A New Migration Model For Distributed Genetic Algorithms

Genetic algorithms are heuristic search algorithm used in science, engineering and many other areas. They are powerful but slow because of their evolutionary nature that mimics the natural selection process. The quality of the solutions delivered depends on the population size, causing larger demand on processing power. Parallel and distributed processing techniques resolve this issue by allocating subpopulations to a number of processors that interact by exchanging parts of their populations through a migration process. Two schemes of migration are in use today; the island and the step-stone models. This paper presents a new topology independent scheme called the selective migration model. This scheme allows migration among demes only if the individuals meet certain criteria at both the source and the destination. Experiments show that this model improves the performance by offering faster convergence in large population setups and better solutions in time-constrained small population setups.