Call for Papers: FOGA XVIII

The 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVIII) aims to advance 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.

FOGA 2025 will take place from August 27 – 29, 2025, in Leiden, The Netherlands, hosted by the Leiden Institute of Computer Science (LIACS). The conference will be preceded by an informal meeting of the ROAR-NET COST Action on Tuesday, August 26, which is open to everyone.

Topics of Interest

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 black-box optimization and machine learning

Submissions covering the entire spectrum of work, ranging from rigorously derived mathematical results to carefully crafted empirical studies, are invited.

Important Dates (Anywhere on Earth - AoE)

1. Submission Deadline: May 5, 2025 (extended)
2. Notification of Decision to Authors: June 27, 2025
3. Camera Ready Deadline: July 10, 2025
4. Early Registration Deadline: July 20, 2025
5. Conference Dates: August 27 – 29, 2025

Submission Guidelines

• Submission System: FOGA uses the "Linklings" submission management system. A separate account is required.
  • Submission Link: Linklings
• Paper Format: Papers should be submitted in PDF format, consistent with the alternate 2-column ACM SIG style file "sigconf".
• Page Limit: The maximum number of pages is 12, including references. Over-length papers will be rejected without review.
• Supplementary Material: Supplementary material is welcome but should not be essential to understand the paper.
• ACM Publications Policy: Authors must adhere to all ACM Publications Policies, including ACM's new Publications Policy on Research Involving Human Participants and Subjects. Alleged violations of this policy or any ACM Publications Policy will be investigated by ACM and may result in a full retraction of your paper, in addition to other potential penalties, as per ACM Publications Policy.
• ORCID ID: Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper.

Review Process

• The review process is double-blind. Authors must omit their names and affiliations from the paper and ensure their identity is not easily revealed. Refer to prior work in a neutral manner. Reviewers should not reveal their identities within the paper reviews.
• Selection criteria include originality of ideas, correctness, clarity, significance of results, and quality of presentation.
• The review process will not include a rebuttal.
• The decision of the Program Committee will be final and cannot be appealed.

Camera Ready Submission Requirements

• Formatting must comply with the ACM format requirements using the sigconf template. See the latest ACM Primary Article Template.
• Verify that your .tex document includes the following lines, using the precise DOI and ISBN numbers that you received back from ACM after completing the copyright process. Make sure that your PDF shows the correct conference information on the front page (you will receive the exact code after completing the ACM Copyright form):

  \acmDOI{10.1145/nnnnnnn.nnnnnnn} % To be updated after completing copyright process
  \acmISBN{978-x-xxxx-xxxx-x/YY/MM} % To be updated after completing copyright process
  \acmConference[FOGA '25]{Foundations of Genetic Algorithms XVIII}{August 27 -- 29 , 2025}{Leiden, The Netherlands}
  \acmYear{2025}
  \copyrightyear{2025}
  

• You MUST use Type 1/TrueType fonts for your submission. All fonts must be embedded. Type 3 fonts are NOT acceptable. Python's matplotlib uses Type 3 fonts by default. You need to configure matplotlib to use TrueType fonts with:

  import matplotlib
  matplotlib.rcParams['pdf.fonttype'] = 42
  matplotlib.rcParams['ps.fonttype'] = 42
  

• Distill/Create an ACM compliant PDF and upload your PDF file at the Linklings site.
• CCS Concepts and Keywords are mandatory to be included on the first page of your submission.

Keynote Speakers

• Joshua D. Knowles
  • Title: Answering Hamming
  • Abstract: The story goes that while working at Bell Labs in the 1950s, the mathematician and computer scientist Richard Hamming would ask colleagues, "what's the most important problem in your field?" … and then follow up with, "so, why aren't you working on it?" Both questions have many possible answers, even for just one person at one time, but they are certainly provocative, tough and uncomfortable. In the talk, I will reflect on my personal answers at various times, some answers for evolutionary computation (EC) and evolutionary multiobjective optimization (EMO) more broadly, and for adjacent fields to EC/EMO as well as for industrial research & innovation. My particular answers (or anyone's) are almost certainly not as important as the effort behind them to grapple with the questions.
  • Bio: Based in the UK, Joshua Knowles is a scientific advisor for the multinational energy technology company SLB, and is a former Professor of Natural Computation at the University of Birmingham where he is presently an honorary senior fellow. He is also an honorary professor in the decision sciences group of Alliance Manchester Business School at The University of Manchester. A central figure in evolutionary multiobjective optimization (EMO) since the late 90s, his work includes fundamental research on archiving with diversity, performance assessment, local search, hypervolume-as-selection, machine decision makers, heterogeneous objectives, and “multiobjectivization”. In 2004-5, he developed the influential multiobjective Bayesian optimization method, ParEGO, for expensive problems. More broadly, Josh is interested in and has published (joint work) on the evolution of evolvability, the evolution of cooperation, neutral evolution, and symbiogenesis (including Deep Optimization). Collaborating across disciplines, Josh has published applications of evolutionary and ML methods in premier journals in astrophysics, analytical chemistry, theoretical biology, bioinformatics, and operations research. In 2024, he was part of an international team that automated the design-engineering of drill bits for oilfield or geothermal drilling by EMO methods with deployment and testing in live operations. He is presently involved in pitching for investment in super-efficient heat exchanger technologies (for hyperscale data centers) partly designed by evolutionary methods.
• Stephanie Wehner
  • Delft University of Technology, The Netherlands
• Tobias Glasmachers
  • Title: Additive drift is all you need -- if you are an evolution strategy.
  • Abstract: Drift analysis is a great tool for proving that optimization algorithms work the way we think they do, and for analyzing them, potentially in great detail. In this talk I will discuss drift analysis for evolution strategies. These algorithms exhibit linear convergence on a wide range of problems, which corresponds to a linear decrease of the logarithmic distance of the best-so-far sample from the optimum, giving rise to simple additive drift. That behavior is enabled by online adaptation of the step size, which decays at the same rate as the distance to the optimum. Moreover, modern evolution strategies like CMA-ES adapt not only the step size, but rather the full covariance matrix of their sampling distribution. The mechanism enables convergence at a problem-independent rate that depends only on the dimension of the search space. The primary challenge of proving the convergence of CMA-ES lies in establishing the stability of the adaptation process, which was recently achieved by analyzing the invariant Markov chain that describes the parameter adaptation process. Yet, a drift-based analysis is still desirable because it can yield much more fine-grained results. For instance, it can provide details about the transient adaptation phase, which often takes up the lion's share of the time for solving the problem. To achieve this, we need a potential function that appropriately penalizes unsuitable parameter configurations, or more precisely, configurations the algorithm tends to move away from. Designing a potential function that captures the dynamics of covariance matrix adaptation is an ongoing challenge. I will present our recent research efforts towards this goal and emphasize why relatively simple additive drift offers a powerful framework for achieving it.

Organizers

• General Chair: Anna V. Kononova, Thomas Bäck
• Program Chair: Francisco Chicano, Jonathan Fieldsend
• Proceedings Chair: Niki van Stein
• Local Chair: Elena Raponi
• Publicity Chair: Carola Doerr

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