Theoretical aspects of evolutionary computing
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
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Triggered Memory-Based Swarm Optimization in Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Dynamic optimization using self-adaptive differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A new hybrid approach for dynamic continuous optimization problems
Applied Soft Computing
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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
A simple multimembered evolution strategy to solve constrained optimization 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
Real-Valued constraint optimization with 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
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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.