Title :
Mixed Mutation Strategy Embedded Differential Evolution
Author :
Pant, Millie ; Ali, Musrrat ; Abraham, Ajith
Author_Institution :
Indian Inst. of Technol. Roorkee, Saharanpur
Abstract :
Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real valued optimization problems. Traditional investigations with differential evolution have used a single mutation operator. Using a variety of mutation operators that can be integrated during evolution could hold the potential to generate a better solution with less computational effort. In view of this, in this paper a mixed mutation strategy which uses the concept of evolutionary game theory is proposed to integrate basic differential evolution mutation and quadratic interpolation to generate a new solution. Throughout of this paper we refer this new algorithm as, differential evolution with mixed mutation strategy (MSDE). The performance of proposed algorithm is investigated and compared with basic differential evolution. The experiments conducted shows that proposed algorithm outperform the basic DE algorithm in all the benchmark problems.
Keywords :
evolutionary computation; game theory; interpolation; optimisation; DE; differential evolution; evolutionary algorithm; game theory; mixed mutation strategy; quadratic interpolation; real valued optimization problem; Convergence; Evolutionary computation; Game theory; Genetic algorithms; Genetic mutations; Genetic programming; Interpolation; Machine intelligence; Quality of service; differential evolution; mixed strategy; mutation operator;
Conference_Titel :
Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-2958-5
Electronic_ISBN :
978-1-4244-2959-2
DOI :
10.1109/CEC.2009.4983087