A Further Improvement on a Genetic Algorithm
Las Vegas, NV（2009）
摘要
In this paper, a new genetic algorithm is developed based on a pre-existing implementation. The new algorithm requires less human interaction through the use of dynamically selected weight and acceptance probability parameters. The algorithm is implemented and tested using six benchmark functions. Results show that the new algorithm significantly outperforms other genetic algorithms in less time and with less human interaction.
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关键词
pre-existing implementation,genetic algorithm,benchmark function,new genetic algorithm,acceptance probability parameter,new algorithm,dynamically selected weight,human interaction,data mining,probability density function,mutation,probability,benchmark testing,genetic algorithms,gallium,crossover,fitness function,reactive power
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