Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the third international conference on Genetic algorithms
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Genetic Algorithm with Disruptive Selection
Proceedings of the 5th International Conference on Genetic Algorithms
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
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The selection operator is one of the main operators in evolutionary algorithms. It interacts with other operators (e.g., crossover, mutation) in a complex way. Consequently, the effects of selection should be considered also together with these operators. This paper provides an overview of several presently used selection operators and discusses a new selection operator specifically devised to assist MCPC, an exploitation-oriented multiple crossover per couple approach, to overcome premature convergence. Some experimental results are also provided.