Improvement of Genetic Algorithm Using PSO and Euclidean Data Distance

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 to obtain gobal and local optimal solution together.

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