EFFICIENCY INCREASING METHOD OF THE EVOLUTIONARY ALGORITHMS BY REINFORCEMENT LEARNING

A scalar optimization method based on evolutionary algorithms controlling by reinforcement learning is proposed. Reinforcement learning is used to choose the most effective fitness function at each generation of the evolutionary algorithm. Experimental results for a model problem H-IFF are given. Co...

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Bibliographic Details
Published in:Nauchno-tekhnicheskiĭ vestnik informat͡s︡ionnykh tekhnologiĭ, mekhaniki i optiki mekhaniki i optiki, 2012-09, Vol.12 (5)
Main Authors: Buzdalova, A S, Buzdalov, M
Format: Article
Language:Russian
Subjects:
Evolutionary algorithms
Genetic algorithms
Machine learning
Optimization
Online Access:Get full text
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Summary:A scalar optimization method based on evolutionary algorithms controlling by reinforcement learning is proposed. Reinforcement learning is used to choose the most effective fitness function at each generation of the evolutionary algorithm. Experimental results for a model problem H-IFF are given. Comparison of the developed method with other evolutionary optimization methods is performed. According to experimental results, the proposed method increases the effectiveness of evolutionary algorithms.
ISSN:2226-1494
2500-0373