Test driving three 1995 genetic algorithms: New test functions and geometric matching |
| |
Authors: | D Whitley R Beveridge C Graves K Mathias |
| |
Institution: | (1) Department of Computer Science, Colorado State University, 80523 Fort Collins, Colorado, USA |
| |
Abstract: | Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several different
types of genetic algorithms with varying performance and search characteristics. In this article we look at three genetic
algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that
most test problems in the genetic algorithm literature can be solved using simple local search methods. In this article, the
three algorithms are compared using new test problems that are not readily solved using simple local search methods. We then
compare a local search method to genetic algorithms for geometric matching and examine a hybrid algorithm that combines local
and genetic search. The geometric matching problem matches a model (e.g., a line drawing) to a subset of lines contained in
a field of line fragments. Local search is currently the best known method for solving general geometric matching problems. |
| |
Keywords: | genetic algorithms test suites search |
本文献已被 SpringerLink 等数据库收录! |