遺伝的アルゴリズムの改良とその応用
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概要
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The Genetic Algorithm (GA) is known as a method to find near optimum solutions of optimization problems in short time. In this paper， we describe how to apply the GA to some problems and how to modify the GA to be conformable to the problems. Firstly， we introduce the GA, and compare the time of computation and the solution for traveling salesman problem (TSP) in the uses of one by one method with the GA. Secondly on the knapsack problem， we discuss the ways of generating new genes by cross-over and mutation. Thirdly， we apply the GA to find the minimum value of continuous functions， and propose a method of narrowing the range of new genes for increasing the precision of the solutions. Lastly、weimplement the GA on a parallel computer system and evaluate the proccssing time in some parallel processing modes for the knapsack problem. As a result of this research, we could conclude that the GA is a basic universal algorithm applicable to many types of problems， and that it is valuable to cxtend the application of the GA.
- 福井大学工学部の論文
福井大学工学部 | 論文
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