research-article

Feedback control for multi-modal optimization using genetic algorithms

Authors:

Ole J. Mengshoel,

Dipan K. PalAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 839 - 846

https://doi.org/10.1145/2576768.2598231

Published: 12 July 2014 Publication History

Abstract

Many optimization problems are multi-modal. In certain cases, we are interested in finding multiple locally optimal solutions rather than just a single optimum as is computed by traditional genetic algorithms (GAs). Several niching techniques have been developed that seek to find multiple such local optima. These techniques, which include sharing and crowding, are clearly powerful and useful. But they do not explicitly let the user control the number of local optima being computed, which we believe to be an important capability. In this paper, we develop a method that provides, as an input parameter to niching, the desired number of local optima. Our method integrates techniques from feedback control, includes a sensor based on clustering, and utilizes a scaling parameter in Generalized Crowding to control the number of niches being explored. The resulting Feedback Control GA (FCGA) is tested in several experiments and found to perform well compared to previous approaches. Overall, the integration of feedback control and Generalized Crowding is shown to effectively guide the search for multiple local optima in a more controlled fashion. We believe this novel capability has the potential to impact future applications as well as other evolutionary algorithms.

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Cited By

Sánchez-Díaz XMasson CMengshoel O(2024)Regularized Feature Selection Landscapes: An Empirical Study of MultimodalityParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_25(409-426)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_25
Gao WLi GZhang QLuo YWang Z(2021)Solving Nonlinear Equation Systems by a Two-Phase Evolutionary AlgorithmIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.295732451:9(5652-5663)Online publication date: Sep-2021
https://doi.org/10.1109/TSMC.2019.2957324
Maggio MAbdelzaher TEsterle LGiese HKephart JMengshoel OPapadopoulos ARobertsson AWolter K(2017)Self-adaptation for Individual Self-aware Computing SystemsSelf-Aware Computing Systems10.1007/978-3-319-47474-8_12(375-399)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_12

Index Terms

Feedback control for multi-modal optimization using genetic algorithms

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cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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Published: 12 July 2014

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GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Sánchez-Díaz XMasson CMengshoel O(2024)Regularized Feature Selection Landscapes: An Empirical Study of MultimodalityParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_25(409-426)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_25
Gao WLi GZhang QLuo YWang Z(2021)Solving Nonlinear Equation Systems by a Two-Phase Evolutionary AlgorithmIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.295732451:9(5652-5663)Online publication date: Sep-2021
https://doi.org/10.1109/TSMC.2019.2957324
Maggio MAbdelzaher TEsterle LGiese HKephart JMengshoel OPapadopoulos ARobertsson AWolter K(2017)Self-adaptation for Individual Self-aware Computing SystemsSelf-Aware Computing Systems10.1007/978-3-319-47474-8_12(375-399)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_12

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