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
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
The Royal Road Functions: Description, Intent and Experimentation
Selected Papers from AISB Workshop on Evolutionary Computing
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
A description of holland's royal road function
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
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
Learning and Intelligent Optimization
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Statistical distribution of the convergence time of evolutionaryalgorithms for long-path problems
IEEE Transactions on Evolutionary Computation
Extreme: dynamic multi-armed bandits for adaptive operator selection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Adaptive strategy selection in differential evolution
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Toward comparison-based adaptive operator selection
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison-based adaptive strategy selection with bandits in differential evolution
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Evolutionary operator self-adaptation with diverse operators
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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One of the choices that most affect the performance of Evolutionary Algorithms is the selection of the variation operators that are efficient to solve the problem at hand. This work presents an empirical analysis of different Adaptive Operator Selection (AOS) methods, i.e., techniques that automatically select the operator to be applied among the available ones, while searching for the solution. Four previously published operator selection rules are combined to four different credit assignment mechanisms. These 16 AOS combinations are analyzed and compared in the light of two well-known benchmark problems in Evolutionary Computation, the Royal Road and the Long K-Path.