Application of human learning concepts to combinatorial optimization problems

Jinhwa Kim, Scott Webster

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study suggests an approach for solving combinatorial optimization problems based on an understanding of learning processes in the human brain. With the information and inspiration from cognitive sciences, we suggest an approach called simulated learning, which simulates human learning and problem solving processes. Its advantage lies in the application to highly unsimulatable problems, where only a set of past solution examples is available. There are three main steps in this method. First, solutions with good performance are selected from a set of randomly generated examples. Second, the combinatorial information from the selected examples is stored into artificial memory matrices. Third, a good solution is derived by analyzing the patterns in the matrices. The results from experiment confirm the efficiency of the method.

Original languageEnglish (US)
Title of host publicationProceedings - Annual Meeting of the Decision Sciences Institute
Editors Anon
Place of PublicationAtlanta, GA, United States
PublisherDecis Sci Inst
Pages513
Number of pages1
Volume2
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3) - San Diego, CA, USA
Duration: Nov 22 1997Nov 25 1997

Other

OtherProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3)
CitySan Diego, CA, USA
Period11/22/9711/25/97

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ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

Cite this

Kim, J., & Webster, S. (1997). Application of human learning concepts to combinatorial optimization problems. In Anon (Ed.), Proceedings - Annual Meeting of the Decision Sciences Institute (Vol. 2, pp. 513). Decis Sci Inst.