Solving the Traveling Salesman Problem Using the Enhanced Genetic Algorithm

Authors:
Lixin Yang;Deborah A. Stacey
Affiliations:
-;-
Venue:
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Year:
2001

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Abstract

An extension to the Enhanced Genetic Algorithm (EGA) analysis of Gary W. Grewal, Thomas C. Wilson, and Deborah A. Stacey [1] is introduced and applied to the TSP. Previously the EGA had successfully handled constraint-Satisfaction problems, such as graph coloring. This paper broadens the application of the EGA to the specific NP-hard problem, the Traveling Salesman Problem (TSP). The first part of this paper deals with the unique features of the EGA such as running in an unsupervised mode, as applied to the TSP. In the second part, we present and analyze results obtained by testing the EGA approach on three TSP benchmarks while comparing the performance with other approaches using genetic algorithms. Our results show that the EGA approach is novel and successful, and its general features make it easy to integrate with other optimization techniques.