Handbook of Evolutionary Machine Learning / edited by Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang.

Format:

Book

Contributor:

Banzhaf, Wolfgang, 1955- editor.

Machado, Penousal, editor.

Zhang, Mengjie, editor.

Series:

Genetic and Evolutionary Computation, 1932-0175

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Computational intelligence.

Evolution (Biology).

Artificial Intelligence.

Machine Learning.

Computational Intelligence.

Evolutionary Biology.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Computational Intelligence.

Evolutionary Biology.

Physical Description:

1 online resource (764 pages) : illustrations.

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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.

Contents:

Part 1. Overview chapters

Chapter 1. EML Fundamentals

Chapter 2. EML in Supervised Learning

Chapter 3. EML in Unsupervised Learning

Chapter 4. EML in Reinforcement Learning

Part 2. Evolutionary Computation as Machine Learning

Chapter 5. Evolutionary Clustering

Chapter 6. Evolutionary Classification and Regression

Chapter 7. Evolutionary Ensemble Learning

Chapter 8. Evolutionary Deep Learning

Chapter 9. Evolutionary Generative Models

Part 3. Evolutionary Computation for Machine Learning

Chapter 10. Evolutionary Data Preparation

Chapter 11. Evolutionary Feature Engineering and Selection

Chapter 12. Evolutionary Model Parametrization

Chapter 13. Evolutionary Model Design

Chapter 14. Evolutionary Model Validation

Part 4. Applications

Chapter 15. EML in Medicine

Chapter 16. EML in Robotics

Chapter 17. EML in Finance

Chapter 18. EML in Science

Chapter 19. EML in Environmental Science

Chapter 20. EML in the Arts.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Banzhaf, Wolfgang Handbook of Evolutionary Machine Learning

ISBN:

9789819938148

9819938147

OCLC:

1407316617