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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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 |
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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 |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Handoko, Stephanus Daniel Kwoh, Chee Keong Ong, Yew-Soon |
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Article |
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Handoko, Stephanus Daniel Kwoh, Chee Keong Ong, Yew-Soon |
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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 |
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Feasibility structure modeling : an effective chaperone for constrained memetic algorithms |
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Feasibility structure modeling : an effective chaperone for constrained memetic algorithms |
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feasibility structure modeling : an effective chaperone for constrained memetic algorithms |
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2021 |
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https://hdl.handle.net/10356/147975 |
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