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Genetic Programming Theory and Practice XVII

  • Book
  • © 2020

Overview

Editors:
  1. Wolfgang Banzhaf

    1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, USA

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  2. Erik Goodman

    1. BEACON Center, Michigan State University, East Lansing, USA

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  3. Leigh Sheneman

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

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  4. Leonardo Trujillo

    1. Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ IT, Tijuana, Mexico

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  5. Bill Worzel

    1. Evolution Enterprises, Ann Arbor, USA

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  • Provides contributions describing cutting-edge work on the theory and applications of genetic programming (GP)
  • Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as financial and insurance problems
  • Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies

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

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xxvi
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  2. Characterizing the Effects of Random Subsampling on Lexicase Selection

    • Austin J. Ferguson, Jose Guadalupe Hernandez, Daniel Junghans, Alexander Lalejini, Emily Dolson, Charles Ofria
    Pages 1-23

  3. It Is Time for New Perspectives on How to Fight Bloat in GP

    • Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez, Daniel Lanza, Wolfgang Banzhaf, Erik Goodman
    Pages 25-38

  4. Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives

    • Ivo Gonçalves, Marta Seca, Mauro Castelli
    Pages 39-62

  5. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?

    • Ting Hu
    Pages 63-77

  6. Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

    • Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
    Pages 79-99

  7. Temporal Memory Sharing in Visual Reinforcement Learning

    • Stephen Kelly, Wolfgang Banzhaf
    Pages 101-119

  8. The Evolution of Representations in Genetic Programming Trees

    • Douglas Kirkpatrick, Arend Hintze
    Pages 121-143

  9. How Competitive Is Genetic Programming in Business Data Science Applications?

    • Arthur Kordon, Theresa Kotanchek, Mark Kotanchek
    Pages 145-163

  10. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming

    • Anil Kumar Saini, Lee Spector
    Pages 165-180

  11. Evolutionary Computation and AI Safety

    • Joel Lehman
    Pages 181-200

  12. Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?

    • Miguel Nicolau, James McDermott
    Pages 201-225

  13. Hands-on Artificial Evolution Through Brain Programming

    • Gustavo Olague, Mariana Chan-Ley
    Pages 227-253

  14. Comparison of Linear Genome Representations for Software Synthesis

    • Edward Pantridge, Thomas Helmuth, Lee Spector
    Pages 255-274

  15. Enhanced Optimization with Composite Objectives and Novelty Pulsation

    • Hormoz Shahrzad, Babak Hodjat, Camille Dollé, Andrei Denissov, Simon Lau, Donn Goodhew et al.
    Pages 275-293

  16. New Pathways in Coevolutionary Computation

    • Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
    Pages 295-305

  17. 2019 Evolutionary Algorithms Review

    • Andrew N. Sloss, Steven Gustafson
    Pages 307-344

  18. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model

    • Robert J. Smith, Malcolm I. Heywood
    Pages 345-366

  19. Modelling Genetic Programming as a Simple Sampling Algorithm

    • David R. White, Benjamin Fowler, Wolfgang Banzhaf, Earl T. Barr
    Pages 367-381

  20. An Evolutionary System for Better Automatic Software Repair

    • Yuan Yuan, Wolfgang Banzhaf
    Pages 383-406
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Keywords

About this book

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP.  In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.



Editors and Affiliations

  • Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, USA

    Wolfgang Banzhaf

  • BEACON Center, Michigan State University, East Lansing, USA

    Erik Goodman

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

    Leigh Sheneman

  • Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ IT, Tijuana, Mexico

    Leonardo Trujillo

  • Evolution Enterprises, Ann Arbor, USA

    Bill Worzel

Bibliographic Information

  • Book Title: Genetic Programming Theory and Practice XVII

  • Editors: Wolfgang Banzhaf, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Bill Worzel

  • Series Title: Genetic and Evolutionary Computation

  • DOI: https://doi.org/10.1007/978-3-030-39958-0

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-39957-3Published: 08 May 2020

  • Softcover ISBN: 978-3-030-39960-3Published: 08 May 2021

  • eBook ISBN: 978-3-030-39958-0Published: 07 May 2020

  • Series ISSN: 1932-0167

  • Series E-ISSN: 1932-0175

  • Edition Number: 1

  • Number of Pages: XXVI, 409

  • Number of Illustrations: 30 b/w illustrations, 112 illustrations in colour

  • Topics: Artificial Intelligence, Computational Intelligence, Algorithm Analysis and Problem Complexity

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