SAEOpt 2017 - Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2017)

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 2017 in Berlin, Germany, 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):
Learning approaches for constructing surrogates
Model management in surrogate-assisted optimisation
Multi-level, multi-fidelity surrogates
Complexity and efficiency of surrogate-assisted methods
Surrogates-assisted evolutionary optimisation of computationally expensive problems
Data-driven optimisation
Model approximation in dynamic, robust and multi-modal optimisation
Model approximation in multi- and many-objective optimisation
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
Performance assessment and improvement techniques in surrogate-assisted evolutionary computation
We invite short papers of up to 8 pages 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 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