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
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of 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
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
Learning and Intelligent Optimization: Second International Conference, LION 2007 II, Trento, Italy, December 8-12, 2007. Selected Papers
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Learning and Intelligent Optimization
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
Autonomous Control Approach for Local Search
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
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
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
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
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
ShareBoost: boosting for multi-view learning with performance guarantees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
Hyperparameter tuning in bandit-based adaptive operator selection
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
An exploration-exploitation compromise-based adaptive operator selection for local search
Proceedings of the 14th 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
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The goal of Adaptive Operator Selection is the on-line control of the choice of variation operators within Evolutionary Algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed Adaptive Operator Selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; Dynamic Multi-Armed Bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the Satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches.