Automated Design of Machine Learning and Search Algorithms

1. Front Matter

   Pages i-xviii

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2. Recent Developments of Automated Machine Learning and Search Techniques

   • Rong Qu

   Pages 1-9

3. Automated Machine Learning—A Brief Review at the End of the Early Years

   • Hugo Jair Escalante

   Pages 11-28

4. A General Model for Automated Algorithm Design

   • Rong Qu

   Pages 29-43

5. Rigorous Performance Analysis of Hyper-heuristics

   • Pietro S. Oliveto

   Pages 45-71

6. AutoMoDe: A Modular Approach to the Automatic Off-Line Design and Fine-Tuning of Control Software for Robot Swarms

   • Mauro Birattari, Antoine Ligot, Gianpiero Francesca

   Pages 73-90

7. A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics

   • Christopher Stone, Emma Hart, Ben Paechter

   Pages 91-107

8. Hyper-heuristics: Autonomous Problem Solvers

   • Mustafa Mısır

   Pages 109-131

9. Toward Real-Time Federated Evolutionary Neural Architecture Search

   • Hangyu Zhu, Yaochu Jin

   Pages 133-147

10. Knowledge Transfer in Genetic Programming Hyper-heuristics

    • Yi Mei, Mazhar Ansari Ardeh, Mengjie Zhang

    Pages 149-169

11. Automated Design of Classification Algorithms

    • Nelishia Pillay, Thambo Nyathi

    Pages 171-184

12. Automated Design (AutoDes): Current Trends and Future Research Directions

    • Nelishia Pillay

    Pages 185-187

This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection.

The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.

The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.

• Machine Learning
• Search
• Algorithm Design and Analysis
• AutoML
• Robot Swarms
• Heuristics
• Deep Learning
• Computational Intelligence
• Artificial Intelligence
• Evolutionary Computing

• Dept. of Computer Science, University of Pretoria, Pretoria, South Africa

  Nelishia Pillay

• School of Computer Science, University of Nottingham, Nottingham, UK

  Rong Qu

Nelishia Pillay is a professor at the University of Pretoria in South Africa. She holds the Multichoice Joint-Chair in Machine Learning and SARChI Chair in Artificial Intelligence. She is chair of the IEEE Technical Committee on Intelligent Systems Applications, IEEE Task Force on Hyper-Heuristics and the IEEE Task Force on Automated Algorithm Design, Configuration and Selection. Her research areas include hyper-heuristics, automated design of machine learning and search techniques, combinatorial optimization, genetic programming, genetic algorithms and deep learning. These are the focus areas of the NICOG (Nature-Inspired Computing Optimization) research group which she has established.

Rong Qu is an associate professor at the School of Computer Science, University of Nottingham. Her research interests include the modeling and optimisation of combinatorial optimisation problems in optimisation research and artificial intelligence. These include evolutionary algorithms, mathematical programming and metaheuristics integrated with machine learning to automate the design of intelligent algorithms. Dr. Qu is an associated editor at IEEE Computational Intelligence Magazine, IEEE Transactions on Evolutionary Computation, Journal of Operational Research Society and PeerJ Computer Science. She is a Senior IEEE Member since 2012 and the Vice-Chair of Evolutionary Computation Task Committee and Technical Committee on Intelligent Systems Applications at IEEE Computational Intelligence Society.

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