DocumentCode
3256558
Title
Solving randomly generated constraint satisfaction problems using a micro-evolutionary hybrid that evolves a population of hill-climbers
Author
Dozier, Gerry ; Bowen, James ; Bahler, Dennis
Author_Institution
Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
Volume
2
fYear
1995
fDate
29 Nov-1 Dec 1995
Firstpage
614
Abstract
This paper introduces a new micro-evolutionary search technique which combines the concept of evolutionary searching with the systematic search concept of hill climbing to form a hybrid that quickly find solutions to constraint satisfaction problems. This new hybrid outperforms a well-known hill climber, the iterative descent method (IDM), on a test suite of 750 randomly-generated constraint satisfaction problems
Keywords
constraint handling; genetic algorithms; problem solving; random processes; search problems; hill-climber population evolution; iterative descent method; micro-evolutionary search technique; performance; randomly generated constraint satisfaction problems; Computer science; Evolutionary computation; Hybrid power systems; Iterative methods; Machine learning; Neural networks; Robustness; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2759-4
Type
conf
DOI
10.1109/ICEC.1995.487454
Filename
487454
Link To Document