ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms

Anurag Sharma, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contribution

7 Citations (Scopus)

Abstract

Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are ‘blind’ to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsT Huang, Zhigang Zeng, C Li, C. S. Leung
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages269-279
Number of pages11
Volume7663
ISBN (Electronic)9783642344756
ISBN (Print)9783642344749
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Evolutionary algorithms
Constraint satisfaction problems

Cite this

Sharma, A., & Sharma, D. (2012). ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 7663, pp. 269-279). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-34475-6_33
Sharma, Anurag ; Sharma, Dharmendra. / ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms. International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. editor / T Huang ; Zhigang Zeng ; C Li ; C. S. Leung. Vol. 7663 Berlin Heidelberg : Springer, 2012. pp. 269-279
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abstract = "Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are ‘blind’ to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.",
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Sharma, A & Sharma, D 2012, ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms. in T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. vol. 7663, Springer, Berlin Heidelberg, pp. 269-279, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34475-6_33

ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms. / Sharma, Anurag; Sharma, Dharmendra.

International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / T Huang; Zhigang Zeng; C Li; C. S. Leung. Vol. 7663 Berlin Heidelberg : Springer, 2012. p. 269-279.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AU - Sharma, Dharmendra

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AB - Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are ‘blind’ to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.

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Sharma A, Sharma D. ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms. In Huang T, Zeng Z, Li C, Leung CS, editors, International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. Vol. 7663. Berlin Heidelberg: Springer. 2012. p. 269-279 https://doi.org/10.1007/978-3-642-34475-6_33