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Handbook of evolutionary machine learning Wolfgang Banzhaf, Penousal Machado, Mengjie Zhang, editors.

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...

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Bibliographic Details
Online Access: Full Text (via Springer)
Other Authors: Banzhaf, Wolfgang, 1955-, Machado, Penousal, Zhang, Mengjie
Format: eBook
Language:English
Published: Singapore : Springer, 2024.
Series:Genetic and evolutionary computation series.
Subjects:
Machine learning.
Evolutionary computation.
Evolutionary computation
Machine learning
Table of Contents:
  • Intro
  • Preface
  • Acknowledgments
  • Contents
  • Contributors
  • Part I Evolutionary Machine Learning Basics
  • 1 Fundamentals of Evolutionary Machine Learning
  • 1.1 Introduction
  • 1.2 A Definition of Evolutionary Machine Learning
  • 1.3 A History of EML
  • 1.3.1 EC Applied to ML Methods
  • 1.3.2 EC Applied to ML Problems
  • 1.4 A Taxonomy of EML
  • 1.4.1 EC for ML Methods
  • 1.4.2 ML for EC Methods
  • 1.4.3 EC for ML Problems
  • 1.5 Discussion of Main Branches of the Field
  • 1.5.1 Neuroevolution
  • 1.5.2 Learning Algorithms
  • 1.5.3 Learning Results
  • 1.5.4 Ethical Aspects
  • 1.6 Open Problems
  • References
  • 2 Evolutionary Supervised Machine Learning
  • 2.1 Introduction
  • 2.2 Evolving General Neural Network Designs
  • 2.2.1 Compact Neural Networks
  • 2.2.2 Deep Networks
  • 2.3 Evolving Explainable Solutions
  • 2.3.1 Decision Trees
  • 2.3.2 Learning Classifier Systems
  • 2.3.3 Genetic Programming
  • 2.3.4 Rulesets
  • 2.4 Evolutionary Metalearning
  • 2.4.1 Neural Architecture Search
  • 2.4.2 Beyond Architecture Search
  • 2.5 Conclusion
  • References
  • 3 EML for Unsupervised Learning
  • 3.1 Introduction
  • 3.2 Main Concepts
  • 3.2.1 Data Preparation
  • 3.2.2 Outlier or Anomaly Detection
  • 3.2.3 Dimensionality Reduction
  • 3.2.4 Association Rule Mining
  • 3.3 EML for Data Preparation
  • 3.3.1 EML for Instance Selection
  • 3.3.2 EML for Feature Discretization
  • 3.3.3 EML for Imputation
  • 3.3.4 EML for Dimensionality Reduction
  • 3.3.5 EML for Outlier or Anomaly Detection
  • 3.3.6 EML for Association Rule Mining
  • 3.4 Conclusions
  • References
  • 4 Evolutionary Computation and the Reinforcement Learning Problem
  • 4.1 Introduction
  • 4.2 Machine Reinforcement Learning
  • 4.2.1 Reinforcement Learning Algorithms
  • 4.2.2 Evolutionary RL Algorithms
  • 4.3 Complex Evaluations
  • 4.3.1 Incremental Growth
  • 4.3.2 Dynamic Inference Complexity
  • 4.3.3 Sensor Space Selectivity
  • 4.3.4 Parallel Algorithms and Hardware Acceleration
  • 4.3.5 Discussion and Open Challenges
  • 4.4 Exploration and Temporal Credit Assignment Ambiguity
  • 4.4.1 Exploration
  • 4.4.2 Credit Assignment
  • 4.4.3 Discussion and Open Challenges
  • 4.5 Partial Observability in Space and Time
  • 4.5.1 Discussion and Open Challenges
  • 4.6 Non-stationary and Multi-Task Environments
  • 4.6.1 Discussion and Open Challenges
  • 4.7 Transformational Learning and Hierarchical Decomposition
  • 4.7.1 Transfer Learning
  • 4.7.2 Hierarchical Reinforcement Learning
  • 4.7.3 Discussion and Open Challenges
  • 4.8 Conclusion
  • References
  • Part II Evolutionary Computation as Machine Learning
  • 5 Evolutionary Regression and Modelling
  • 5.1 Introduction
  • 5.2 Evolutionary Computation for Regression: Fundamentals
  • 5.2.1 Evolutionary Computation for Learning Coefficients for Regression
  • 5.2.2 Genetic Programming for Symbolic Regression
  • 5.2.3 Learning Classifier Systems for Regression and Function Approximation

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