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ORCIDAutomated Design of Machine Learning and Search Algorithms 2021
- Nelishia Pillay, Rong Qu:
Automated Design of Machine Learning and Search Algorithms. Natural Computing Series, Springer 2021, ISBN 978-3-030-72068-1 - Rong Qu:
Recent Developments of Automated Machine Learning and Search Techniques. 1-9 - Hugo Jair Escalante:
Automated Machine Learning - A Brief Review at the End of the Early Years. 11-28 - Rong Qu:
A General Model for Automated Algorithm Design. 29-43 - Pietro S. Oliveto:
Rigorous Performance Analysis of Hyper-heuristics. 45-71 - Mauro Birattari, Antoine Ligot, Gianpiero Francesca:
AutoMoDe: A Modular Approach to the Automatic Off-Line Design and Fine-Tuning of Control Software for Robot Swarms. 73-90 - Christopher Stone, Emma Hart, Ben Paechter:
A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics. 91-107 - Mustafa Misir
:
Hyper-heuristics: Autonomous Problem Solvers. 109-131 - Hangyu Zhu, Yaochu Jin:
Toward Real-Time Federated Evolutionary Neural Architecture Search. 133-147 - Yi Mei, Mazhar Ansari Ardeh, Mengjie Zhang:
Knowledge Transfer in Genetic Programming Hyper-heuristics. 149-169 - Nelishia Pillay, Thambo Nyathi:
Automated Design of Classification Algorithms. 171-184 - Nelishia Pillay:
Automated Design (AutoDes): Current Trends and Future Research Directions. 185-187
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