In many real world optimisation problems evaluating the objective function(s) is computationally expensive. 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, and successful applications include aerodynamic design optimisation, structural design optimisation, data-driven optimisation, chip design, drug design, robotics and many more.
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):
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
Accepted papers will be presented orally (20 minutes) at the workshop and distributed in the workshop proceedings to all conference attendees. Authors should follow the format of the GECCO manuscript style; further details are available in the following link.
User Name : shaun
Posted 08-12-2016 on 17:43:48 AEDT