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OPTIMIZATION
MACHINE LEARNING
SURROGATES
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About |
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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. |
Call for Papers |
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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 2020 in Cancun, Mexico, 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:
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Summary |
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SAEOpt 2020 : Surrogate-Assisted Evolutionary Optimisation will take place in Cancun, Mexico. It’s a 5 days event starting on Jul 08, 2020 (Wednesday) and will be winded up on Jul 12, 2020 (Sunday). SAEOpt 2020 falls under the following areas: OPTIMIZATION, MACHINE LEARNING, SURROGATES, etc. Submissions for this Workshop can be made by Apr 03, 2020. Authors can expect the result of submission by Apr 17, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Apr 24, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the SAEOpt 2020
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Credits and Sources |
| [1] SAEOpt 2020 : Surrogate-Assisted Evolutionary Optimisation |
