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

Arina Buzdalova; Maxim Buzdalov

doi:10.1109/ICMLA.2012.32

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

Arina Buzdalova, Maxim Buzdalov

ITMO University

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

29 Citations (Scopus)

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 language	English
Title of host publication	ICMLA '12
Subtitle of host publication	Proceedings of the 2012 11th International Conference on Machine Learning and Applications
Publisher	IEEE Press
Pages	150-155
Number of pages	6
Volume	1
ISBN (Print)	978-1-4673-4651-1
DOIs	https://doi.org/10.1109/ICMLA.2012.32
Publication status	Published - 12 Dec 2012
Externally published	Yes

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title = "Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning",

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Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. / Buzdalova, Arina ; Buzdalov, Maxim.
ICMLA '12: Proceedings of the 2012 11th International Conference on Machine Learning and Applications. Vol. 1 IEEE Press, 2012. p. 150-155.

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

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AB - 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.

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

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