Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Asynchronous differential evolution
MMCP'11 Proceedings of the 2011 international conference on Mathematical Modeling and Computational Science
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Asynchronous differential evolution with adaptive correlation matrix
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The efficiency of an algorithm to find the global minimum depends on its ability to keep population diversity during evolutionary iterations. Statistical variance can serve as a measure of population diversity. We analyse the expected population variance after mutation and crossover for best/1/bin strategy of Classical Differential Evolution and for new strategies of a novel Asynchronous Differential Evolution. Relations between the control parameters (Np , F, Cr ) of algorithms and the extension factor of population variance are derived. Constraints on control parameters to prevent premature convergence of the algorithm are suggested and compared with phase portraits (convergence domains) for several benchmark functions.