This paper introduces and benchmarks Sferes v2 , a C++ framework designed to help researchers in evolutionary computation to make their code run as fast as possible on a multi-core computer. It is based on three main concepts: (1) including multi-core optimizations from the start of the design process; (2) providing state-of-the art implementations of well-selected current evolutionary algorithms (EA), and especially multiobjective EAs; (3) being based on modern (template-based) C++ techniques to be both abstract and efficient. Benchmark results show that when a single core is used, running time of classic EAs included in Sferes v2 (NSGA-2 and CMA-ES) are of the same order of magnitude than specialized C code. When n cores are used, typical speed-ups range from 0.75n to 0.9n; however, parallelization efficiency critically depends on the time to evaluate the fitness function.
No takes yet. Share an insight, caveat, or question.
Mouret et al. (2010) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: