Self-Adaptive Fuzzy Genetic Algorithms and Their Applications

• Main Authors: Ming-Da Wu, 吳明達
• Other Authors: Chuen-Tsai Sun
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/22893920816254184728

碩士 === 國立交通大學 === 資訊科學學系 === 83 === It is known that genetic algorithms (GAs) are an effective search method which also have the advantages of robustness and efficiency. In this thesis, we introduce two new ideas to further improve GAs. The first direction is focused on solving it multi-stage problems, which have the property that different strategies should be employed in different stages. Since the boundaries between stages are rather fuzzy than crisp, fuzzy theories are suitable for describing these characteristics. We introduce two ways of incorporating fuzzy theory into GAs, i.e, fuzzily characterized features and fuzzy polyploidy. In the second approach, we add a self-adaptive function to traditional GAs. A dynamic fitnesst echniques was developed, which is helpful for continuous evolution and robust solution. We expect to improve not only the quality but also the efficiency of GA search by using these twomethods. Two experiments were presented in this thesis to verify the power of our new methods. First, we tested our idea in the domain of Othello game playing, which is an challenging game because of the drastic board changes that result from moves. Second, an even more difficult problem, Taiwan stock market investment analysis, was used to validate the effectiveness and robustness of our methods.