究所 === 100 === In this dissertation, we mainly focus on researching the cooperative behavior of evolutionary algorithms. Algorithms discussed in this dissertation include genetic algorithm (GA), particle swarm optimization (PSO) and evolution strategy with covariance matrix adaptation (CMA-ES). The modification of genetic algorithm (GA) is done by introducing the group-based symbiotic evolution (GSE) technique which enables genetic algorithm (GA) to partition the search space into smaller subspaces and explore each smaller subspace by a separate agent to alleviate the curse of dimensionality. We also propose a separability detection method based on covariance matrix adaption mechanism into the cooperative particle swarm optimization (CPSO) to locate non-separable variables into the same swarm. As to the research of evolution strategy with covariance matrix adaptation (CMA-ES), we introduce the mean shift procedure which allows us to apply multiple CMA-ES instances to explore different parts of the search space in parallel. The scope of this dissertation includes how to implement evolutionary algorithms on neural-fuzzy systems, the improvement of algorithms, parallel computing and the emergence of two algorithms