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SEMCCO
2010

Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies

9 years 11 days ago
Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies
Differential Evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. Thus, to obtain optimal performance, time consuming parameter tuning is necessary. Different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper, we propose a DE with an ensemble of mutation and crossover strategies and their associated control parameters known as EPSDE. In EPSDE, a pool of distinct mutation and crossover strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of 25 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with st...
Rammohan Mallipeddi, Ponnuthurai Nagaratnam Sugant
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SEMCCO
Authors Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan
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