Abstract
In this paper, a GPGPU (general purpose graphics processing unit) compatible Archived based Stochastic Ranking Evolutionary Algorithm (G-ASREA) is proposed, that ranks the population with respect to an archive of non-dominated solutions. It reduces the complexity of the deterministic ranking operator from O(mn2) to O(man) and further speeds up ranking on GPU. Experiments compare G-ASREA with a CPU version of ASREA and NSGA-II on ZDT test functions for a wide range of population sizes. The results confirm the gain in ranking complexity by showing that on 10K individuals, G-ASREA ranking is ≈ x5000 faster than NSGA-II and ≈ x15 faster than ASREA. © 2010 Springer-Verlag.
Cite
CITATION STYLE
Sharma, D., & Collet, P. (2010). GPGPU-compatible archive based stochastic ranking evolutionary algorithm (G-ASREA) for multi-objective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6239 LNCS, pp. 111–120). https://doi.org/10.1007/978-3-642-15871-1_12
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