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Genetic Programming

23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Ting Hu

    1. Queen's University, Kingston, Canada

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  2. Nuno Lourenço

    1. University of Coimbra, Coimbra, Portugal

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  3. Eric Medvet

    1. University of Trieste, Trieste, Italy

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  4. Federico Divina

    1. Pablo de Olavide University, Seville, Spain

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12101)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: EuroGP 2020.

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Table of contents (18 papers)

  1. Front Matter

    Pages i-x
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  2. Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data

    • Baligh Al-Helali, Qi Chen, Bing Xue, Mengjie Zhang
    Pages 1-17

  3. Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

    • Muhammad Sheraz Anjum, Conor Ryan
    Pages 18-34

  4. Incremental Evolution and Development of Deep Artificial Neural Networks

    • Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado
    Pages 35-51

  5. Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

    • Irene Azzali, Leonardo Vanneschi, Mario Giacobini
    Pages 52-67

  6. Comparing Genetic Programming Approaches for Non-functional Genetic Improvement

    • Aymeric Blot, Justyna Petke
    Pages 68-83

  7. Automatically Evolving Lookup Tables for Function Approximation

    • Oliver Krauss, William B. Langdon
    Pages 84-100

  8. Optimising Optimisers with Push GP

    • Michael A. Lones
    Pages 101-117

  9. An Evolutionary View on Reversible Shift-Invariant Transformations

    • Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Alberto Leporati
    Pages 118-134

  10. Benchmarking Manifold Learning Methods on a Large Collection of Datasets

    • Patryk Orzechowski, Franciszek Magiera, Jason H. Moore
    Pages 135-150

  11. Ensemble Genetic Programming

    • Nuno M. Rodrigues, João E. Batista, Sara Silva
    Pages 151-166

  12. SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

    • Stefano Ruberto, Valerio Terragni, Jason H. Moore
    Pages 167-183

  13. Effect of Parent Selection Methods on Modularity

    • Anil Kumar Saini, Lee Spector
    Pages 184-194

  14. Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models

    • Aliyu Sani Sambo, R. Muhammad Atif Azad, Yevgeniya Kovalchuk, Vivek Padmanaabhan Indramohan, Hanifa Shah
    Pages 195-210

  15. Challenges of Program Synthesis with Grammatical Evolution

    • Dominik Sobania, Franz Rothlauf
    Pages 211-227

  16. Detection of Frailty Using Genetic Programming

    • Adane Tarekegn, Fulvio Ricceri, Giuseppe Costa, Elisa Ferracin, Mario Giacobini
    Pages 228-243

  17. Is k Nearest Neighbours Regression Better Than GP?

    • Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, Sara Silva, Leonardo Trujillo
    Pages 244-261

  18. Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

    • Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
    Pages 262-278

  19. Classification of Autism Genes Using Network Science and Linear Genetic Programming

    • Yu Zhang, Yuanzhu Chen, Ting Hu
    Pages 279-294

  20. Back Matter

    Pages 295-295
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Other volumes

  1. Genetic Programming

Keywords

About this book

This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications.
The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.

Editors and Affiliations

  • Queen's University, Kingston, Canada

    Ting Hu

  • University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  • University of Trieste, Trieste, Italy

    Eric Medvet

  • Pablo de Olavide University, Seville, Spain

    Federico Divina

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