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
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
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
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
Real-Valued constraint optimization with ICHEA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Solving dynamic constraint optimization problems using ICHEA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
An incremental approach to solving dynamic constraint satisfaction problems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
ICHEA for discrete constraint satisfaction problems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are 'blind' to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.