HYPERGEN-a distributed genetic algorithm on a hypercube

The genetic algorithm is a robust search and optimization technique based on the principles of natural genetics and survival of the fittest. Genetic algorithms (GA) are a promising new approach to global optimization problems, and are applicable to a wide variety of problems. HYPERGEN was developed as a research tool for investigating parallel genetic algorithms applied to combinatorial optimization problems. It provides the user with a wide variety of options to test the particular problem at hand. In addition, HYPERGEN is modular enough for a user to insert routines of his own for special needs, or for doing further research studies on parallel GAs. HYPERGEN was used successfully to find new `best´ tours on three `standard´ TSP problems, and out-performed a parallel simulated annealing algorithm on various package placement problems. The authors found it fairly easy to fine tune the parameters that drive a parallel GA for near optimal performance (population size, migration rate, and migration interval)