Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Journal of Global Optimization
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Exploring dynamic self-adaptive populations in differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Differential Evolution (DE) is a popular and efficient optimization technique for real-valued spaces based on the concepts of Darwinian evolution. Its main peculiarity is the use of a differential mutation operator that allows DE to automatically adjust the exploration/exploitation balance of its search moves. The major DE drawback is the need of a preliminary tuning of some numerical parameters. Although, recently some parameters adaptive schemes have been proposed, none of these takes into account the side effects introduced by changing two or more parameters at the same time. In this paper we introduce a DE self-adaptive scheme that takes into account the parameters dependencies by means of a multivariate probabilistic technique based on an Estimation of Distribution Algorithm working on the parameters space. Experiments have been performed on a set of commonly adopted benchmark problems and the obtained results show the competitiveness of our approach with respect to other adaptive DE schemes. Moreover, our scheme could be potentially adopted not only in DE but also in any other Evolutionary Algorithm or meta-heuristic technique presenting parameters that regulate the behavior of the search.