A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for Computational and Systems Biology Applications

Abstract

Many computational and systems biology challenges, in particular those related to big data analysis, can be formulated as optimization problems and therefore can be addressed using heuristics. Beside the typical optimization problems, formulated with respect to a single target, the possibility of optimizing multiple objectives (MO) is rapidly becoming more appealing. In this context, MO Evolutionary Algorithms (MOEAs) are one of the most widely used classes of methods to solve MO optimization problems. However, these methods can be particularly demanding from the computational point of view and, therefore, effective parallel implementations are needed. This fact, together with the wide diffusion of powerful and low-cost general-purpose Graphics Processing Units, promoted the development of software tools that focus on the parallelization of one or more computational phases among the steps characterizing MOEAs. In this paper we present a fine-grained parallelization of the Fast Non-dominating Sorting Genetic Algorithm (NSGA-II) for the CUDA architecture. In particular, we will discuss how this solution can be exploited to solve multi-objective optimization task in the field of computational and systems biology.