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Parallel Problem Solving from Nature – PPSN XVII

17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Günter Rudolph

    1. TU Dortmund, Dortmund, Germany

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  2. Anna V. Kononova

    1. Leiden University, Leiden, The Netherlands

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  3. Hernán Aguirre

    1. Shinshu University, Nagano, Japan

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  4. Pascal Kerschke

    1. Technische Universität Dresden, Dresden, Germany

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  5. Gabriela Ochoa

    1. University of Stirling, Stirling, UK

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  6. Tea Tušar

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13398)

Included in the following conference series:

Conference proceedings info: PPSN 2022.

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About this book

This two-volume set LNCS 13398 and LNCS 13399 constitutes the refereed proceedings of the 17th International Conference on Parallel Problem Solving from Nature, PPSN 2022, held in Dortmund, Germany, in September 2022.

The  87 revised full papers were carefully reviewed and selected from numerous submissions. The conference presents a study of computing methods derived from natural models. Amorphous Computing, Artificial Life, Artificial Ant Systems, Artificial Immune Systems, Artificial Neural Networks, Cellular Automata, Evolutionary Computation, Swarm Computing, Self-Organizing Systems, Chemical Computation, Molecular Computation, Quantum Computation, Machine Learning, and Artificial Intelligence approaches using Natural Computing methods are just some of the topics covered in this field.

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Table of contents (42 papers)

  1. Front Matter

    Pages i-xxiv
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  2. Automated Algorithm Selection and Configuration

    1. Front Matter

      Pages 1-1
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    2. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods

      • Raphael Patrick Prager, Moritz Vinzent Seiler, Heike Trautmann, Pascal Kerschke
      Pages 3-17

    3. Improving Nevergrad’s Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration

      • Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, Olivier Teytaud et al.
      Pages 18-31

    4. Non-elitist Selection Can Improve the Performance of Irace

      • Furong Ye, Diederick Vermetten, Carola Doerr, Thomas Bäck
      Pages 32-45

    5. Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features

      • Ana Kostovska, Anja Jankovic, Diederick Vermetten, Jacob de Nobel, Hao Wang, Tome Eftimov et al.
      Pages 46-60

  3. Bayesian- and Surrogate-Assisted Optimization

    1. Front Matter

      Pages 61-61
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    2. A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization

      • Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck
      Pages 63-75

    3. Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary Optimization

      • Amir Abbasnejad, Dirk V. Arnold
      Pages 76-89

    4. Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature

      • Alma Rahat, Tinkle Chugh, Jonathan Fieldsend, Richard Allmendinger, Kaisa Miettinen
      Pages 90-103

    5. Finding Knees in Bayesian Multi-objective Optimization

      • Arash Heidari, Jixiang Qing, Sebastian Rojas Gonzalez, Jürgen Branke, Tom Dhaene, Ivo Couckuyt
      Pages 104-117

    6. High Dimensional Bayesian Optimization with Kernel Principal Component Analysis

      • Kirill Antonov, Elena Raponi, Hao Wang, Carola Doerr
      Pages 118-131

    7. Single Interaction Multi-Objective Bayesian Optimization

      • Juan Ungredda, Juergen Branke, Mariapia Marchi, Teresa Montrone
      Pages 132-145

    8. Surrogate-Assisted LSHADE Algorithm Utilizing Recursive Least Squares Filter

      • Mateusz Zaborski, Jacek Mańdziuk
      Pages 146-159

    9. Towards Efficient Multiobjective Hyperparameter Optimization: A Multiobjective Multi-fidelity Bayesian Optimization and Hyperband Algorithm

      • Zefeng Chen, Yuren Zhou, Zhengxin Huang, Xiaoyun Xia
      Pages 160-174

  4. Benchmarking and Performance Measures

    1. Front Matter

      Pages 175-175
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    2. A Continuous Optimisation Benchmark Suite from Neural Network Regression

      • Katherine M. Malan, Christopher W. Cleghorn
      Pages 177-191

    3. BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems

      • Jonathan Heins, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob Bossek, Heike Trautmann
      Pages 192-206

    4. Evolutionary Approaches to Improving the Layouts of Instance-Spaces

      • Kevin Sim, Emma Hart
      Pages 207-219

  5. Combinatorial Optimization

    1. Front Matter

      Pages 221-221
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Other volumes

  1. Parallel Problem Solving from Nature – PPSN XVII

  2. Parallel Problem Solving from Nature – PPSN XVII

Editors and Affiliations

  • TU Dortmund, Dortmund, Germany

    Günter Rudolph

  • Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  • Shinshu University, Nagano, Japan

    Hernán Aguirre

  • Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  • University of Stirling, Stirling, UK

    Gabriela Ochoa

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

Bibliographic Information

  • Book Title: Parallel Problem Solving from Nature – PPSN XVII

  • Book Subtitle: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I

  • Editors: Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, Tea Tušar

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-14714-2

  • 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 2022

  • Softcover ISBN: 978-3-031-14713-5Published: 15 August 2022

  • eBook ISBN: 978-3-031-14714-2Published: 13 August 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXIV, 619

  • Number of Illustrations: 13 b/w illustrations, 189 illustrations in colour

  • Topics: Theory of Computation, Computer Applications, Artificial Intelligence, Machine Learning, Mathematics of Computing

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