Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of...

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Bibliographic Details
Published in2012 Eleventh International Conference on Machine Learning and Applications Vol. 1; pp. 150 - 155
Main Authors Buzdalova, A., Buzdalov, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Algorithm design and analysis
Educational institutions
Evolutionary computation
Genetic algorithms
Learning
Optimization
Roads
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ISBN1467346519
9781467346511
DOI10.1109/ICMLA.2012.32

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Summary:In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.
ISBN:1467346519
9781467346511
DOI:10.1109/ICMLA.2012.32