Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs
Multi-step crossover fusion (MSXF) is a promising crossover method using only the neighborhood structure and the distance measure, when heuristic crossovers are hardly introduced. However, MSXF works unsteadily according to the temperature parameter, like as Simulated Annealing. In this paper, we introduce deterministic multi-step crossover fusion (dMSXF) to take this parameter away. Instead of the probabilistic acceptance of MSXF, neighbors are restricted to be closer to the goal solution, the best candidate of them is selected definitely as the next step solution. The performance of dMSXF is tested on 1max problem and Traveling Salesman Problem, and its superiority to conventional methods, e.g. uniform crossover, is shown.
KeywordsGenetic Algorithm Simulated Annealing Traveling Salesman Problem Travel Salesman Problem Neighborhood Structure
Unable to display preview. Download preview PDF.
- Markon 2001.Sandor Markon, Dirk V. Arnold, Thomas Bäck, Thomas Beielstein, and Hans-Georg Beyer: Thresholding-a Selection Operator for Noisy ES, Congress on Evolutionary Computation, pp. 465–472 (2001)Google Scholar
- Nagata 97.Nagata, Y. and Kobayashi, S.: Edge Assembly Crossover: A High-power Genetic Algorithm fot the Traveling Salesman Problem, Proceedings of the 7th International Conference on Genetic Algorithms, pp. 450–457 (1997)Google Scholar
- Nagata 2000.Nagata, Y.: Genetic Algorithm for Traveling Salesman Problem using Edge Assembly Crossover: its Proposal and Analysis, Doctoral thesis (2000)Google Scholar
- Padberg 1991.
- Reinelt 1994.G. Reinelt, The Traveling Salesman: Computational Solutions for TSP Applications. Vol. 840 of Lecture Notes in Computer Science, Springer-Verlag (1994)Google Scholar
- Satoh 1996.H. Satoh, M. Yamamura, and S. Kobayashi: Minimal Generation Gap Model for GAs Considering Both Exploration and Exploitation, Proc. of IIZUKA, pp. 494–497 (1996)Google Scholar
- Schleuter 1997.Gorges-Schleuter, M.: Asparagos96 and the Traveling Salesman Problem, Proc. of the 1997 IEEE International Conference of Evolutionary Computation, pp. 171–174 (1997)Google Scholar
- Shimodaira 1999.Hisashi Shimodaira: A Diversity Control Oriented Genetic Algorithm (DCGA): Development and Experimental Results, Proceedings of the Genetic and Evolutionary Computation Conference, pp. 603–611 (1999)Google Scholar
- Yamada 96.Yamada, T. and Ryohei, N.: Scheduling by Generic Local Search with Multi-Step Crossover, Proceedings of the 4th conference on 4th PPSN, pp. 960–969 (1996)Google Scholar
- Yamada95.Yamada, T. and Nakano, R.: A GA with multi-step crossover for jobshop scheduling problems, Proc. of Int. Conf. on GAs in Engneering Systems: Innovations and Applications (GALESIA) '95, pp. 146–151 (1995)Google Scholar