Test driving three 1995 genetic algorithms: New test functions and geometric matching

Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several diierent types of genetic algorithms with varying performance and search characteristics. In this paper we look at 3 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 solved using simple local search methods. In this paper, the 3 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 eld of line fragments. Local search is currently the best known method for solving general geometric matching problems.