Wan, Hua creator Yulu, Qi Chairperson Murai, Shunji Examination committee Huynh, Ngoc Phien Examination Committee Canadian International Development Agency (CIDA) Scholarship donor text th Bangkok Asian Institute of Technology 1993 monographic eng

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54 leaves + 1 online resource To build up a knowledge-based system in Artificial Intelligence (Al), selecting an appropriate set of rules is one of the key problems. In this thesis, a Genetic Classifier System approach is employed to optimize the rules for knowledge-based system. The performance of a classifier system which belongs to the genetics-based machine learning architecture is tested using the various Genetic Algorithm operators, namely: reproduction, crossover, and mutation. Together with these GA operators, rule/message and apportionment of credit determine the fittest set of string rules, expressed as classifiers which serve as solution set, in two cases, namely: the eleven-multiplexer task and the Expert System of stock-cutting problem. Based on the experimental results, it is suggested that Genetic Classifier System is a feasible approach to optimize the rules for improving the performance of knowledge-based system. A thesis submitted in partial fulfillment of the requirement for the degree of Master of Science Thesis (M.Sc.) - Asian Institute of Technology, 1993 Genetic algorithms Asian Institute of Technology. http://203.159.5.9/ait-thesis/detail.php?q=B16140 http://203.159.5.9/ait-thesis/detail.php?q=B16140 080998 20260818115031.0