Improving the Performance and Scalability of Differential Evolution

Authors:
Antony W. Iorio;Xiaodong Li
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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Abstract

Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity. The new DE approach demonstrates fast convergence towards the global optimum and is highly scalable in the decision space.