A Parallel Genetic Algorithm Framework for Cloud Computing Applications

Genetic Algorithms (GA) are a subclass of evolutionary algorithms that use the principle of evolution in order to search for solutions to optimization problems. Evolutionary algorithms are by their nature very good candidates for parallelization, and genetic algorithms do not make an exception. Moreover, researchers have stated that genetic algorithms with larger populations tend to obtain better solutions with faster convergence. These are the main reasons why they can benefit from a MapReduce implementation. However, research in this area is still young, and there are only a few approaches for adapting genetic algorithms to the MapReduce model. In this article we analyze the use of subpopulations for the GA MapReduce implementations. MapReduce naturally creates subpopulations, and if this characteristic is properly explored, we can find better solutions for genetic algorithm parallelization. In this context, we propose new models for two well know genetic algorithm implementations, namely island and neighborhood model. Our solutions are using the island model, with isolated subpopulations, and the neighborhood model, with overlapping subpopulations. We incorporate these solutions in a framework, that makes the development of Cloud applications using Genetic Algorithm easier.