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
fDate :
29 Nov-1 Dec 1995
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;
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2759-4
DOI :
10.1109/ICEC.1995.487454
