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A model of human performance on the traveling salesperson problem.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A model of human performance on the traveling salesperson problem. / MacGregor, J. N.; Ormerod, Thomas C.; Chronicle, E.
In: Memory and Cognition, Vol. 28, No. 7, 2000, p. 1183-1190.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

MacGregor, JN, Ormerod, TC & Chronicle, E 2000, 'A model of human performance on the traveling salesperson problem.', Memory and Cognition, vol. 28, no. 7, pp. 1183-1190. <http://www.psychonomic.org/search/view.cgi?id=2872>

APA

MacGregor, J. N., Ormerod, T. C., & Chronicle, E. (2000). A model of human performance on the traveling salesperson problem. Memory and Cognition, 28(7), 1183-1190. http://www.psychonomic.org/search/view.cgi?id=2872

Vancouver

MacGregor JN, Ormerod TC, Chronicle E. A model of human performance on the traveling salesperson problem. Memory and Cognition. 2000;28(7):1183-1190.

Author

MacGregor, J. N. ; Ormerod, Thomas C. ; Chronicle, E. / A model of human performance on the traveling salesperson problem. In: Memory and Cognition. 2000 ; Vol. 28, No. 7. pp. 1183-1190.

Bibtex

@article{6c36c1bd32034d019b54d70a8326d49d,
title = "A model of human performance on the traveling salesperson problem.",
abstract = "A computational model is proposed of how humans solve the traveling salesperson problem (TSP). Tests of the model are reported, using human performance measures from a variety of 10-, 20-, 40-, and 60-node problems, a single 48-node problem, and a single 100-node problem. The model provided a range of solutions that approximated the range of human solutions and conformed closely to quantitative and qualitative characteristics of human performance. The minimum path lengths of subjects and model deviated by average absolute values of 0.0%, 0.9%, 2.4%, 1.4%, 3.5%, and 0.02% for the 10-, 20-, 40-, 48-, 60-, and 100-node problems, respectively. Because the model produces a range of solutions, rather than a single solution, it may find better solutions than some conventional heuristic algorithms for solving TSPs, and comparative results are reported that support this suggestion.",
author = "MacGregor, {J. N.} and Ormerod, {Thomas C.} and E. Chronicle",
year = "2000",
language = "English",
volume = "28",
pages = "1183--1190",
journal = "Memory and Cognition",
issn = "1532-5946",
publisher = "Springer New York",
number = "7",

}

RIS

TY - JOUR

T1 - A model of human performance on the traveling salesperson problem.

AU - MacGregor, J. N.

AU - Ormerod, Thomas C.

AU - Chronicle, E.

PY - 2000

Y1 - 2000

N2 - A computational model is proposed of how humans solve the traveling salesperson problem (TSP). Tests of the model are reported, using human performance measures from a variety of 10-, 20-, 40-, and 60-node problems, a single 48-node problem, and a single 100-node problem. The model provided a range of solutions that approximated the range of human solutions and conformed closely to quantitative and qualitative characteristics of human performance. The minimum path lengths of subjects and model deviated by average absolute values of 0.0%, 0.9%, 2.4%, 1.4%, 3.5%, and 0.02% for the 10-, 20-, 40-, 48-, 60-, and 100-node problems, respectively. Because the model produces a range of solutions, rather than a single solution, it may find better solutions than some conventional heuristic algorithms for solving TSPs, and comparative results are reported that support this suggestion.

AB - A computational model is proposed of how humans solve the traveling salesperson problem (TSP). Tests of the model are reported, using human performance measures from a variety of 10-, 20-, 40-, and 60-node problems, a single 48-node problem, and a single 100-node problem. The model provided a range of solutions that approximated the range of human solutions and conformed closely to quantitative and qualitative characteristics of human performance. The minimum path lengths of subjects and model deviated by average absolute values of 0.0%, 0.9%, 2.4%, 1.4%, 3.5%, and 0.02% for the 10-, 20-, 40-, 48-, 60-, and 100-node problems, respectively. Because the model produces a range of solutions, rather than a single solution, it may find better solutions than some conventional heuristic algorithms for solving TSPs, and comparative results are reported that support this suggestion.

M3 - Journal article

VL - 28

SP - 1183

EP - 1190

JO - Memory and Cognition

JF - Memory and Cognition

SN - 1532-5946

IS - 7

ER -