Control of parallel population dynamics by social-like behavior of GA-individuals
A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of “social states” individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly.
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- 1.Adams, J., Balas, E., Zawack, D.: The Shifting Bottleneck Procedure for Job Shop Scheduling. Management Science 34 (1988) 391–401Google Scholar
- 2.Applegate, D., Cook, W.: A Computational Study of the Job-Shop Scheduling Problem. ORSA Journal on Computing 2 (1991) 149–156Google Scholar
- 3.Bierwirth, C.: A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms. OR Spektrum (to appear)Google Scholar
- 4.Davidor, Y.: A Naturally Occurring Nice & Species Phenomenon: The Model and First Results. Proc. of ICGA 4, Morgan Kaufmann (1991) 257–263Google Scholar
- 5.Davidor, Y., Yamada, T., Nakano, R.: The ECOlogical Framework II, Improving GA Performance at Virtually Zero Cost. Proc. of ICGA 5, Morgan Kaufmann (1993) 171–176Google Scholar
- 6.Dell'Amico, M., Trubian, M.: Applying Tabu-Search to the Job Shop Scheduling Problem. Annals of Operations Research 41 (1993) 231–252Google Scholar
- 7.Eshelman, L.J., Schaffer, J.D.: Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. Proc. of ICGA 4, Morgan Kaufmann (1991) 115–122Google Scholar
- 8.Gorges-Schleuter, M.: Comparison of Local Mating Strategies in Massively Parallel Genetic Algorithms. Proc. of PPSN 2, North-Holland (1992) 553–562Google Scholar
- 9.van Laarhoven, P.J., Aarts, E.H., Lenstra, J.K.: Job Shop Scheduling by Simulated Annealing. Operations Research 40 (1993) 113–125Google Scholar
- 10.Mehlhorn, K., Näher, S.: LEDA, A Library of Efficient Data Types and Algorithms. TR A 05/89 University of Saarbrücken (1989)Google Scholar
- 11.Mühlenbein, H., Gorges-Schleuter, M.: Die Evolutionsstrategie: Prinzip für parallele Algorithmen. GMD Annual Report (1988)Google Scholar
- 12.Staats, A.W.: Social behaviorism. The Dorsey Press, Illinois (1975)Google Scholar
- 13.Taillard, E.: Parallel Taboo Search Techniques for the Job Shop Problem. TR ORWP 89/11, Ecole Polytechnique de Lausanne (1989)Google Scholar