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
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Adapting operator settings in genetic algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
DAMS: distributed adaptive metaheuristic selection
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Controlling parameters during execution of parallel evolutionary algorithms is an open research area. Some recent research have already shown good results applying self-calibrating strategies. The motivation of this work is to improve the search of parallel genetic algorithms using monitoring techniques. Monitoring results guides the algorithm to take some actions based on both the search state and the values of its parameters. In this paper, we propose a parameter control architecture for parallel evolutionary algorithms, based on self-adaptable monitoring techniques. Our approach provides an efficient and low cost monitoring technique to design parameters control strategies. Moreover, it is completely independant of the implementation of the evolutionary algorithm.