论文信息 - An Adaptive Evolutionary Algorithm for Numerical Optimization

An Adaptive Evolutionary Algorithm for Numerical Optimization

In this paper, a normalized floating point representation has been used for making it be possible to design biotechnical genetic operators as well as to apply some genetic operators like inversion. To improve the adaptation of evolutionary algorithms and avoid the biases which may exist in some genetic operators, we have designed and applied several kinds of genetic operators with some probability. The experimental results show that our adaptive evolutionary algorithm has a better performance than the BGA (Breeder genetic Algorithm) and GAFOC (Genetic Algorithm For Optimal Control problems) for the test problems.

Lishan Kang | Zhengjun Pan | Lishan Kang | Zhengjun Pan

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