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Genetic Programming Theory and Practice XIV

  • Book
  • © 2018

Overview

Editors:
  1. Rick Riolo

    1. Center for the Study of Complex Sys, University of Michigan, Ann Arbor, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Bill Worzel

    1. Evolution Enterprises, Ann Arbor, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Brian Goldman

    1. Colorado State University, Fort Collins, USA

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  4. Bill Tozier

    1. Ann Arbor, USA

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  • Provides chapters describing cutting-edge work on the theory and applications of genetic programming (GP)
  • Offers large-scale, real-world applications of GP to a variety of problem domains
  • Written by leading international experts from both academia and industry

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

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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. Chapters in this volume include: 

  • Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression
  • Hybrid Structural and Behavioral Diversity Methods in GP
  • Multi-Population Competitive Coevolution for Anticipation of Tax Evasion
  • Evolving Artificial General Intelligence for Video Game Controllers
  • A Detailed Analysis of a PushGP Run
  • Linear Genomes for Structured Programs
  • Neutrality, Robustness, and Evolvability in GP
  • Local Search in GP
  • PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification
  • Relational Structure in Program Synthesis Problems with Analogical Reasoning
  • An Evolutionary Algorithm for Big Data Multi-Class Classification Problems
  • A Generic Framework for Building Dispersion Operators in the Semantic Space
  • Assisting Asset Model Development with Evolutionary Augmentation
  • Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool 

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.


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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xv
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  2. Similarity-Based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression

    • Stephan M. Winkler, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Philipp Fleck
    Pages 1-17

  3. An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming

    • Armand R. Burks, William F. Punch
    Pages 19-34

  4. Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion

    • Erik Hemberg, Jacob Rosen, Una-May O’Reilly
    Pages 35-51

  5. Evolving Artificial General Intelligence for Video Game Controllers

    • Itay Azaria, Achiya Elyasaf, Moshe Sipper
    Pages 53-63

  6. A Detailed Analysis of a PushGP Run

    • Nicholas Freitag McPhee, Mitchell D. Finzel, Maggie M. Casale, Thomas Helmuth, Lee Spector
    Pages 65-83

  7. Linear Genomes for Structured Programs

    • Thomas Helmuth, Lee Spector, Nicholas Freitag McPhee, Saul Shanabrook
    Pages 85-100

  8. Neutrality, Robustness, and Evolvability in Genetic Programming

    • Ting Hu, Wolfgang Banzhaf
    Pages 101-117

  9. Local Search is Underused in Genetic Programming

    • Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juárez-Smith, Pierrick Legrand, Sara Silva, Mauro Castelli et al.
    Pages 119-137

  10. PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification

    • Babak Hodjat, Hormoz Shahrzad, Risto Miikkulainen, Lawrence Murray, Chris Holmes
    Pages 139-148

  11. Discovering Relational Structure in Program Synthesis Problems with Analogical Reasoning

    • Jerry Swan, Krzysztof Krawiec
    Pages 149-164

  12. An Evolutionary Algorithm for Big Data Multi-Class Classification Problems

    • Michael F. Korns
    Pages 165-178

  13. A Generic Framework for Building Dispersion Operators in the Semantic Space

    • Luiz Otavio V. B. Oliveira, Fernando E. B. Otero, Gisele L. Pappa
    Pages 179-195

  14. Assisting Asset Model Development with Evolutionary Augmentation

    • Steven Gustafson, Arun Subramaniyan, Aisha Yousuf
    Pages 197-210

  15. Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

    • Randal S. Olson, Jason H. Moore
    Pages 211-223

  16. Back Matter

    Pages 225-227
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Reviews

“This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. … I strongly recommend this book for researchers in evolutionary computing and GP.” (S. V. Nagaraj, Computing Reviews, November 12, 2020)

Editors and Affiliations

  • Center for the Study of Complex Sys, University of Michigan, Ann Arbor, USA

    Rick Riolo

  • Evolution Enterprises, Ann Arbor, USA

    Bill Worzel

  • Colorado State University, Fort Collins, USA

    Brian Goldman

  • Ann Arbor, USA

    Bill Tozier

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