High performance evolutionary computation with tensor-based acceleration
Author:
Affiliation:
1. AGH University of Science and Technology, Kraków, Poland
Funder
Polish Ministry of Education and Science
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3512290.3528753
Reference19 articles.
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3. A solution for the quadratic assignment problem (QAP) through a parallel genetic algorithm based grid on GPU
4. John Runwei Cheng and Mitsuo Gen. 2020. Parallel Genetic Algorithms with GPU Computing. In Industry 4.0 Tamás Bányai and Antonella Petrilloand Fabio De Felice (Eds.). IntechOpen Rijeka Chapter 6. John Runwei Cheng and Mitsuo Gen. 2020. Parallel Genetic Algorithms with GPU Computing. In Industry 4.0 Tamás Bányai and Antonella Petrilloand Fabio De Felice (Eds.). IntechOpen Rijeka Chapter 6.
5. Library for evolutionary algorithms in Python (LEAP)
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2. A High-Performance Tensorial Evolutionary Computation for Solving Spatial Optimization Problems;Communications in Computer and Information Science;2023-11-13
3. Gaggle: Genetic Algorithms on the GPU using PyTorch;Proceedings of the Companion Conference on Genetic and Evolutionary Computation;2023-07-15
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