Title :
An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm
Author :
Shi, X.H. ; Wan, L.M. ; Lee, H.P. ; Yang, X.W. ; Wang, L.M. ; Liang, Y.C.
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Abstract :
This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the optimization problem.
Keywords :
artificial life; genetic algorithms; genetic algorithm; hybrid evolutionary algorithm; natural features; optimization problem; particle swarm optimization; variable population-size; Biological cells; Computational modeling; Computer science; Data structures; Educational institutions; Evolutionary computation; Genetic algorithms; High performance computing; Mathematics; Particle swarm optimization;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259777
