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Parallel Problem Solving from Nature – PPSN XVII

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

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Günter Rudolph

    1. TU Dortmund, Dortmund, Germany

  2. Anna V. Kononova

    1. Leiden University, Leiden, The Netherlands

  3. Hernán Aguirre

    1. Shinshu University, Nagano, Japan

  4. Pascal Kerschke

    1. Technische Universität Dresden, Dresden, Germany

  5. Gabriela Ochoa

    1. University of Stirling, Stirling, UK

  6. Tea Tušar

    1. Jožef Stefan Institute, Ljubljana, Slovenia

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13399)

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 (43 papers)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  2. Genetic Programming

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Digging into Semantics: Where Do Search-Based Software Repair Methods Search?

      • Hammad Ahmad, Padriac Cashin, Stephanie Forrest, Westley Weimer
      Pages 3-18

    3. Gene-pool Optimal Mixing in Cartesian Genetic Programming

      • Joe Harrison, Tanja Alderliesten, Peter A. N. Bosman
      Pages 19-32

    4. Genetic Programming for Combining Directional Changes Indicators in International Stock Markets

      • Xinpeng Long, Michael Kampouridis, Panagiotis Kanellopoulos
      Pages 33-47

    5. Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

      • Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
      Pages 48-62

    6. Towards Discrete Phenotypic Recombination in Cartesian Genetic Programming

      • Roman Kalkreuth
      Pages 63-77

  3. Multi-Objective Optimization

    1. Front Matter

      Pages 79-79
      Download chapter PDF

    2. A General Architecture for Generating Interactive Decomposition-Based MOEAs

      • Giomara Lárraga, Kaisa Miettinen
      Pages 81-95

    3. An Exact Inverted Generational Distance for Continuous Pareto Front

      • Zihan Wang, Chunyun Xiao, Aimin Zhou
      Pages 96-109

    4. Direction Vector Selection for R2-Based Hypervolume Contribution Approximation

      • Tianye Shu, Ke Shang, Yang Nan, Hisao Ishibuchi
      Pages 110-123

    5. Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-objective Optimization?

      • Shuang Li, Ke Li, Wei Li
      Pages 124-137

    6. Dynamic Multi-modal Multi-objective Optimization: A Preliminary Study

      • Yiming Peng, Hisao Ishibuchi
      Pages 138-150

    7. Fair Feature Selection with a Lexicographic Multi-objective Genetic Algorithm

      • James Brookhouse, Alex Freitas
      Pages 151-163

    8. Greedy Decremental Quick Hypervolume Subset Selection Algorithms

      • Andrzej Jaszkiewicz, Piotr Zielniewicz
      Pages 164-178

    9. Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation

      • Damy M. F. Ha, Timo M. Deist, Peter A. N. Bosman
      Pages 179-192

    10. Identifying Stochastically Non-dominated Solutions Using Evolutionary Computation

      • Hemant Kumar Singh, Juergen Branke
      Pages 193-206

    11. Large-Scale Multi-objective Influence Maximisation with Network Downscaling

      • Elia Cunegatti, Giovanni Iacca, Doina Bucur
      Pages 207-220

    12. Multi-Objective Evolutionary Algorithm Based on the Linear Assignment Problem and the Hypervolume Approximation Using Polar Coordinates (MOEA-LAPCO)

      • Diana Cristina Valencia-Rodríguez, Carlos Artemio Coello Coello
      Pages 221-233

    13. New Solution Creation Operator in MOEA/D for Faster Convergence

      • Longcan Chen, Lie Meng Pang, Hisao Ishibuchi
      Pages 234-246
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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

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