Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding
Machine Learning - Special issue on genetic algorithms
Journal of Global Optimization
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Advances in Differential Evolution
Advances in Differential Evolution
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
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
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Proceedings of the 12th annual conference companion 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
Adaptive parameter selection for strategy adaptation in differential evolution
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
From adaptive to more dynamic control in evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Differential evolution with improved mutation strategy
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Multi-Objective differential evolution with adaptive control of parameters and operators
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
Parameter optimization of PEMFC model with improved multi-strategy adaptive differential evolution
Engineering Applications of Artificial Intelligence
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Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. Different strategies have been proposed for the offspring generation; but the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, the probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. Four credit assignment methods, that update the known performance of each strategy based on the relative fitness improvement achieved by its recent applications, are analyzed. To evaluate the performance of our approach, thirteen widely used benchmark functions are used. Experimental results confirm that our approach is able to adaptively choose the suitable strategy for different problems. Compared to classical DE algorithms and to a recently proposed adaptive scheme (SaDE), it obtains better results in most of the functions, in terms of the quality of the final results and convergence speed.