Call for abstracts

We invite authors/participants to submit new ideas, positional statements, and reviews/summaries/comments/techniques on Bayesian and surrogate-assisted search and optimisation. Accepted abstracts will be asked to provide a 15 minute talk with 5 minutes for questions. All presentations will be in-person and submissions are understood to indicate the authors intention to attend in person when submitting.

We especially welcome submissions from early career researchers and PhD’s.

Submissions should be in the form of 1-page abstracts (1 page abstract PDF file).

Please send your submissions to George De Ath

Topics of interest

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 optimisation
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

NOTE - Submissions will not be published in the conference proceedings unless already accepted through the conference submission process.