AUSTRALASIAN CONFERENCE ON ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE (ACALCI 2017)
31 January-2 February 2017, Melbourne, Australia
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Tutorial: Data-Driven Evolutionary Optimisation

Optimisation aims to find the best solution from numerous feasible ones, which is demanded in nearly every field. Evolutionary optimisation represents a family of optimisation techniques based on Darwinian principles, characterised by a population of candidate solutions evolved via nature-inspired operations to search for the optimum. Intrinsically, it belongs to a generate-and-test problem solver which will produce a large volume of “data” (e.g., candidate solutions and their related information) as search progresses. Such "data" may contain prolific information about the behaviours of optimisation methods and the properties of optimisation tasks.

In the past few decades, a lot of efforts had been made to enhance evolutionary optimisation techniques via exploiting (e.g., using data analytics techniques) the “data” generated in the course of search. However, modern optimisation problems, featured with the fast-growing scale, complexity and uncertainty, can seldom be tackled by simply hybridising evolutionary optimisation with some off-the-shelf data analytics tools, and therefore call for an in-depth investigation on how to leverage the “data” generated during search to facilitate optimisation.

This tutorial aims to introduce a unified perspective on evolutionary optimisation techniques that adopts data analytics as an indispensable component, describe how to identify and address various data analytics tasks in the search process, and discuss an emerging research trend which makes use of search experience gained by solving some problems to facilitate solving other problems via knowledge transfer. The audience is expected to get to know the fundamentals and recent developments in data-driven evolutionary optimisation, and be inspired to employ such techniques to deal with their encountered optimisation problems.


Speaker

Kai Qin
School of Science
RMIT University, Melbourne, Australia
Dr. Kai Qin received his BEng degree from Southeast University, China, in 2001, and his PhD from Nanyang Technological University, Singapore, in 2007. He is now a lecturer in the School of Science at RMIT University. His major research interests include evolutionary computation, machine learning, image processing, GPU computing, and services computing. He has published 70+ papers and received two best paper awards. Two of his co-authored papers are the 1st and 4th most cited papers (Web of Science) in IEEE Transactions on Evolutionary Computation over the last 10 years. He is currently chairing the IEEE Computational Intelligence Society task force on Collaborative Learning and Optimisation, promoting research on synergising machine learning and intelligent optimisation to resolve challenging real-world problems which involve learning and optimisation as indispensable and interwoven tasks.


Important Dates
Paper submission deadline: 10 September 2016
Decision notification: 17 October 2016
Camera ready submission: 7 November 2016
Conference dates: 31 January-2 February 2017
Tutorials: 3 February 2017 (at RMIT University)
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