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Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process

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Abstract

Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.

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Authors and Affiliations

  1. Department of Information Management, I-Shou University, Kaohsiung, 84008, Taiwan, Republic of China

    Tzung-Pei Hong & Wen-Yang Lin

  2. Institute of Electrical Engineering, Chung-Hua University, Hsin-Chu, 30067, Taiwan, Republic of China

    Hong-Shung Wang

  3. Institute of Information Engineering, I-Shou University, Kaohsiung, 84008, Taiwan, Republic of China

    Wen-Yuan Lee

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  1. Tzung-Pei Hong
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  2. Hong-Shung Wang
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  3. Wen-Yang Lin
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Hong, TP., Wang, HS., Lin, WY. et al. Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process. Applied Intelligence 16, 7–17 (2002). https://doi.org/10.1023/A:1012815625611

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