The parallel genetic algorithm as function optimizer

✍ Scribed by H. Mühlenbein; M. Schomisch; J. Born

• Book ID: 104304340
• Publisher: Elsevier Science
• Year: 1991
• Tongue: English
• Weight: 778 KB
• Volume: 17
• Category: Article
• ISSN: 0167-8191
• DOI: 10.1016/s0167-8191(05)80052-3

✦ Synopsis

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.