Title :
The self-adaptive Pareto differential evolution algorithm
Author :
Abbass, Hussein A.
Author_Institution :
Sch. of Comput. Sci., Univ. of New South Wales, Canberra, ACT, Australia
Abstract :
The Pareto differential evolution (PDE) algorithm was introduced and showed competitive results. The behavior of PDE, as in many other evolutionary multiobjective optimization (EMO) methods, varies according to the crossover and mutation rates. In this paper, we present a new version of PDE with self-adaptive crossover and mutation. We call the new version self-adaptive Pareto differential evolution (SPDE). The emphasis of this paper is to analyze the dynamics and behavior of SPDE. The experiments also show that the algorithm is very competitive with other EMO algorithms
Keywords :
adaptive systems; evolutionary computation; optimisation; crossover rates; dynamics; evolutionary multiobjective optimization methods; mutation rates; self-adaptive Pareto differential evolution algorithm; Algorithm design and analysis; Australia; Computer science; Evolutionary computation; Genetic mutations; Optimization methods; Pareto optimization; Upper bound;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1007033
