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Handbook of Evolutionary Machine Learning

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  • © 2024

Overview

Editors:
  1. Wolfgang Banzhaf

    1. Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

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  2. Penousal Machado

    1. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

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  3. Mengjie Zhang

    1. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

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  • Explores various ways evolution can help improve current methods of machine learning
  • Presents real-world applications in medicine, robotics, science, finance, and other domains
  • Serves as an essential reference for those interested in evolutionary approaches to machine learning

Part of the book series: Genetic and Evolutionary Computation (GEVO)

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Table of contents (26 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Evolutionary Machine Learning Basics

    1. Front Matter

      Pages 1-1
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    2. Fundamentals of Evolutionary Machine Learning

      • Wolfgang Banzhaf, Penousal Machado
      Pages 3-28

    3. Evolutionary Supervised Machine Learning

      • Risto Miikkulainen
      Pages 29-57

    4. EML for Unsupervised Learning

      • Roberto Santana
      Pages 59-78

    5. Evolutionary Computation and the Reinforcement Learning Problem

      • Stephen Kelly, Jory Schossau
      Pages 79-118

  3. Evolutionary Computation as Machine Learning

    1. Front Matter

      Pages 119-119
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    2. Evolutionary Regression and Modelling

      • Qi Chen, Bing Xue, Will Browne, Mengjie Zhang
      Pages 121-149

    3. Evolutionary Clustering and Community Detection

      • Julia Handl, Mario Garza-Fabre, Adán José-García
      Pages 151-169

    4. Evolutionary Classification

      • Bach Nguyen, Bing Xue, Will Browne, Mengjie Zhang
      Pages 171-204

    5. Evolutionary Ensemble Learning

      • Malcolm I. Heywood
      Pages 205-243

  4. Evolution and Neural Networks

    1. Front Matter

      Pages 245-245
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    2. Evolutionary Neural Network Architecture Search

      • Zeqiong Lv, Xiaotian Song, Yuqi Feng, Yuwei Ou, Yanan Sun, Mengjie Zhang
      Pages 247-281

    3. Evolutionary Generative Models

      • João Correia, Francisco Baeta, Tiago Martins
      Pages 283-329

    4. Evolution Through Large Models

      • Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, Kenneth O. Stanley
      Pages 331-366

    5. Hardware-Aware Evolutionary Approaches to Deep Neural Networks

      • Lukas Sekanina, Vojtech Mrazek, Michal Pinos
      Pages 367-396

    6. Adversarial Evolutionary Learning with Distributed Spatial Coevolution

      • Jamal Toutouh, Erik Hemberg, Una-May O’Reilly
      Pages 397-435

  5. Evolutionary Computation for Machine Learning

    1. Front Matter

      Pages 437-437
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    2. Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)

      • Jason H. Moore, Pedro H. Ribeiro, Nicholas Matsumoto, Anil K. Saini
      Pages 439-455

    3. Evolutionary Model Validation—An Adversarial Robustness Perspective

      • Inês Valentim, Nuno Lourenço, Nuno Antunes
      Pages 457-485
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Keywords

About this book

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. 
This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.

Editors and Affiliations

  • Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

    Wolfgang Banzhaf

  • Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  • School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

About the editors

Wolfgang Banzhaf is a professor in the Department of Computer Science and Engineering at Michigan State University. He is the John R. Koza Endowed Chair in Genetic Programming and a member of the BEACON Center for the Study of Evolution in Action. His research interests include evolutionary computation and complex adaptive systems. Studies of self-organization and the field of Artificial Life are also of very much interest to him. 
 
Penousal Machado is an associate professor in the Department of Informatics at the University of Coimbra in Portugal, the coordinator of the Cognitive and Media Systems group of the Centre for Informatics and Systems of the University of Coimbra (CISUC), and the scientific director of the Computational Design and Visualization Lab of CISUC. His research interests include evolutionary computation, computational creativity, artificial intelligence, and information visualization.
 
Mengjie Zhang is a Professor of Computer Science, Head of the Evolutionary Computation and machine learning Research Group, and Director of Data Science and Artificial Intelligence, Victoria University of Wellington, New Zealand. His current research interests include artificial intelligence and machine learning, particularly genetic programming, image analysis, feature selection and reduction, job shop scheduling, and transfer learning. 

Bibliographic Information

  • Book Title: Handbook of Evolutionary Machine Learning

  • Editors: Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang

  • Series Title: Genetic and Evolutionary Computation

  • DOI: https://doi.org/10.1007/978-981-99-3814-8

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

  • Hardcover ISBN: 978-981-99-3813-1Published: 02 November 2023

  • Softcover ISBN: 978-981-99-3816-2Due: 03 December 2023

  • eBook ISBN: 978-981-99-3814-8Published: 01 November 2023

  • Series ISSN: 1932-0167

  • Series E-ISSN: 1932-0175

  • Edition Number: 1

  • Number of Pages: XVI, 768

  • Number of Illustrations: 54 b/w illustrations, 148 illustrations in colour

  • Topics: Artificial Intelligence, Machine Learning, Computational Intelligence, Evolutionary Biology

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