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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. |
