international conference on parallel problem solving from nature

18 th International Conference on

Parallel problem solving from nature ppsn 2024.

September 14 - 18, 2024 Hagenberg, Austria

Welcome to PPSN 2024!

The International Conference on Parallel Problem Solving From Nature is a biannual open forum fostering the study of natural models, iterative optimization heuristics search heuristics, machine learning, and other artificial intelligence approaches. We invite researchers and practitioners to present their work, ranging from rigorously derived mathematical results to carefully crafted empirical studies.

All information regarding the conference organization, call for papers/tutorials/workshops, submission, registration, venue, etc. will be provided and constantly updated on this website and on our social media channels. In case of any additional inquiries, please do not hesitate to contact us .

See you soon in beautiful Hagenberg, Austria!

Michael Affenzeller & Stephan Winkler PPSN 2024 Conference Chairs

Latest News

Keynote by Bernardino Romera-Paredes

February 15, 2024 Jan Zenisek --> General

We are very pleased to announce Bernardino Romera-Paredes (Google DeepMind) as our second keynoter for this year’s edition of PPSN! He is a former core team member of AlphaFold2 and AlphaTensor and now research scientist at Google DeepMind in London.

PPSN 2024 will take place from September 14-18, 2024 . Other important dates are: (deadline interpretation = end of the day, anywhere on earth)

• Paper submission due: April 5, 2024 Extension: April 18, 2024
• Paper acceptance notification: May 31, 2024
• [Closed] Tutorials/Workshop proposal submission due: February 9, 2024
• [Closed] Tutorials/Workshop acceptance notification: February 23, 2024
• Tutorials/Workshops: September 14–15, 2024
• Poster Sessions: September 16–18, 2024

PPSN 2024 will take place on the Campus of the University of Applied Sciences Upper Austria , located in the Softwarepark Hagenberg, Austria .

For more details regarding dates have a look at the pages for Calls and Program .

For more details regarding venue, travel, accomodation and tourism go to: Venue .

Questions?  Contact Us!

Fh-prof. pd di dr. michael affenzeller, fh-prof. pd di dr. stephan winkler, mag. michaela beneder.

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17th PPSN 2022: Dortmund, Germany

16th PPSN 2020: Leiden, The Netherlands

15th PPSN 2018: Coimbra, Portugal

14th PPSN 2016: Edinburgh, UK

13th PPSN 2014: Ljubljana, Slovenia

12th PPSN 2012: Taormina, Italy

11th ppsn 2010: kraków, poland.

10th PPSN 2008: Dortmund, Germany

9th ppsn 2006: reykjavik, iceland.

8th PPSN 2004: Birmingham, UK

7th ppsn 2002: granada, spain, 6th ppsn 2000: paris, france, 5th ppsn 1998: amsterdam, the netherlands, 4th ppsn 1996: berlin, germany, 3rd ppsn 1994: jerusalem, israel, 2nd ppsn 1992: brussels, belgium, 1st ppsn 1990: dortmund, germany.

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• Conference proceedings
• © 2020

Parallel Problem Solving from Nature – PPSN XVI

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

• Thomas Bäck   ORCID: https://orcid.org/0000-0001-6768-1478 0 ,
• Mike Preuss   ORCID: https://orcid.org/0000-0003-4681-1346 1 ,
• André Deutz   ORCID: https://orcid.org/0000-0002-9047-6533 2 ,
• Hao Wang   ORCID: https://orcid.org/0000-0002-4933-5181 3 ,
• Carola Doerr   ORCID: https://orcid.org/0000-0002-4981-3227 4 ,
• Michael Emmerich   ORCID: https://orcid.org/0000-0002-7342-2090 5 ,
• Heike Trautmann   ORCID: https://orcid.org/0000-0002-9788-8282 6

Leiden University, Leiden, The Netherlands

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Sorbonne University, Paris, France

University of münster, münster, germany.

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)

Conference series link(s): PPSN: International Conference on Parallel Problem Solving from Nature

122k Accesses

291 Citations

22 Altmetric

Conference proceedings info: PPSN 2020.

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Table of contents (50 papers)

Front matter, automated algorithm selection and configuration, evolving deep forest with automatic feature extraction for image classification using genetic programming.

• Ying Bi, Bing Xue, Mengjie Zhang

Fast Perturbative Algorithm Configurators

• George T. Hall, Pietro S. Oliveto, Dirk Sudholt

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

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

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

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

Bayesian- and Surrogate-Assisted Optimization

Multi-fidelity optimization approach under prior and posterior constraints and its application to compliance minimization.

• Youhei Akimoto, Naoki Sakamoto, Makoto Ohtani

Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling

• Marie Anastacio, Holger Hoos

Evolving Sampling Strategies for One-Shot Optimization Tasks

• Jakob Bossek, Carola Doerr, Pascal Kerschke, Aneta Neumann, Frank Neumann

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

• Guoxia Fu, Chaoli Sun, Ying Tan, Guochen Zhang, Yaochu Jin

Designing Air Flow with Surrogate-Assisted Phenotypic Niching

• Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck

Variance Reduction for Better Sampling in Continuous Domains

• Laurent Meunier, Carola Doerr, Jeremy Rapin, Olivier Teytaud

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

• Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, Carola Doerr

Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation

• Lauchlan Toal, Dirk V. Arnold

Benchmarking and Performance Measures

Proposal of a realistic many-objective test suite.

• Weiyu Chen, Hisao Ishibuchi, Ke Shang

Approximate Hypervolume Calculation with Guaranteed or Confidence Bounds

• A. Jaszkiewicz, R. Susmaga, P. Zielniewicz

Can Compact Optimisation Algorithms Be Structurally Biased?

• Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck

Other Volumes

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.

• genetic alogrithms
• evolutionary algorithms
• multi- and many objective optimization
• bayesian- and surrogate-assisted optimization
• combinatorial optimization
• genetic programming
• landscape analysis
• benchmarking
• performance measures
• automated algorithm selection
• reinforcement learning
• real-world applications
• adaptive algorithms
• artificial intelligence
• computer networks
• correlation analysis
• machine learning
• mathematics

Thomas Bäck, Mike Preuss, André Deutz, Michael Emmerich

Hao Wang, Carola Doerr

Heike Trautmann

Book Title : Parallel Problem Solving from Nature – PPSN XVI

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

Editors : Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann

Series Title : Lecture Notes in Computer Science

DOI : https://doi.org/10.1007/978-3-030-58112-1

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer Nature Switzerland AG 2020

Softcover ISBN : 978-3-030-58111-4 Published: 03 September 2020

eBook ISBN : 978-3-030-58112-1 Published: 02 September 2020

Series ISSN : 0302-9743

Series E-ISSN : 1611-3349

Edition Number : 1

Number of Pages : XXIX, 735

Number of Illustrations : 92 b/w illustrations, 169 illustrations in colour

Topics : Theory of Computation , Artificial Intelligence , Mathematics of Computing , Discrete Mathematics in Computer Science , Probability and Statistics in Computer Science , Information Systems and Communication Service

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