Elitism-based compact genetic algorithms
By Chang Wook Ahn, Rudrapatna S. Ramakrishna. IEEE Transactions on Evolutionary Computation, 2003
297 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/0ae32f809544eba28d17c96acd1253dd8686e0f8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ronald R. Yager
- Kagan Tumer
- Naif Alajlan
- Farid Melgani
- Yakoub Bazi
- Haikel Salem Alhichri
- Shauharda Khadka
- Mohamad Mahmoud Al Rahhal
In Computational Theory and Mathematics
- Ronald R. Yager
- Kagan Tumer
- Shauharda Khadka
- Harish Garg
- Zeshui Xu
- Peide Liu
- Enrique Herrera-Viedma
- Xiaolu Zhang
Citations in the corpus
- Evolution-Guided Policy Gradient in Reinforcement Learning (2018, background): “Additionally, the redundancy inherent in a population also promotes robustness and stable convergence properties particularly when combined with elitism [2].”
- Deep learning approach for active classification of electrocardiogram signals (2016, background): “Basically, these approaches start by training a global classifier on a large dataset and another local-classifier on the first few minutes from the test record labeled by an expert [5,15,41,32,34].”