Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-level ranking for constrained multi-objective evolutionary optimisation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Solving dynamic constrained optimization problems with asynchronous change pattern
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A predictive evolutionary algorithm for dynamic constrained inverse kinematics problems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
An evolutionary linear programming algorithm for solving the stock reduction problem
International Journal of Computer Applications in Technology
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A number of population based optimization algorithms have been proposed in recent years to solve unconstrained and constrained single and multi-objective optimization problems. Most of such algorithms inherently prefer a feasible solution over an infeasible one during the course of search, which translates to approaching the constraint boundary from the feasible side of the search space. Previous studies [1], [2] have already demonstrated the benefits of explicitly maintaining a fraction of infeasible solutions in Infeasiblity Driven Evolutionary Algorithm (IDEA) for single and multi-objective constrained optimization problems. In this paper, the benefits of IDEA as a sub-evolve mechanism are highlighted for dynamic, constrained single objective optimization problems. IDEA is particularly attractive for such problems as it offers a faster rate of convergence over a conventional EA, which is of significant interest in dynamic optimization problems. The algorithm is tested on two new dynamic constrained test problems. For both the problems, the performance of IDEA is found to be significantly better than conventional EA.