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Title: Constraint satisfaction using a hybrid evolutionary hill-climbing algorithm that performs opportunistic arc and path revision

This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves (Constraint Satisfaction Problems (CSPs)). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method, on a test suite of 750 randomly generated CSPs.
Authors:
 [1] ;  [2]
  1. National Univ. of Ireland, Cork (Ireland)
  2. North Carolina A&T State Univ., Greensboro, NC (United States)
Publication Date:
OSTI Identifier:
430675
Report Number(s):
CONF-960876--
TRN: 96:006521-0050
Resource Type:
Conference
Resource Relation:
Conference: 13. National conference on artifical intelligence and the 8. Innovative applications of artificial intelligence conference, Portland, OR (United States), 4-8 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the thirteenth national conference on artificial intelligence and the eighth innovative applications of artificial intelligence conference. Volume 1 and 2; PB: 1626 p.
Publisher:
American Association for Artificial Intelligence, Menlo Park, CA (United States)
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; ARTIFICIAL INTELLIGENCE; ALGORITHMS; INFORMATION RETRIEVAL; NETWORK ANALYSIS; LEARNING