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ICHEA for Discrete Constraint Satisfaction Problems

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AI 2012: Advances in Artificial Intelligence (AI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

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Abstract

Constraint satisfaction problem (CSP) is a subset of optimization problem where at least one solution is sought that satisfies all the given constraints. Presently, evolutionary algorithms (EAs) have become standard optimization techniques for solving unconstrained optimization problems where the problem is formalized for discrete or continuous domains. However, traditional EAs are considered ‘blind’ to constraint as they do not extract and exploit information from the constraints. A variation of EA – intelligent constraint handling for EA (ICHEA) proposed earlier models constraints to guide the evolutionary search to get improved and efficient solutions for continuous CSPs. As many real world CSPs have constraints defined in the form of discrete functions, this paper serves as an extension to ICHEA that reports its applicability for solving discrete CSPs. The experiment has been carried on a classic discrete CSP – the N-Queens problem. The experimental results show that extracting information from constraints and exploiting it in the evolutionary search makes the search more efficient. This provision is a problem independent formulation in ICHEA.

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Authors and Affiliations

  1. Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia

    Anurag Sharma & Dharmendra Sharma

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  1. Anurag Sharma
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  2. Dharmendra Sharma
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Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Sharma, A., Sharma, D. (2012). ICHEA for Discrete Constraint Satisfaction Problems. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_21

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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