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
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Adaptive strategy selection in differential evolution
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Different strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PM-AdapSS-DE) [4], a method proposed to automatically select the mutation strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform strategy selection within the same sub-set of strategies.