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Genetic Programming

26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Gisele Pappa

    1. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

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  2. Mario Giacobini

    1. Università degli studi di Torino, Turin, Italy

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  3. Zdenek Vasicek

    1. Brno University of Technology, Brno, Czech Republic

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13986)

Included in the following conference series:

Conference proceedings info: EuroGP 2023.

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About this book

This book constitutes the refereed proceedings of the 26th European Conference on Genetic Programming, EuroGP 2023, held as part of EvoStar 2023, in Brno, Czech Republic, during April 12–14, 2023, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications. 

The 14 revised full papers and 8 short papers presented in this book were carefully reviewed and selected from 38 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms for both optimization and machine learning problems as well as exploring GP to address complex real-world problems. 

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Table of contents (22 papers)

  1. Front Matter

    Pages i-xi
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  2. Long Presentations

    1. Front Matter

      Pages 1-1
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    2. A Self-Adaptive Approach to Exploit Topological Properties of Different GAs’ Crossover Operators

      • José Ferreira, Mauro Castelli, Luca Manzoni, Gloria Pietropolli
      Pages 3-18

    3. A Genetic Programming Encoder for Increasing Autoencoder Interpretability

      • Finn Schofield, Luis Slyfield, Andrew Lensen
      Pages 19-35

    4. Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows

      • Julia Reuter, Hani Elmestikawy, Fabien Evrard, Sanaz Mostaghim, Berend van Wachem
      Pages 36-51

    5. Phenotype Search Trajectory Networks for Linear Genetic Programming

      • Ting Hu, Gabriela Ochoa, Wolfgang Banzhaf
      Pages 52-67

    6. GPAM: Genetic Programming with Associative Memory

      • Tadeas Juza, Lukas Sekanina
      Pages 68-83

    7. MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning

      • Hengzhe Zhang, Qi Chen, Alberto Tonda, Bing Xue, Wolfgang Banzhaf, Mengjie Zhang
      Pages 84-100

    8. Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming

      • David Wittenberg, Franz Rothlauf
      Pages 101-116

    9. Context Matters: Adaptive Mutation for Grammars

      • Pedro Carvalho, Jessica Mégane, Nuno Lourenço, Penousal Machado
      Pages 117-132

    10. A Boosting Approach to Constructing an Ensemble Stack

      • Zhilei Zhou, Ziyu Qiu, Brad Niblett, Andrew Johnston, Jeffrey Schwartzentruber, Nur Zincir-Heywood et al.
      Pages 133-148

    11. Adaptive Batch Size CGP: Improving Accuracy and Runtime for CGP Logic Optimization Flow

      • Bryan Martins Lima, Naiara Sachetti, Augusto Berndt, Cristina Meinhardt, Jonata Tyska Carvalho
      Pages 149-164

    12. Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning

      • Nicholas Matsumoto, Anil Kumar Saini, Pedro Ribeiro, Hyunjun Choi, Alena Orlenko, Leo-Pekka Lyytikäinen et al.
      Pages 165-181

    13. Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies

      • Christopher Crary, Wesley Piard, Greg Stitt, Caleb Bean, Benjamin Hicks
      Pages 182-197

    14. Memetic Semantic Genetic Programming for Symbolic Regression

      • Alessandro Leite, Marc Schoenauer
      Pages 198-212

    15. Grammatical Evolution with Code2vec

      • Michał Kowalczykiewicz, Piotr Lipiński
      Pages 213-224

  3. Short Presentations

    1. Front Matter

      Pages 225-225
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    2. Domain-Aware Feature Learning with Grammar-Guided Genetic Programming

      • Leon Ingelse, Alcides Fonseca
      Pages 227-243

    3. Genetic Improvement of LLVM Intermediate Representation

      • William B. Langdon, Afnan Al-Subaihin, Aymeric Blot, David Clark
      Pages 244-259

    4. Spatial Genetic Programming

      • Iliya Miralavy, Wolfgang Banzhaf
      Pages 260-275
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Other volumes

  1. Genetic Programming

Editors and Affiliations

  • Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Gisele Pappa

  • Università degli studi di Torino, Turin, Italy

    Mario Giacobini

  • Brno University of Technology, Brno, Czech Republic

    Zdenek Vasicek

Bibliographic Information

  • Book Title: Genetic Programming

  • Book Subtitle: 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings

  • Editors: Gisele Pappa, Mario Giacobini, Zdenek Vasicek

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-29573-7

  • Publisher: Springer Cham

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

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Softcover ISBN: 978-3-031-29572-0Published: 29 March 2023

  • eBook ISBN: 978-3-031-29573-7Published: 28 March 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 356

  • Number of Illustrations: 14 b/w illustrations, 102 illustrations in colour

  • Topics: Professional Computing, Complex Systems, Computer Communication Networks, Machine Learning, Natural Language Processing (NLP)

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