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Article

Improving global numerical optimization using a search-space reduction algorithm

Authors:

Vinicius Veloso de Melo,

Alexandre Claudio Botazzo Delbem,

Dorival Leao Pinto, Junior,

Fernando Marques FedersonAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 1195 - 1202

https://doi.org/10.1145/1276958.1277191

Published: 07 July 2007 Publication History

Abstract

We have developed an algorithm for reduction of search-space, called Domain Optimization Algorithm (DOA), applied to global optimization. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. DOA basically worksusing simple models for search-space regions to identify and eliminate non-promising regions. The proposed approach has shown relevant results for tests using hard benchmark functions.

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Cited By

Iacca Gde Melo V(2021)Cluster-centroid-based mutation strategies for Differential EvolutionSoft Computing10.1007/s00500-021-06448-zOnline publication date: 6-Nov-2021
https://doi.org/10.1007/s00500-021-06448-z
Akbari HKazerooni A(2020)KASRA: A Kriging-based adaptive space reduction algorithm for global optimization of computationally expensive black-box constrained problemsApplied Soft Computing10.1016/j.asoc.2020.106154(106154)Online publication date: Feb-2020
https://doi.org/10.1016/j.asoc.2020.106154
Ye PPan GDong Z(2018)Ensemble of surrogate based global optimization methods using hierarchical design space reductionStructural and Multidisciplinary Optimization10.1007/s00158-018-1906-658:2(537-554)Online publication date: 6-Feb-2018
https://doi.org/10.1007/s00158-018-1906-6
Show More Cited By

Index Terms

Improving global numerical optimization using a search-space reduction algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Published In

cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

Acceptance Rates

GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

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

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Cited By

Iacca Gde Melo V(2021)Cluster-centroid-based mutation strategies for Differential EvolutionSoft Computing10.1007/s00500-021-06448-zOnline publication date: 6-Nov-2021
https://doi.org/10.1007/s00500-021-06448-z
Akbari HKazerooni A(2020)KASRA: A Kriging-based adaptive space reduction algorithm for global optimization of computationally expensive black-box constrained problemsApplied Soft Computing10.1016/j.asoc.2020.106154(106154)Online publication date: Feb-2020
https://doi.org/10.1016/j.asoc.2020.106154
Ye PPan GDong Z(2018)Ensemble of surrogate based global optimization methods using hierarchical design space reductionStructural and Multidisciplinary Optimization10.1007/s00158-018-1906-658:2(537-554)Online publication date: 6-Feb-2018
https://doi.org/10.1007/s00158-018-1906-6
Ziogou CPistikopoulos EGeorgiadis MVoutetakis SPapadopoulou S(2013)Empowering the Performance of Advanced NMPC by Multiparametric Programming—An Application to a PEM Fuel Cell SystemIndustrial & Engineering Chemistry Research10.1021/ie303477h52:13(4863-4873)Online publication date: 22-Mar-2013
https://doi.org/10.1021/ie303477h

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