Scalable parallel genetic algorithms

Genetic algorithms, search algorithms based on the genetic processes observ ed 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 o f the solutions because of the increased complexity. One of the most releva nt research trends in genetic algorithms is the implementation of parallel genetic algorithms with the goal of obtaining quality of solutions efficien tly. This paper first reviews the state-of-the-art in parallel genetic algo rithms. Parallelization strategies and emerging implementations are reviewe d and relevant results are discussed. Second, this paper discusses importan t issues regarding scalability of parallel genetic algorithms.