This work mainly investigates the genetic algorithm (GA) associated with the notation of fuzzy theory and applications. The proposed crossover model for GA algorithm is guided via the grade of fuzzy membership functions, and the applicability is demonstrated by solving the travel salesman problem (TSP). The crossover model for the traditional GA use the probability rule to produce the next generation, where it always cause the time consumption for the useless evaluation. Thus, we study a distinct crossover model for GA algorithm associate with the fuzzy grade notion; this dynamic guide mode for GA algorithm can speed up the convergent process and improve the efficiency and the error rate from the simulating results. The experiments are performed and verified by the international benchmarks with TSPLIB and compared with some other ones.
關聯:
Business and Information 2013，Bali, Indonesia，2013/07/07~09
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