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Solving Dynamic Constraint Optimization Problems Using ICHEA

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7665)

Abstract

Many real-world constrained problems have a set of predefined static constraints that can be solved by evolutionary algorithms (EAs) whereas some problems have dynamic constraints that may change over time or may be received by the problem solver at run time. Recently there has been some interest in academic research for solving continuous dynamic constraint optimization problems (DCOPs) where some new benchmark problems have been proposed. Intelligent constraint handling evolutionary algorithm (ICHEA) is demonstrated to be a versatile constraints guided EA for continuous constrained problems which efficiently solves constraint satisfaction problems (CSPs) in [22], constraint optimization problems (COPs) in [23] and dynamic constraint satisfaction problems (DCSPs) in [24]. We investigate efficiency of ICHEA in solving benchmark DCOPs and compare and contrast its performance with other well-known EAs.

Keywords

  • evolutionary algorithm (EA)
  • constraint satisfaction problem (CSP)
  • dynamic constraint satisfaction problems (DCSP)
  • constraint optimization problem (COP)
  • dynamic constraint optimization problem (DCOP)
  • Intelligent constraint handling evolutionary algorithm (ICHEA)

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Sharma, A., Sharma, D. (2012). Solving Dynamic Constraint Optimization Problems Using ICHEA. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_53

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  • DOI: https://doi.org/10.1007/978-3-642-34487-9_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34486-2

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