Feasibility structure modeling : an effective chaperone for constrained memetic algorithms

An important issue in designing memetic algorithms (MAs) is the choice of solutions in the population for local refinements, which becomes particularly crucial when solving computationally expensive problems. With single evaluation of the objective/constraint functions necessitating tremendous compu...

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Main Authors: Handoko, Stephanus Daniel, Kwoh, Chee Keong, Ong, Yew-Soon
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/147975
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1479752021-04-16T02:08:42Z Feasibility structure modeling : an effective chaperone for constrained memetic algorithms Handoko, Stephanus Daniel Kwoh, Chee Keong Ong, Yew-Soon School of Computer Science and Engineering Engineering::Computer science and engineering Convergence Analysis Evolutionary Algorithms An important issue in designing memetic algorithms (MAs) is the choice of solutions in the population for local refinements, which becomes particularly crucial when solving computationally expensive problems. With single evaluation of the objective/constraint functions necessitating tremendous computational power and time, it is highly desirable to be able to focus search efforts on the regions where the global optimum is potentially located so as not to waste too many function evaluations. For constrained optimization, the global optimum must either be located at the trough of some feasible basin or some particular point along the feasibility boundary. Presented in this paper is an instance of optinformatics where a new concept of modeling the feasibility structure of inequality-constrained optimization problemsdubbed the feasibility structure modelingis proposed to perform geometrical predictions of the locations of candidate solutions in the solution space: deep inside any infeasible region, nearby any feasibility boundary, or deep inside any feasible region. This knowledge may be unknown prior to executing an MA but it can be mined as the search for the global optimum progresses. As more solutions are generated and subsequently stored in the database, the feasibility structure can thus be approximated more accurately. As an integral part, a new paradigm of incorporating the classificationrather than the regressioninto the framework of MAs is introduced, allowing the MAs to estimate the feasibility boundary such that effective assessments of whether or not the candidate solutions should experience local refinements can be made. This eventually helps preventing the unnecessary refinements and consequently reducing the number of function evaluations required to reach the global optimum. Accepted version 2021-04-16T02:08:42Z 2021-04-16T02:08:42Z 2010 Journal Article Handoko, S. D., Kwoh, C. K. & Ong, Y. (2010). Feasibility structure modeling : an effective chaperone for constrained memetic algorithms. IEEE Transactions On Evolutionary Computation, 14(5), 740-758. https://dx.doi.org/10.1109/TEVC.2009.2039141 1089-778X https://hdl.handle.net/10356/147975 10.1109/TEVC.2009.2039141 2-s2.0-77957572225 5 14 740 758 en IEEE Transactions on Evolutionary Computation © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TEVC.2009.2039141. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Convergence Analysis
Evolutionary Algorithms
spellingShingle Engineering::Computer science and engineering
Convergence Analysis
Evolutionary Algorithms
Handoko, Stephanus Daniel
Kwoh, Chee Keong
Ong, Yew-Soon
Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
description An important issue in designing memetic algorithms (MAs) is the choice of solutions in the population for local refinements, which becomes particularly crucial when solving computationally expensive problems. With single evaluation of the objective/constraint functions necessitating tremendous computational power and time, it is highly desirable to be able to focus search efforts on the regions where the global optimum is potentially located so as not to waste too many function evaluations. For constrained optimization, the global optimum must either be located at the trough of some feasible basin or some particular point along the feasibility boundary. Presented in this paper is an instance of optinformatics where a new concept of modeling the feasibility structure of inequality-constrained optimization problemsdubbed the feasibility structure modelingis proposed to perform geometrical predictions of the locations of candidate solutions in the solution space: deep inside any infeasible region, nearby any feasibility boundary, or deep inside any feasible region. This knowledge may be unknown prior to executing an MA but it can be mined as the search for the global optimum progresses. As more solutions are generated and subsequently stored in the database, the feasibility structure can thus be approximated more accurately. As an integral part, a new paradigm of incorporating the classificationrather than the regressioninto the framework of MAs is introduced, allowing the MAs to estimate the feasibility boundary such that effective assessments of whether or not the candidate solutions should experience local refinements can be made. This eventually helps preventing the unnecessary refinements and consequently reducing the number of function evaluations required to reach the global optimum.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Handoko, Stephanus Daniel
Kwoh, Chee Keong
Ong, Yew-Soon
format Article
author Handoko, Stephanus Daniel
Kwoh, Chee Keong
Ong, Yew-Soon
author_sort Handoko, Stephanus Daniel
title Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
title_short Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
title_full Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
title_fullStr Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
title_full_unstemmed Feasibility structure modeling : an effective chaperone for constrained memetic algorithms
title_sort feasibility structure modeling : an effective chaperone for constrained memetic algorithms
publishDate 2021
url https://hdl.handle.net/10356/147975
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