Adaptive Selection of Helper-Objectives with Reinforcement Learning

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

7 Citations (SciVal)

Abstract

In this paper a previously proposed method of choosing auxiliary fitness functions is applied to adaptive selection of helper-objectives. Helper-objectives are used in evolutionary computation to enhance the optimization of the primary objective. The method based on choosing between objectives of a single-objective evolutionary algorithm with reinforcement learning is briefly described. It is tested on a model problem. From the results of the experiment, it can be concluded that the method allows to automatically select the most effective helper-objectives and ignore the ineffective ones. It is also shown that the proposed method outperforms multi-objective evolutionary algorithms, that were used with helper-objectives originally.
Original languageEnglish
Title of host publicationICMLA '12
Subtitle of host publicationProceedings of the 2012 11th International Conference on Machine Learning and Applications
PublisherIEEE Press
Pages66-67
Number of pages2
Volume2
ISBN (Print)978-1-4673-4651-1
DOIs
Publication statusPublished - 12 Dec 2012
Externally publishedYes
Event11th IEEE International Conference on Machine Learning and Applications - Cancan, Mexico
Duration: 12 Dec 201215 Dec 2012

Conference

Conference11th IEEE International Conference on Machine Learning and Applications
Abbreviated titleICMLA 2012
Country/TerritoryMexico
CityCancan
Period12 Dec 201215 Dec 2012

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