An introduction to differential evolution
New ideas in optimization
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
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)
DE/isolated/1: a new mutation operator for multimodal optimization with differential evolution
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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A new mutation concept is proposed to generalize local selection based Differential Evolution algorithm to work in general multi-modal problems. Three variations of the proposed method are compared with classic Differential Evolution algorithm using a set of five well known test functions and their variants. The general idea of the new mutation operation is to divide the mutation into two parts: the local and global mutation. The global mutation works as a migration operator allowing the algorithm perform global search efficiently, while the local mutation improves the efficiency of local search. The results show that the concept of global mutation is able to generalize the good performance of local selection based Differential Evolution from convex uni-modal functions to general non-convex and multi-modal problems. Among the tested functions, the new method was able to outperform the classic Differential Evolution in all butone. A limited analysis of the effects of control parameters to the performance of the algorithm is also done.