Abstract
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). >
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