Title :
Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm
Author :
Dozier, Gerry ; Bowen, Judy ; Bahler, Dennis
Author_Institution :
Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
Abstract :
Microgenetic algorithms (MGAs) are genetic algorithms that use a very small population size (population size < 10). Recently, interest in MGAs has grown because, for some problems, they are able to find solutions with fewer evaluations than genetic algorithms with larger population sizes. This paper introduces two heuristic-based MGAs which quickly find solutions to constraint satisfaction problems. Both of these algorithms outperform a well-known algorithm, the iterative descent method, on most instances of the N-queens problem. We compare these three algorithm on the basis of the mean number of evaluations needed to find solutions to several instances of the N-queens problem
Keywords :
algorithm theory; conjugate gradient methods; constraint handling; genetic algorithms; heuristic programming; problem solving; search problems; N-queens problem; constraint satisfaction problems; heuristic-based microgenetic algorithms; iterative descent method; number of evaluations; small population size; Computer science; Convergence; Genetic algorithms; Genetic mutations; Guidelines; Heuristic algorithms; Iterative algorithms; Iterative methods; Large-scale systems; Parallel processing;
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
DOI :
10.1109/ICEC.1994.349934