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© 2020

Genetic Programming

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

  • Ting Hu
  • Nuno Lourenço
  • Eric Medvet
  • Federico Divina
Conference proceedings EuroGP 2020

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

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 12101)

Table of contents

  1. Front Matter
    Pages i-x
  2. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado
    Pages 35-51
  3. Irene Azzali, Leonardo Vanneschi, Mario Giacobini
    Pages 52-67
  4. Oliver Krauss, William B. Langdon
    Pages 84-100
  5. Michael A. Lones
    Pages 101-117
  6. Luca Mariot, Stjepan Picek, Domagoj Jakobovic, Alberto Leporati
    Pages 118-134
  7. Patryk Orzechowski, Franciszek Magiera, Jason H. Moore
    Pages 135-150
  8. Nuno M. Rodrigues, João E. Batista, Sara Silva
    Pages 151-166
  9. Stefano Ruberto, Valerio Terragni, Jason H. Moore
    Pages 167-183
  10. Anil Kumar Saini, Lee Spector
    Pages 184-194
  11. Aliyu Sani Sambo, R. Muhammad Atif Azad, Yevgeniya Kovalchuk, Vivek Padmanaabhan Indramohan, Hanifa Shah
    Pages 195-210
  12. Dominik Sobania, Franz Rothlauf
    Pages 211-227
  13. Adane Tarekegn, Fulvio Ricceri, Giuseppe Costa, Elisa Ferracin, Mario Giacobini
    Pages 228-243
  14. Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, Sara Silva, Leonardo Trujillo
    Pages 244-261
  15. Back Matter
    Pages 295-295

About these proceedings

Introduction

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.

Keywords

computer programming computer science computer systems correlation analysis distributed computer systems distributed systems education evolutionary algorithms functional programming generic programming genetic algorithms genetic programming haskell internet linguistics machine learning mathematics object-oriented programming parallel processing systems program compilers

Editors and affiliations

  1. 1.Queen's UniversityKingstonCanada
  2. 2.University of CoimbraCoimbraPortugal
  3. 3.University of TriesteTriesteItaly
  4. 4.Pablo de Olavide UniversitySevilleSpain

Bibliographic information

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