A surrogate-assisted selection scheme for genetic algorithms employing multi-layer neural networks
Author:
Affiliation:
1. Hokkaido University, Sapporo, Hokkaido, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3205651.3208787
Reference4 articles.
1. Yaochu Jin. Surrogate-assisted evolutionary computation:Recent advances and future challenges. 2011 Yaochu Jin. Surrogate-assisted evolutionary computation:Recent advances and future challenges. 2011
2. Mihn Nghia Le et.al. .Evolution by Adapting Surrogates.(2010) Mihn Nghia Le et.al. . Evolution by Adapting Surrogates. (2010)
3. Tanmoy Chatterjee Souvik Chakraborty Rajib Chowdhury. A Critical Review of Surrogate Assisted Robust Design Optimization.(2017) Tanmoy Chatterjee Souvik Chakraborty Rajib Chowdhury. A Critical Review of Surrogate Assisted Robust Design Optimization. (2017)
4. Yaochu Jin Bernhard Sendhoff. On Evolutionary Optimization with Approximate Fitness Function.(2000) Yaochu Jin Bernhard Sendhoff. On Evolutionary Optimization with Approximate Fitness Function. (2000)
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1. Neural-network assistance to calculate precise eigenvalue for fitness evaluation of real product design;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2019-07-13
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