Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Extreme Value Based Adaptive Operator Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A dynamic island model for adaptive operator selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
The purpose of adaptive operator selection is to choose dynamically the most suitable variation operator of an evolutionary algorithm at each iteration of the search process. These variation operators are applied on individuals of a population which evolves, according to an evolutionary process, in order to find an optimal solution. Of course the efficiency of an operator may change during the search and therefore its application should be precisely controlled. In this paper, we use dynamic island models as operator selection mechanisms. A sub-population is associated to each operators and individuals are allowed to migrate from one sub-population to another one. In order to evaluate the performance of this adaptive selection mechanism, we propose an abstract operator representation using fitness improvement distributions that allow us to define non stationary operators with mutual interactions. Our purpose is to show that the adaptive selection is able to identify not only good operators but also suitable sequences of operators.