Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Controlling Genetic Algorithms With Reinforcement Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Adapting operator settings in genetic algorithms
Evolutionary Computation
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Solving constraint satisfaction problems using hybrid evolutionarysearch
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
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In this paper, we evaluate parameter control strategies for evolutionary approaches to solve constrained combinatorial problems. For testing, we have used two well known evolutionary algorithms that solve the Constraint Satisfaction Problems GSA and SAW. We contrast our results with REVAC, a recently proposed technique for parameter tuning.