Scalable parallel genetic algorithms

Genetic algorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, genetic algorithms exhibit high computational cost and degradation of the quality of the solutions because of the increased complexity. One of the most relevant research trends in genetic algorithms is the implementation of parallel genetic algorithms with the goal of obtaining quality of solutions efficiently. This paper first reviews the state-of-the-art in parallel genetic algorithms. Parallelization strategies and emerging implementations are reviewed and relevant results are discussed. Second, this paper discusses important issues regarding scalability of parallel genetic algorithms.