Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
A single-point mutation evolutionary programming
Information Processing Letters
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
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Mutation operators play an important role in evolutionary programming. Several different mutation operators have been developed in the past decades. However, each mutation operator is only efficient in some type of problems, but fails in another one. In order to overcome the disadvantage, a possible solution is to use a mixed mutation strategy, which mixes various mutation operators. In this paper, an example of such strategies is introduced which employs five different mutation strategies: Gaussian, Cauchy, Levy, single-point and chaos mutations. It also combines with the technique of species conservation to prevent the evolutionary programming from being trapped in local optima. This mixed strategy has been tested on 21 benchmark functions. The simulation results show that the mixed mutation strategy is superior to any pure mutation strategy.