Search space modulation in genetic algorithms: evolving the search space by sinusoidal transformations

Authors:
Jose Antonio Martin H.
Affiliations:
Instituto de Automática Industrial, -C.S.I.C.-
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 4
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A computationally efficient evolutionary algorithm for real-parameter optimization

Evolutionary Computation

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Abstract

An experimental form of Modulation (Reinterpretation) of the Search Space is presented. This modulation is developed as a mathematical method that can be implemented directly into existing evolutionary algorithms without writing special operators, changing the program loop etc. The main mathematical principle behind this method is the dynamic sinusoidal envelope of the search space. This method is presented in order to solve some theoretical and practical issues in evolutionary algorithms like numerical bounded variables, dynamic focalized search, dynamic control of diversity, feasible region analysis etc.