| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系所 === 93 === Traditional genetic programming (GP) is designed to search the structural solution by utilizing crossover mechanisms of tree structure to get better structures. But the expression method of the genotype structure is complicated, and while calculating the fitness, the recombination process must take many steps to accomplish. Thus, a novel representation scheme called block type genetic programming (BGP) is proposed in this thesis. Since BGP has the advantages of simple representations and easy programming for operations, the steps of genotype operation can be largely reduced and the efficiency in the course of learning is greatly improved.
In this thesis, we will verify the performance of BGP by using three optimization problems. Those problems are pattern recognition, direct load control and system modeling. Moreover, we also use the genetic algorithm (GA) to solve the question of demand exchange. Based on the searching ability of GA, we can find the best unloaded strategy for power users and reduce the cost of the power company.
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