Real-Valued Constraint Optimization with ICHEA

Anurag Sharma; Dharmendra Sharma

doi:10.1007/978-3-642-34487-9_50

Real-Valued Constraint Optimization with ICHEA

Anurag Sharma, Dharmendra Sharma

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Intelligent constraint handling evolutionary algorithm (ICHEA) is a recently proposed variation of evolutionary algorithm (EA) that solves realvalued constraint satisfaction problems (CSPs) efficiently [20]. ICHEA has ability to extract and exploit information from constraints that guides its evolutionary search operators in contrast to traditional EAs that are ‘blind’ to constraints. Even its efficacy to solve CSPs it was not implemented to handle constraint optimization problems (COPs). This paper proposes an enhancement to ICHEA to solve real-valued COPs. The presented approach demonstrates very competitive results with other state-of-the-art approaches in terms of quality of solutions on well-known benchmark test problems.

Original language	English
Title of host publication	International Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	T Huang, Zeng Zhigang, C Li, C. S. Leung
Place of Publication	Germany
Publisher	Springer
Pages	406-416
Number of pages	11
Volume	7665
ISBN (Electronic)	9783642344879
ISBN (Print)	9783642344862
DOIs	https://doi.org/10.1007/978-3-642-34487-9_50
Publication status	Published - 2012
Event	19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012

Conference

Conference	19th International Conference on Neural Information Processing 2012
Country/Territory	Qatar
City	Doha
Period	12/11/12 → 15/11/12

Access to Document

10.1007/978-3-642-34487-9_50

Cite this

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title = "Real-Valued Constraint Optimization with ICHEA",

abstract = "Intelligent constraint handling evolutionary algorithm (ICHEA) is a recently proposed variation of evolutionary algorithm (EA) that solves realvalued constraint satisfaction problems (CSPs) efficiently [20]. ICHEA has ability to extract and exploit information from constraints that guides its evolutionary search operators in contrast to traditional EAs that are {\textquoteleft}blind{\textquoteright} to constraints. Even its efficacy to solve CSPs it was not implemented to handle constraint optimization problems (COPs). This paper proposes an enhancement to ICHEA to solve real-valued COPs. The presented approach demonstrates very competitive results with other state-of-the-art approaches in terms of quality of solutions on well-known benchmark test problems.",

keywords = "Constraint optimization problems, ICHEA, Evolutionary algorithms",

author = "Anurag Sharma and Dharmendra Sharma",

year = "2012",

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booktitle = "International Conference on Neural Information Processing (ICONIP 2012)",

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address = "Netherlands",

note = "19th International Conference on Neural Information Processing 2012 ; Conference date: 12-11-2012 Through 15-11-2012",

}

Sharma, A & Sharma, D 2012, Real-Valued Constraint Optimization with ICHEA. in T Huang, Z Zhigang, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7665, Springer, Germany, pp. 406-416, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34487-9_50

Real-Valued Constraint Optimization with ICHEA. / Sharma, Anurag; Sharma, Dharmendra.
International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / T Huang; Zeng Zhigang; C Li; C. S. Leung. Vol. 7665 Germany: Springer, 2012. p. 406-416.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Real-Valued Constraint Optimization with ICHEA

AU - Sharma, Anurag

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - Intelligent constraint handling evolutionary algorithm (ICHEA) is a recently proposed variation of evolutionary algorithm (EA) that solves realvalued constraint satisfaction problems (CSPs) efficiently [20]. ICHEA has ability to extract and exploit information from constraints that guides its evolutionary search operators in contrast to traditional EAs that are ‘blind’ to constraints. Even its efficacy to solve CSPs it was not implemented to handle constraint optimization problems (COPs). This paper proposes an enhancement to ICHEA to solve real-valued COPs. The presented approach demonstrates very competitive results with other state-of-the-art approaches in terms of quality of solutions on well-known benchmark test problems.

AB - Intelligent constraint handling evolutionary algorithm (ICHEA) is a recently proposed variation of evolutionary algorithm (EA) that solves realvalued constraint satisfaction problems (CSPs) efficiently [20]. ICHEA has ability to extract and exploit information from constraints that guides its evolutionary search operators in contrast to traditional EAs that are ‘blind’ to constraints. Even its efficacy to solve CSPs it was not implemented to handle constraint optimization problems (COPs). This paper proposes an enhancement to ICHEA to solve real-valued COPs. The presented approach demonstrates very competitive results with other state-of-the-art approaches in terms of quality of solutions on well-known benchmark test problems.

KW - Constraint optimization problems

KW - ICHEA

KW - Evolutionary algorithms

U2 - 10.1007/978-3-642-34487-9_50

DO - 10.1007/978-3-642-34487-9_50

M3 - Conference contribution

SN - 9783642344862

VL - 7665

SP - 406

EP - 416

BT - International Conference on Neural Information Processing (ICONIP 2012)

A2 - Huang, T

A2 - Zhigang, Zeng

A2 - Li, C

A2 - Leung, C. S.

PB - Springer

CY - Germany

T2 - 19th International Conference on Neural Information Processing 2012

Y2 - 12 November 2012 through 15 November 2012

ER -

Real-Valued Constraint Optimization with ICHEA

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