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Parallel Problem Solving from Nature – PPSN XVIII

18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part III

  • Conference proceedings
  • © 2024

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Editors:
  1. Michael Affenzeller

    1. University of Applied Sciences Upper Austria, Wels, Austria

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  2. Stephan M. Winkler

    1. University of Applied Sciences Upper Austria, Hagenberg, Austria

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  3. Anna V. Kononova

    1. Leiden University, Leiden, The Netherlands

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  4. Heike Trautmann

    1. University of Paderborn, Paderborn, Germany

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  5. Tea Tušar

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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  6. Penousal Machado

    1. University of Coimbra, Coimbra, Portugal

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  7. Thomas Bäck

    1. Leiden University, LEIDEN, The Netherlands

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15150)

Included in the following conference series:

Conference proceedings info: PPSN 2024.

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About this book

This multi-volume LNCS set, LNCS 15148-15151, constitutes the refereed proceedings of the 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024, held in Hagenberg, Austria, in September 2024.

The 101 full papers presented in these proceedings were carefully reviewed and selected from 294 submissions. The papers presented in these four volumes are organized in the following topical sections:

Part I: Combinatorial Optimization; Genetic Programming; Fitness Landscape Modeling and Analysis.

Part II: Benchmarking and Performance Measures; Automated Algorithm Selection and Configuration; Numerical Optimization; Bayesian- and Surrogate-Assisted Optimization.

Part III: Theoretical Aspects of Nature-Inspired Optimization; (Evolutionary) Machine Learning and Neuroevolution; Evolvable Hardware and Evolutionary Robotics.

Part IV: Multi-Objective Optimization; Real-World Applications.

Keywords

Table of contents (25 papers)

  1. Front Matter

    Pages i-xix
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  2. Theoretical Aspects of Nature-Inspired Optimization

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Self-adjusting Evolutionary Algorithms are Slow on a Class of Multimodal Landscapes

      • Johannes Lengler, Konstantin Sturm
      Pages 3-18

    3. Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem

      • Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton
      Pages 19-35

    4. Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints

      • Frank Neumann, Carsten Witt
      Pages 36-52

    5. Runtime Analysis of a Multi-valued Compact Genetic Algorithm on Generalized OneMax

      • Sumit Adak, Carsten Witt
      Pages 53-69

    6. Faster Optimization Through Genetic Drift

      • Cella Florescu, Marc Kaufmann, Johannes Lengler, Ulysse Schaller
      Pages 70-85

    7. Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms

      • Denis Antipov, Timo Kötzing, Aishwarya Radhakrishnan
      Pages 86-101

    8. How Population Diversity Influences the Efficiency of Crossover

      • Sacha Cerf, Johannes Lengler
      Pages 102-116

    9. Overcoming Binary Adversarial Optimisation with Competitive Coevolution

      • Per Kristian Lehre, Shishen Lin
      Pages 117-132

    10. Evolving Populations of Solved Subgraphs with Crossover and Constraint Repair

      • Jiwon Lee, Andrew M. Sutton
      Pages 133-148

    11. Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem

      • Jonathan Gadea Harder, Aneta Neumann, Frank Neumann
      Pages 149-165

    12. Archive-Based Single-Objective Evolutionary Algorithms for Submodular Optimization

      • Frank Neumann, Günter Rudolph
      Pages 166-180

    13. Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential

      • Denis Antipov, Aneta Neumann, Frank Neumann
      Pages 181-196

    14. Proven Runtime Guarantees for How the MOEA/D: Computes the Pareto Front from the Subproblem Solutions

      • Benjamin Doerr, Martin S. Krejca, Noé Weeks
      Pages 197-212

    15. Ranking Diversity Benefits Coevolutionary Algorithms on an Intransitive Game

      • Mario Alejandro Hevia Fajardo, Per Kristian Lehre
      Pages 213-229

    16. On the Equivalence Between Stochastic Tournament and Power-Law Ranking Selection and How to Implement Them Efficiently

      • Duc-Cuong Dang, Andre Opris, Dirk Sudholt
      Pages 230-245

    17. Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary Algorithms

      • Duc-Cuong Dang, Andre Opris, Dirk Sudholt
      Pages 246-263

    18. Runtime Analysis for State-of-the-Art Multi-objective Evolutionary Algorithms on the Subset Selection Problem

      • Renzhong Deng, Weijie Zheng, Mingfeng Li, Jie Liu, Benjamin Doerr
      Pages 264-279

    19. When Does the Time-Linkage Property Help Optimization by Evolutionary Algorithms?

      • Mingfeng Li, Weijie Zheng, Wen Xie, Ao Sun, Xin Yao
      Pages 280-294
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Other volumes

  1. Parallel Problem Solving from Nature – PPSN XVIII

  2. Parallel Problem Solving from Nature – PPSN XVIII

  3. Parallel Problem Solving from Nature – PPSN XVIII

  4. Parallel Problem Solving from Nature – PPSN XVIII

Editors and Affiliations

  • University of Applied Sciences Upper Austria, Wels, Austria

    Michael Affenzeller

  • University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan M. Winkler

  • Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  • University of Paderborn, Paderborn, Germany

    Heike Trautmann

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

  • University of Coimbra, Coimbra, Portugal

    Penousal Machado

  • Leiden University, LEIDEN, The Netherlands

    Thomas Bäck

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