An incremental approach to solving dynamic constraint satisfaction problems

Authors:
Anurag Sharma;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

Citing 11
Cited 1

Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions

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Constraint Solving in Uncertain and Dynamic Environments: A Survey

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Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

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A fast and elitist multiobjective genetic algorithm: NSGA-II

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Comparing evolutionary algorithms on binary constraint satisfaction problems

IEEE Transactions on Evolutionary Computation
ICHEA: a constraint guided search for improving evolutionary algorithms

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I

Solving dynamic constraint optimization problems using ICHEA

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III

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Abstract

Constraint satisfaction problems (CSPs) underpin many science and engineering applications. Recently introduced intelligent constraint handling evolutionary algorithm (ICHEA) in [14] has demonstrated strong potential in solving them through evolutionary algorithms (EAs). ICHEA outperforms many other evolutionary algorithms to solve CSPs with respect to success rate (SR) and efficiency. This paper is an enhancement of ICHEA to improve its efficiency and SR further by an enhancement of the algorithm to deal with local optima obstacles. The enhancement also includes a capability to handle dynamically introduced constraints without restarting the whole algorithm that uses the knowledge from already solved constraints using an incremental approach. Experiments on benchmark CSPs adapted as dynamic CSPs has shown very promising results.