Co-Evolutionary Algorithms Based on Mixed Strategy

Authors:
Wei Hou;HongBin Dong;GuiSheng Yin
Affiliations:
Harbin Engineering University and Northeast Agricultural University, China;Harbin Engineering University, China;Harbin Engineering University, China
Venue:
Journal of Information Technology Research
Year:
2011

Citing 11
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
A single-point mutation evolutionary programming

Information Processing Letters
Large scale evolutionary optimization using cooperative coevolution

Information Sciences: an International Journal
Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery

Pattern Recognition
An analysis on crossovers for real number chromosomes in an infinite population size

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Ensemble strategies with adaptive evolutionary programming

Information Sciences: an International Journal
A game-theoretic approach for designing mixed mutation strategies

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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
Automatic Clustering Using an Improved Differential Evolution Algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strategy set consists of cuboid, two-points and heuristic crossover operators. The novel algorithms automatically select crossover and/or mutation operators from a given mixed strategy set, and improve the evolutionary performance by dynamically utilizing the most effective operator at different stages of evolution. The proposed algorithms are tested on a set of 21 benchmark problems. The results show that the new mixed strategies perform equally well or better than the best of the previous evolutionary methods for all of the benchmark problems. The proposed MMCGA has shown significant superiority over others.