Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding
Machine Learning - Special issue on genetic algorithms
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Use of statistical outlier detection method in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Adapting operator settings in genetic algorithms
Evolutionary Computation
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analysis of adaptive operator selection techniques on the royal road and long k-path problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning and Intelligent Optimization
| Hi-index | 0.00 |
The performance of evolutionary algorithms is highly affected by the selection of the variation operators to solve the problem at hand. This abstract presents a survey of results that have been obtained using the "Extreme - Dynamic Multi-Armed Bandit" (Ex-DMAB), a technique used to automatically select the operator to be applied between the available ones, while searching for the solution. Experiments on three well-known artificial problems of the EC community are presented, namely the OneMax, the long k-path and the Royal Road, demonstrating some improvements over both any choice of a single-operator alone, and the naive uniform choice of one operator at each application. The Ex-DMAB approach is also compared to the optimal choice of operators, whenever available. The results are discussed in the light of the new parameters that are introduced to tune the selection technique...