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Genetic Programming Theory and Practice XX

  • Book
  • © 2024

Overview

Editors:
  1. Stephan Winkler

    1. School of Information, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

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  2. Leonardo Trujillo

    1. Engineering Sciences Graduate Program, Tecnológico Nacional de México, IT de Tijuana, Tijuana, Mexico

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  3. Charles Ofria

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

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  4. Ting Hu

    1. School of Computing, Queen's University, Kingston, Canada

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  • Explores the intersection of GP and evolutionary computation, with machine learning and deep learning methods
  • Provides a unique combination of theoretical contributions and state-of-the-art real-world problem solving with GP
  • Discusses novel selection strategies, modular architectures, and unique fitness assignment strategies

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

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xv
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  2. TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning

    • Pedro Ribeiro, Anil Saini, Jay Moran, Nicholas Matsumoto, Hyunjun Choi, Miguel Hernandez et al.
    Pages 1-17

  3. Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology

    • Mario Hevia Fajardo, Per Kristian Lehre, Jamal Toutouh, Erik Hemberg, Una-May O’Reilly
    Pages 19-44

  4. Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming

    • Nathan Haut, Wolfgang Banzhaf, Bill Punch, Dirk Colbry
    Pages 45-64

  5. How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions

    • Wolfgang Banzhaf, Ting Hu, Gabriela Ochoa
    Pages 65-86

  6. The Impact of Step Limits on Generalization and Stability in Software Synthesis

    • Nicholas Freitag McPhee, Richard Lussier
    Pages 87-104

  7. Genetic Programming Techniques for Glucose Prediction in People with Diabetes

    • J. Ignacio Hidalgo, Jose Manuel Velasco, Daniel Parra, Oscar Garnica
    Pages 105-124

  8. Methods for Rich Phylogenetic Inference Over Distributed Sexual Populations

    • Matthew Andres Moreno
    Pages 125-141

  9. A Melting Pot of Evolution and Learning

    • Moshe Sipper, Achiya Elyasaf, Tomer Halperin, Zvika Haramaty, Raz Lapid, Eyal Segal et al.
    Pages 143-158

  10. Particularity

    • Lee Spector, Li Ding, Ryan Boldi
    Pages 159-176

  11. The OpenELM Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms

    • Herbie Bradley, Honglu Fan, Theodoros Galanos, Ryan Zhou, Daniel Scott, Joel Lehman
    Pages 177-201

  12. GP for Continuous Control: Teacher or Learner? The Case of Simulated Modular Soft Robots

    • Eric Medvet, Giorgia Nadizar
    Pages 203-224

  13. Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

    • Christian Haider, Fabricio Olivetti de Franca, Bogdan Burlacu, Florian Bachinger, Gabriel Kronberger, Michael Affenzeller
    Pages 225-240

  14. Phylogeny-Informed Fitness Estimation for Test-Based Parent Selection

    • Alexander Lalejini, Matthew Andres Moreno, Jose Guadalupe Hernandez, Emily Dolson
    Pages 241-261

  15. Origami: (un)folding the Abstraction of Recursion Schemes for Program Synthesis

    • Matheus Campos Fernandes, Fabricio Olivetti de Franca, Emilio Francesquini
    Pages 263-281

  16. Reachability Analysis for Lexicase Selection via Community Assembly Graphs

    • Emily Dolson, Alexander Lalejini
    Pages 283-301

  17. Let’s Evolve Intelligence, Not Solutions

    • Talib S. Hussain
    Pages 303-333

  18. Back Matter

    Pages 335-337
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Keywords

About this book

Genetic Programming Theory and Practice brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year’s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine, and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the- art in GP research.

Editors and Affiliations

  • School of Information, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  • Engineering Sciences Graduate Program, Tecnológico Nacional de México, IT de Tijuana, Tijuana, Mexico

    Leonardo Trujillo

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

    Charles Ofria

  • School of Computing, Queen's University, Kingston, Canada

    Ting Hu

Bibliographic Information

  • Book Title: Genetic Programming Theory and Practice XX

  • Editors: Stephan Winkler, Leonardo Trujillo, Charles Ofria, Ting Hu

  • Series Title: Genetic and Evolutionary Computation

  • DOI: https://doi.org/10.1007/978-981-99-8413-8

  • 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: 978-981-99-8412-1Published: 17 February 2024

  • Softcover ISBN: 978-981-99-8415-2Due: 20 March 2024

  • eBook ISBN: 978-981-99-8413-8Published: 17 February 2024

  • Series ISSN: 1932-0167

  • Series E-ISSN: 1932-0175

  • Edition Number: 1

  • Number of Pages: XV, 337

  • Number of Illustrations: 13 b/w illustrations, 94 illustrations in colour

  • Topics: Artificial Intelligence

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