Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolving artificial intelligence
Evolving artificial intelligence
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.)
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Improving crossover operator for real-coded genetic algorithms using virtual parents
Journal of Heuristics
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
A New Self-adaptative Crossover Operator for Real-Coded Evolutionary Algorithms
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A new evolutionary reinforcement scheme for stochastic learning automata
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Automatic control based on wasp behavioral model and stochastic learning automata
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
Investigation of self-organizing map for genetic algorithm
Advances in Engineering Software
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
Evolvability and speed of evolutionary algorithms in light of recent developments in biology
Journal of Artificial Evolution and Applications
Information Sciences: an International Journal
Rotationally invariant crossover operators in evolutionary multi-objective optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
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This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.