THE PARALLEL GENETIC ALGORITHM AS FUNCTION OPTIMIZER

被引：409
|
作者：
MUHLENBEIN, H [1 ]
SCHOMISCH, M [1 ]
BORN, J [1 ]
机构：
[1] INST INFORMAT & RECHENTECH, W-1199 BERLIN ADLERSHOF, GERMANY
来源：
PARALLEL COMPUTING | 1991年 / 17卷 / 6-7期
关键词：
SEARCH METHODS; OPTIMIZATION METHODS; PARALLEL GENETIC ALGORITHM; PERFORMANCE EVALUATION; SPEEDUP RESULTS; MINIMIZATION;
D O I：
10.1016/S0167-8191(05)80052-3
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions. The PGA uses a mixed strategy. Subpopulations try to locate good local minima. If a subpopulation does not progress after a number of generations, hillclimbing is done. Good local minima of a subpopulation are diffused to neighboring subpopulations. Many simulation results are given with popular test functions. The PGA is at least as good as other genetic algorithms on simple problems. A comparison with mathematical optimization methods is done for very large problems. Here a breakthrough can be reported. The PGA is able to find the global minimum of Rastrigin's function of dimension 400 on a 64 processor system! Furthermore, we give an example of a superlinear speedup.
引用
收藏
页码：619 / 632
页数：14
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