CFP: Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt@GECCO 2024)
Alma Rahat
In many real-world optimisation problems, evaluating the objective function(s) is expensive, perhaps requiring days of computation for a single evaluation. Surrogate-assisted optimisation attempts to alleviate this problem by employing computationally cheap 'surrogate' models to estimate the objective function(s) or the ranking relationships of the candidate solutions.
Surrogate-assisted approaches have been widely used across the field of evolutionary optimisation, including continuous and discrete variable problems, although little work has been done on combinatorial problems. Surrogates have been employed in solving a variety of optimization problems, such as multi-objective optimisation, dynamic optimisation, and robust optimisation. Surrogate-assisted methods have also found successful applications in aerodynamic design optimisation, structural design optimisation, data-driven optimisation, chip design, drug design, robotics, and many more. Most interestingly, the need for on-line learning of the surrogates has led to a fruitful crossover between the machine learning and evolutionary optimisation communities, where advanced learning techniques such as ensemble learning, active learning, semi-supervised learning and transfer learning have been employed in surrogate construction.
Despite recent successes in using surrogate-assisted evolutionary optimisation, there remain many challenges. This workshop aims to promote the research on surrogate assisted evolutionary optimization including the synergies between evolutionary optimisation and learning. Thus, this workshop will be of interest to a wide range of GECCO participants. Particular topics of interest include (but are not limited to):
* Bayesian optimisation
* Advanced machine learning techniques for constructing surrogates
* Model management in surrogate-assisted optimisation
* Multi-level, multi-fidelity surrogates
* Complexity and efficiency of surrogate-assisted methods
* Small and big data-driven evolutionary optimization
* Model approximation in dynamic, robust, and multi-modal optimisation
* Model approximation in multi- and many-objective optimisation
* Surrogate-assisted evolutionary optimisation of high-dimensional problems
* Comparison of different modelling methods in surrogate construction
* Surrogate-assisted identification of the feasible region
* Comparison of evolutionary and non-evolutionary approaches with surrogate models
* Test problems for surrogate-assisted evolutionary optimisation
* Performance improvement techniques in surrogate-assisted evolutionary computation
* Performance assessment of surrogate-assisted evolutionary algorithms
We invite short papers of up to 8 pages (excluding references) presenting novel developments in one or more of these areas, or other areas relevant to surrogate-assisted evolutionary optimisation. We welcome position papers of up to 2 pages (including references) showcasing exciting exploratory and preliminary results.
We also welcome proposals for short demonstrations or presentations (5-10 minutes) on the following topics:
- Surrogate-assisted optimisation in real world
- Contemporary test problems in surrogate-assisted optimisation
- Other relevant accepted GECCO papers or recent journal papers
** Important Dates **
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Submission opening: February 12, 2024
Submission deadline: April 8, 2024
Notification: May 3, 2024
Camera-ready: May 10, 2024
Author's mandatory registration: date to be confirmed
Conference date: 14-18 July, 2024.
** Submission **
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Accepted papers will be presented orally (20 minutes) at the workshop and distributed in the workshop proceedings to all conference attendees. The authors should follow the format of the GECCO manuscript style; further details are available in the following link.
https://gecco-2024.sigevo.org/Paper-Submission-Instructions
Manuscripts should not exceed eight pages for regular submission and two pages for position papers. For proposals of short demonstrations or presentations (5-10 minutes), a half-page abstract should be submitted. This year all submissions will be handled through the standard GECCO submission site: https://ssl.linklings.net/conferences/gecco/
Please note that acceptance to the workshop will be based on a double-blind peer review of the submitted papers.
For more information, visit: www.saeopt.bitbucket.io
Please feel free to email me if you have any queries.
Best,
Alma.
On behalf of the organisers of SAEOpt:
Dr Alma Rahat (Swansea)
Prof. Richard Everson (Exeter)
Prof. Jonathan Fieldsend (Exeter)
Prof. Handing Wang (Xidian)
Prof. Yaochu Jin (Westlake)
Dr Tinkle Chugh (Exeter)
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Dr Alma Rahat
Associate Professor of Data Science
EDI Lead, School of Mathematics and Computer Science
Turing Fellow, and Academic Liaison for Swansea University, The Alan Turing Institute
Coordinator for MSc in Data Science Programme
Department of Computer Science | Adran Gyfrifiadureg,
Swansea University | Prifysgol Abertawe,
United Kingdom.
Office: 112, Computational Foundry, Bay Campus.
Tel: +44 (0) 1792 205678 (ext: 8621)
Web: www.swansea.ac.uk/staff/science/compsci/rahat-a-a-m/
Virtual office link: https://swanseauniversity.zoom.us/my/a.a.m.rahat
Pronouns: he/him/his
Title: Dr