Using Genetic Algorithms with Real-coded Binary Representation for Solving Non-stationary Problems
This paper presents genetic algorithms with real-coded binary representation - a novel approach to improve the performance of genetic algorithms. The algorithm is capable of maintaining the diversity of the evolved population during the whole run which protects it from the premature convergence. This is achieved by using a special encoding scheme, introducing a high redundancy, which is further supported by the so-called gene-strength adaptation mechanism for controlling the diversity. The mechanism for the population diversity self-regulation increases the robustness of the algorithm when solving non-stationary problems as was empirically proven on two test cases. The achieved results show the competitiveness of the proposed algorithm with other techniques designed for solving non-stationary problems.
KeywordsGenetic Algorithm Tracking Error Knapsack Problem Standard Genetic Algorithm Binary Gene
Unable to display preview. Download preview PDF.
- Fukunaga, A. (1997) Restart scheduling for genetic algorithms. In Thomas Back, editor, Proceedings of ICGA’97, 1997.Google Scholar
- Morrison, R. W., De Jong, K. A. (2000) Triggered hypermutation revisited. In Proceedings of CEC’2000, pp. 1025–1032Google Scholar
- Collard, P., Gaspar, A. (1996) Royal-road landscapes for a dual genetic algorithm, in W. Wahlster, editor, ECAI 96: 12th European Conference on Artificial Intelligence, Wiley & Son, pp 214–217Google Scholar
- Eggermont, J., Lenaerts, T. (2002) Dynamic Optimization using Evolutionary Algorithms with a Case-based Memory, Proceedings of BNAIC’02, K.U.Leuven, Belgium, pages 107–114Google Scholar
- Lewis, J., Hart, E., Ritchie, G. (1998) A comparison of dominance mechanisms and simple mutation on non-stationary problems. In Eiben, A.E. et al. (Eds.). Proceedings of PPSN’98, LNCS 1498, pp. 139–148, SpringerGoogle Scholar
- Ng, K. P., Wong, K. C. (1995) A new diploid scheme and dominance change mechanism for non-stationary function optimization. In Proceedings of ICGA’95, pp. 159–166. Morgan KaufmannGoogle Scholar
- Ošmera, P., Kvasnička, V., Pospíchal, J. (1997) Genetic algorithms with diploid chromosomes. In Proceedings of Mendel’97, pp. 111–116Google Scholar