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Parallel Problem Solving from Nature – PPSN XVI

16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Thomas Bäck

    1. Leiden University, Leiden, The Netherlands

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  2. Mike Preuss

    1. Leiden University, Leiden, The Netherlands

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  3. André Deutz

    1. Leiden University, Leiden, The Netherlands

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  4. Hao Wang

    1. Sorbonne University, Paris, France

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  5. Carola Doerr

    1. Sorbonne University, Paris, France

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  6. Michael Emmerich

    1. Leiden University, Leiden, The Netherlands

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  7. Heike Trautmann

    1. University of Münster, Münster, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12269)

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

Included in the following conference series:

Conference proceedings info: PPSN 2020.

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Table of contents (50 papers)

  1. Front Matter

    Pages i-xxix
    Download chapter PDF

  2. Automated Algorithm Selection and Configuration

    1. Front Matter

      Pages 1-1
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    2. Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming

      • Ying Bi, Bing Xue, Mengjie Zhang
      Pages 3-18

    3. Fast Perturbative Algorithm Configurators

      • George T. Hall, Pietro S. Oliveto, Dirk Sudholt
      Pages 19-32

    4. Dominance, Indicator and Decomposition Based Search for Multi-objective QAP: Landscape Analysis and Automated Algorithm Selection

      • Arnaud Liefooghe, Sébastien Verel, Bilel Derbel, Hernan Aguirre, Kiyoshi Tanaka
      Pages 33-47

    5. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem

      • Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, Heike Trautmann
      Pages 48-64

    6. Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification

      • Sara Tari, Holger Hoos, Julie Jacques, Marie-Eléonore Kessaci, Laetitia Jourdan
      Pages 65-77

  3. Bayesian- and Surrogate-Assisted Optimization

    1. Front Matter

      Pages 79-79
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    2. Multi-fidelity Optimization Approach Under Prior and Posterior Constraints and Its Application to Compliance Minimization

      • Youhei Akimoto, Naoki Sakamoto, Makoto Ohtani
      Pages 81-94

    3. Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling

      • Marie Anastacio, Holger Hoos
      Pages 95-110

    4. Evolving Sampling Strategies for One-Shot Optimization Tasks

      • Jakob Bossek, Carola Doerr, Pascal Kerschke, Aneta Neumann, Frank Neumann
      Pages 111-124

    5. A Surrogate-Assisted Evolutionary Algorithm with Random Feature Selection for Large-Scale Expensive Problems

      • Guoxia Fu, Chaoli Sun, Ying Tan, Guochen Zhang, Yaochu Jin
      Pages 125-139

    6. Designing Air Flow with Surrogate-Assisted Phenotypic Niching

      • Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck
      Pages 140-153

    7. Variance Reduction for Better Sampling in Continuous Domains

      • Laurent Meunier, Carola Doerr, Jeremy Rapin, Olivier Teytaud
      Pages 154-168

    8. High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

      • Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, Carola Doerr
      Pages 169-183

    9. Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation

      • Lauchlan Toal, Dirk V. Arnold
      Pages 184-197

  4. Benchmarking and Performance Measures

    1. Front Matter

      Pages 199-199
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    2. Proposal of a Realistic Many-Objective Test Suite

      • Weiyu Chen, Hisao Ishibuchi, Ke Shang
      Pages 201-214

    3. Approximate Hypervolume Calculation with Guaranteed or Confidence Bounds

      • A. Jaszkiewicz, R. Susmaga, P. Zielniewicz
      Pages 215-228

    4. Can Compact Optimisation Algorithms Be Structurally Biased?

      • Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck
      Pages 229-242
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Other volumes

  1. Parallel Problem Solving from Nature – PPSN XVI

  2. Parallel Problem Solving from Nature – PPSN XVI

Keywords

About this book

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020.

The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.


Editors and Affiliations

  • Leiden University, Leiden, The Netherlands

    Thomas Bäck, Mike Preuss, André Deutz, Michael Emmerich

  • Sorbonne University, Paris, France

    Hao Wang, Carola Doerr

  • University of Münster, Münster, Germany

    Heike Trautmann

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