ICHEA for discrete 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:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

Citing 14
Cited 0

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
Solving Binary Constraint Satisfaction Problems Using Evolutionary Algorithms with an Adaptive Fitness Function

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Co-evolutionary Constraint Satisfaction

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Experimental complexity analysis of continuous constraint satisfaction problems

Information Sciences: an International Journal
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems

Evolutionary Computation
Solving n-queens with a genetic algorithm and its usefulness in a computational intelligence course

Journal of Computing Sciences in Colleges
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

Applied Soft Computing
A review of constraint-handling techniques for evolution strategies

Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
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

Quantified Score

Hi-index	0.00

Visualization

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.