Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Adapting Self-Adaptive Parameters in Evolutionary Algorithms
Applied Intelligence
Analyzing cooperative coevolution with evolutionary game theory
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A game-theoretic memory mechanism for coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Combining mutation operators in evolutionary programming
IEEE Transactions on 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
Mixed mutation strategy embedded differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Pure strategy or mixed strategy?
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Co-Evolutionary Algorithms Based on Mixed Strategy
Journal of Information Technology Research
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Different mutation operators have been proposed in evolutionary programming. However, each operator may be efficient in solving a subset of problems, but will fail in another one. Through a mixture of various mutation operators, it is possible to integrate their advantages together. This paper presents a game-theoretic approach for designing evolutionary programming with a mixed mutation strategy. The approach is applied to design a mixed strategy using Gaussian and Cauchy mutations. The experimental results show the mixed strategy can obtain the same performance as, or even better than the best of pure strategies.