Dynamics in Proportionate Selection.
This paper proposes a new selection method for Genetic Algorithms. The motivation behind the proposed method is to investigate the effect of different selection methods on the rate of convergence. The new method Dynamic Selection Method (DSM) is based on proportionate selection. DSM functions by continuously changing the criteria for parent selection (dynamic) based on the number of generations in a run and the current generation. Results show that by using DSM to maintain diversity in a population gives slower convergence, but, their overall performance was an improvement. Relationship between slower convergences, in GA runs, leading to better solutions, has been identified.
KeywordsGenetic Algorithm Travel Salesman Problem Slow Convergence Replacement Ratio Parent Selection
Unable to display preview. Download preview PDF.
- Holland, J. (1975). Adaptation In Search, Optimization & Machine Learning. Addison-Wesley, 1989 Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.Google Scholar
- M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, 1996.Google Scholar
- Back T. (1996). Evolutionary Algorithms in Theory and Practice, Oxford University Press, NY.Google Scholar
- Goldberg, D. E. and Deb, K., "A Comparative Analysis of Selection Schemes Used in Genetic Algorithms," in FGA1, pp.69–93, 1991Google Scholar
- Baker, J. E.: Adaptive Selection Methods for Genetic Algorithms. Proceedings of an International Conference on Genetic Algorithms and their Application, pp. 101–111, Hillsdale, New Jersey, USA: Lawrence Erlbaum Associates, 1985Google Scholar
- E. Lawler, J. Lenstra, A. Rinnooy Kan, and D. Shmoys. The Traveling Salesman Problem. John Wiley, 1985.Google Scholar
- I. Oliver, D. Smith, and J. Holland. A study of permutation crossover operators on the travelling salesman problem. In Grefenstette J.J.Google Scholar
- Whitley, D. (1989). The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In Proc. 3th International Conf. on Genetic Algorithms. 116, D. Schaffer, ed., Morgan Kaufmann.Google Scholar