Reinforcement Learning for Online Control of Evolutionary Algorithms

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

Research output: Chapter in Book / Report / Conference proceedingChapterAcademic

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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.
Original languageEnglish
Title of host publicationEngineering Self-Organising Systems
Pages151-160
Publication statusPublished - 2007

Bibliographical note

Engineering Self-Organising Systems, 151-160

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