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

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

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.
Original languageEnglish
Title of host publicationICMLA '12
Subtitle of host publicationProceedings of the 2012 11th International Conference on Machine Learning and Applications
PublisherIEEE Press
Pages150-155
Number of pages6
Volume1
ISBN (Print)978-1-4673-4651-1
DOIs
Publication statusPublished - 12 Dec 2012
Externally publishedYes

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