Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Asymptotically efficient adaptive control in stochastic regression models
Advances in Applied Mathematics
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
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
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Extreme Value Based Adaptive Operator Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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: dynamic multi-armed bandits for adaptive operator selection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
On benchmark properties for adaptive operator selection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Adaptive Operator Selection for Iterated Local Search
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Toward comparison-based adaptive operator selection
Proceedings of the 12th annual conference 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
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
DAMS: distributed adaptive metaheuristic selection
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Generalized adaptive pursuit algorithm for genetic pareto local search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Policy learning in resource-constrained optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The road to VEGAS: guiding the search over neutral networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
From adaptive to more dynamic control in evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Communications of the ACM
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Autonomous Agents and Multi-Agent Systems
Evolutionary operator self-adaptation with diverse operators
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
Siblingrivalry: online autotuning through local competitions
Proceedings of the 2012 international conference on Compilers, architectures and synthesis for embedded systems
Evolutionary functional black-box testing in an industrial setting
Software Quality Control
Sustainable cooperative coevolution with a multi-armed bandit
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Non stationary operator selection with island models
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolution by adapting surrogates
Evolutionary Computation
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An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offspring; and an adaptation rule, that modifies the selection mechanism based on the rewards of the different operators. This paper is concerned with the latter, and proposes a new approach for it based on the well-known Multi-Armed Bandit paradigm. However, because the basic Multi-Armed Bandit methods have been developed for static frameworks, a specific Dynamic Multi-Armed Bandit algorithm is proposed, that hybridizes an optimal Multi-Armed Bandit algorithm with the statistical Page-Hinkley test, which enforces the efficient detection of changes in time series. This original Operator Selection procedure is then compared to the state-of-the-art rules known as Probability Matching and Adaptive Pursuit on several artificial scenarios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outperform the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best.