Key points are not available for this paper at this time.
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.
Mouret et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: