ABSTRACT
We describe the application of Genetic Algorithms to the traveling salesman problem with time windows. A new type of crossover operator, called edge-type crossover, with a heuristically selected initial population, is used in the genetic search. When compared with alternative methods from the literature, experiments indicate that the heuristic initialization speeds the genetic search process.
- 1.Baker, E. (1983). An Exact Algorithm for the Time Constrained Traveling Salesman Problem, Operations Research 31, 938-945.Google ScholarDigital Library
- 2.Baker, E. and J.R. Schaffer (1986). Solution Improvement Heuristics for the Vehicle Routing and Scheduling Problem with Time Window Constraints, American Journal of Mathematical and Management Sciences, Special Issue 6 (3-4), 261-300.Google Scholar
- 3.Christofides, N., A. Mingozzi and P. Toth (1981). State-Space Relaxation Procedures for the Computation of Bounds Problems, Networks 11, 145-164.Google ScholarCross Ref
- 4.Davis, L. and M. Steenstrup (1987). Genetic Algorithms and Simulated Annealing" An Overview, in G~netic Algorithms and Simulated Annealing, in Davis (ed.), Morgan Kaufmann, Los Altos, California , l-ll.Google Scholar
- 5.Eilon, S., C. Watson-Gandy and N. Christofides (1971). Distribution Management, Griffin Press, London, England.Google Scholar
- 6.Goldberg, D.E. and R. Lingle, Jr (1985). Alleles, Loci, and the Traveling Salesman Problem, in Grefenstette (ed.), Genetic Algorithms and Their Applications" Proceedings of the Second International Google ScholarDigital Library
- 7.Goldberg, D.E. (1989). Genetic Algorithms in Search Optimization, anac Machine Learning, Addison -Wesley, New York, New York. Google ScholarDigital Library
- 8.Grefenstette, J.J. (1984). A User's Guide to G~N~S,rS, Technical Report CS-84-II, Computer Science Department, Vanderbilt UniversRy, Nashville, Tennessee.Google Scholar
- 9.Grefenstette, J.J., R. Gopal, B.J. Rosmaita and D. Van Gucht (1985). Genetic Algorithms for the Traycling Salesman Problem, in Grefenstette (ed.), Proceedings of the First International Conference on Genetic Algorithms and Their Applications, 160-168. Google ScholarDigital Library
- 10.Holland, J.H. (1975). daptatian in Natural anK Artificial Systems, University of Michigan Press, Ann Arbor, Michigan. Google ScholarDigital Library
- 11.Lenstra, J. and A. Rinnooy Kan (1981). Complexity of Vehicle Routing and Scheduling Problems, Networks l I, 221-227.Google Scholar
- 12.Nygard, Kendall E. and Cheng-Hong Yang (1992). Genetic Algorithm for the Traveling Salesman Problem with Time Windows, Computer Scienc~ angf Operations Research: New DeveloPment in Their Interfaces, Pergamon Press, Oxford, England, 411-423.Google Scholar
Index Terms
- The effects of initial population in genetic search for time constrained traveling salesman problems
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