Pluggable Parallelization of Evolutionary Algorithms Applied to the Optimization of Biological Processes

Current wide availability of multicore systems requires tools that can help scientists to smoothly update their applications to take advantage of the parallel processing capabilities of these systems. In this paper, we present an experience with aspect-oriented programming (AOP) techniques to perform this move. We describe the parallelization of a Java library that implements algorithms from the Evolutionary Computation field (JECoLi), applied to two case studies in Bioinformatics, namely the optimization of feeding profiles in fed-batch fermentations and in silico strain optimization in Metabolic Engineering. AOP allowed us to enable the library to take advantage of multicore systems with minimal impact on the original code and to simultaneously develop the parallelization and the original library. Moreover, we developed modules that extend the library´s behavior for a better usage of multicore resources. Performance results show that this approach boosts performance, does not compromise the quality of the final solutions and enables a more loosely coupled development.