Dear colleagues,
Are you interested in understanding how evolutionary algorithms and other randomized search heuristics work? Consider submitting and attending the FOGA 2025 conference in Leiden! Yes, FOGA is not just for theoreticians 😉
18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
FOGA XVIII, Aug 27 – 29, 2025, Leiden, The Netherlands
https://naco.liacs.nl/foga2025/
+ extra day for COST action ROAR-NET meeting on Aug 26: https://naco.liacs.nl/foga2025//roarnet.html
:: FOGA submission deadline: May 2, 2025
:: FOGA keynote speakers:
Joshua D. Knowles, Schlumberger, https://www.birmingham.ac.uk/staff/profiles/computer-science/honorary-staff/knowles-joshua
Stephanie Wehner, Delft University of Technology, The Netherlands, https://qutech.nl/person/stephanie-wehner/
Tobias Glasmachers, Ruhr-Universität Bochum, Germany, https://www.ini.rub.de/the_institute/people/tobias-glasmachers/
:: Further information: All relevant information can be found on the website: https://naco.liacs.nl/foga2025/
:: Note that FOGA is _not_ only focused on mathematical analysis of randomized search heuristics, but aims at covering the entire spectrum of work, ranging from rigorously derived mathematical results to carefully crafted empirical studies, as long the key contribution is in helping us understand why something is happening.
The FOGA series aims at advancing our understanding of the working principles behind evolutionary algorithms and related randomized search heuristics, such as local search algorithms, differential evolution, ant colony optimization, particle swarm optimization, artificial immune systems, simulated annealing, and other Monte Carlo methods for search and optimization. Connections to related areas, such as Bayesian optimization and direct search, are of interest as well. FOGA is the premier event to discuss advances on the theoretical foundations of these algorithms, tools needed to analyze them, and different aspects of comparing algorithms’ performance. Topics of interest include, but are not limited to:
Run time analysis
Mathematical tools suitable for the analysis of search heuristics
Fitness landscapes and problem difficulty
(On- and offline) configuration and selection of algorithms, heuristics, operators, and parameters
Stochastic and dynamic environments, noisy evaluations
Constrained optimization
Problem representation
Complexity theory for search heuristics
Multi-objective optimization
Benchmarking aspects, including performance measures, the selection of meaningful benchmark problems, and statistical aspects
Connection between randomized search and machine learning
Please address all questions concerning the CfP to the program chairs (Jonathan Fieldsend and Francisco Chicano) and all other questions to the general and local organization team (Thomas Bäck, Anna V. Kononova, and Elena Raponi)
Looking forward to seeing you in Leiden,
FOGA organizers