Reinforcement learning for online control of evolutionary algorithms

A. E. Eiben; Mark Horvath; Wojtek Kowalczyk; Martijn C. Schut

doi:https://doi.org/10.1007/978-3-540-69868-5_10

Reinforcement learning for online control of evolutionary algorithms

A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Martijn C. Schut

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

52 Citations (Scopus)

Abstract

The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers
Publisher	Springer Verlag
Pages	151-160
Volume	4335 LNAI
ISBN (Print)	3540698671
DOIs	https://doi.org/10.1007/978-3-540-69868-5_10
Publication status	Published - 2007
Externally published	Yes
Event	4th International Workshop on Engineering Self-Organising Applications, ESOA 2006 - , Japan Duration: 9 May 2006 → 9 May 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Conference

Conference	4th International Workshop on Engineering Self-Organising Applications, ESOA 2006
Country/Territory	Japan
Period	9/05/2006 → 9/05/2006

Access to Document

https://doi.org/10.1007/978-3-540-69868-5_10

Cite this

Eiben, A. E., Horvath, M., Kowalczyk, W., & Schut, M. C. (2007). Reinforcement learning for online control of evolutionary algorithms. In Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers (Vol. 4335 LNAI, pp. 151-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Verlag. https://doi.org/10.1007/978-3-540-69868-5_10

Eiben, A. E. ; Horvath, Mark ; Kowalczyk, Wojtek et al. / Reinforcement learning for online control of evolutionary algorithms. Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers. Vol. 4335 LNAI Springer Verlag, 2007. pp. 151-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Reinforcement learning for online control of evolutionary algorithms",

abstract = "The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

author = "Eiben, {A. E.} and Mark Horvath and Wojtek Kowalczyk and Schut, {Martijn C.}",

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Eiben, AE, Horvath, M, Kowalczyk, W & Schut, MC 2007, Reinforcement learning for online control of evolutionary algorithms. in Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers. vol. 4335 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, pp. 151-160, 4th International Workshop on Engineering Self-Organising Applications, ESOA 2006, Japan, 9/05/2006. https://doi.org/10.1007/978-3-540-69868-5_10

Reinforcement learning for online control of evolutionary algorithms. / Eiben, A. E.; Horvath, Mark; Kowalczyk, Wojtek et al.
Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers. Vol. 4335 LNAI Springer Verlag, 2007. p. 151-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Reinforcement learning for online control of evolutionary algorithms

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AU - Kowalczyk, Wojtek

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N2 - The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA. © Springer-Verlag Berlin Heidelberg 2007.

AB - The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA. © Springer-Verlag Berlin Heidelberg 2007.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/37425493

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DO - https://doi.org/10.1007/978-3-540-69868-5_10

M3 - Conference contribution

SN - 3540698671

VL - 4335 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 151

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BT - Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers

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Eiben AE, Horvath M, Kowalczyk W, Schut MC. Reinforcement learning for online control of evolutionary algorithms. In Engineering Self-Organising Systems - 4th International Workshop ESOA 2006, Revised and Invited Papers. Vol. 4335 LNAI. Springer Verlag. 2007. p. 151-160. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-540-69868-5_10

Reinforcement learning for online control of evolutionary algorithms

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