Doctoral Dissertations

Computational intelligence and the traveling salesman


"The Traveling Salesman Problem (TSP) is one of the most widely studied problems in the computer science literature...Computational Intelligence approaches have traditionally performed very poorly on combinatorial optimization problems such as the TSP, when compared to results in Operations Research. This dissertation explores two different Computational Intelligence techniques and combines them with the latest Operations Research local search heuristics to develop new heuristics that expand the capabilities of existing techniques. The first approach combines an Adaptive Resonance Theory (ART) neural network with the iterated Lin-Kernighan algorithm to divide and conquer extremely large TSPs. This new algorithm provides significant advantages in scaling and memory usage. The second approach uses an Evolutionary Algorithm to parallelize the iterated Lin-Kernighan algorithm and explore the search space more thoroughly"--Abstract, page iii.


Computer Science

Degree Name

Ph. D. in Computer Science


System requirements: C++ Compiler required. Microsoft Visual Studio.NET using the Intel C++ Compiler 8.0 recommended.


University of Missouri--Rolla

Publication Date

Summer 2004


ix, 77 pages; CD-ROM

Note about bibliography

Includes bibliographical references (pages 73-76).


© 2004 Samuel Aaron Mulder, All rights reserved.

Document Type

Dissertation - Citation

File Type




Subject Headings

Traveling-salesman problem
Heuristic programming
Combinatorial optimization

Thesis Number

T 8560

Print OCLC #


Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

This document is currently not available here.

Share My Dissertation If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.