A learning genetic algorithm is proposed to solve the experimental parameters optimization problem. This method can not only enhance the efficiency of genetic algorithm through the pre-given user experience, but also improve the efficiency of genetic algorithm via learning the knowledge obtained from the optimization process. Experimental results suggest that the learning genetic algorithm can effectively optimize the experimental parameters.
Key words: Genetic algorithms, experimental parameters optimization, combinatorial optimization.
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