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Model-based evolutionary algorithms: GECCO 2020 tutorial
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Use of the q-Gaussian mutation in evolutionary algorithms
Special issue on advances in computational intelligence and bioinformaticsThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed ...
Hybrid biogeography-based evolutionary algorithms
Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the ...
Co-Evolutionary Algorithms Based on Mixed Strategy
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. ...
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