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 optimisation
problems, such as multi-objective optimisation, dynamic optimisation,
and robust optimisation. Surrogate-assisted methods have also found
successful applications to 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. The Workshop on
Surrogate-Assisted Evolutionary Optimisation (SAEOpt) to be held at
GECCO 2025 in Malaga, Spain, aims to promote the research on
surrogate-assisted evolutionary optimisation, particularly the synergies
between evolutionary optimisation and machine learning. 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 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
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