An Improved Genetic Algorithm
نویسندگان
چکیده
In this paper, an improved genetic algorithm for multi-object optimization is proposed. Simulated annealing is used to local search in genetic algorithms. Furthermore, fuzzy reasoning is adopted to modify crossover probability and mutation probability according to characteristics of population in genetic algorithms instead of fixed parameters. And so, it can be convergence to global optimum quickly. This indicates that using this algorithm for multi-objective optimization problems has very strong utilitarian value. Keywords—genetic algorithm; simulated annealing; multiobject optimization.
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