PARADE: A Massively Parallel Differential Evolution Template for EASEA

Author:

Arabas Jarosław,Maitre Ogier,Collet Pierre

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. EASEA, https://lsiit.u-strasbg.fr/easea/index.php/EASEA_platform

2. Cabido, R., Duarte, A., Montemayor, A., Pantrigo, J.: Differential evolution for global optimization on gpu. In: Int. Conf. on Metaheuritic and Nature Inspired Computing (2010)

3. Gonzalez, S., Barriga, N.: Fully parallel differential evolution. In: GECCO Competition: GPUs for Genetc and Evolutionary Computation (2011)

4. Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms, pp. 75–102. Springer (2006)

5. Hansen, N.: Compilation of results on the 2005 cec benchmark function set. Tech. rep., Institute of Computational Science ETH Zurich (2006)

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1. Improving Evolutionary Algorithms in a Continuous Domain by Monitoring the Population Midpoint;IEEE Transactions on Evolutionary Computation;2017-10

2. Parallel Evolutionary Algorithms Performing Pairwise Comparisons;Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII;2015-01-17

3. Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms;International Journal of Parallel Programming;2013-11-08

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