DocumentCode
3001862
Title
Adapting the genetic algorithm to the travelling salesman problem
Author
Pullan, Wayne
Author_Institution
Sch. of Inf. Technol., Griffith Univ., Gold Coast, Qld., Australia
Volume
2
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
1029
Abstract
The combination of local optimisation heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the travelling salesman problem (TSP). In problem domains where the problem can be represented geometrically, such as networks and chemical structures, the combination of local optimisation operators and phenotype genetic operators has also been an effective approach. This paper evaluates the combination of local optimisation heuristics and phenotype genetic operators when applied to the TSP. The local optimisation heuristics reduce the search domain, while the phenotype genetic operators eliminate the creation of invalid tours and also assist the generation of suboptimal schema. The implementation of the genetic algorithm is described and results presented.
Keywords
genetic algorithms; travelling salesman problems; genetic algorithm; local optimisation heuristics; phenotype genetic operators; travelling salesman problem; Atomic measurements; Australia; Chemicals; Genetic algorithms; Gold; Information technology; NP-hard problem; Optimization methods; Simulated annealing; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299781
Filename
1299781
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