DocumentCode
303964
Title
Solving randomly generated fuzzy constraint networks using evolutionary/systematic hill-climbing
Author
Bowen, James ; Dozier, Gerry
Author_Institution
Dept. of Comput. Sci., Nat. Univ. of Ireland, Cork, Ireland
Volume
1
fYear
1996
fDate
8-11 Sep 1996
Firstpage
226
Abstract
This paper introduces an evolutionary/systematic hybrid which combines the concept of evolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid that quickly find solutions to fuzzy constraint satisfaction problems. This hybrid outperforms a modified version of a well known hill-climber, the iterative descent method, on a test suite of 500 randomly generated fuzzy constraint networks
Keywords
constraint handling; fuzzy set theory; genetic algorithms; search problems; arc revision; evolutionary algorithm; evolutionary/systematic hybrid; fuzzy constraint networks; fuzzy constraint satisfaction problems; fuzzy set theory; hill-climbing search; systematic search; Computer science; Control engineering; Evolutionary computation; Fuzzy control; Fuzzy sets; Fuzzy systems; Machine learning; NASA; Robustness; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3645-3
Type
conf
DOI
10.1109/FUZZY.1996.551746
Filename
551746
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