Advanced Genetic Algorithm Using PSO and Euclidean Data Distance

다국어 초록 (Multilingual Abstract)

When we obtain an optimal solution using GA (Genetic Algorithm), operation such as crossover, reproduction, and mutation procedures is using to generate for the next generations. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close affinity can convergent To improve an optimal learning solution of GA, this paper deal with applying PSO (Particle Swarm Optimization) and Euclidian data distance to mutation procedure on GA's differentiation. Through this approaches, we can have global and local optimal solution together, and the faster and the exact optimal solution without any local solution. Four test functions are used for proof of this suggested algorithm.