Towards a Better Understanding of Search Algorithms: A New Standard on Algorithm Design

Abstract

Along with the recent successes of machine learning to numerous applications comes the next challenge of general AI optimisation algorithms. In research, intelligent search algorithms are often designed case by case for specific problems. The rich knowledge of algorithm design is scattered and often discarded, leading to huge waste. In practice, practitioners are faced with the barrier of extensive expertise required to design effective algorithms. There is a lack of standard in algorithm design to support a deeper understanding on and retain coherent knowledge of good behaviours of search algorithms. Such standard is key to develop effective algorithms in both research and practice efficiently.

This talk introduces a new standard on algorithm design based on algorithmic components, and discuss some recent results on automated algorithm design for combinatorial optimisation problems. With the new standard, the new algorithms automatically evolved can be modelled in a consistent form of algorithmic components configured in the best way. These configurations thus can potentially provide a way to gain better understanding of algorithm behaviours. The talk will also present research on the search spaces of hyper-heuristics. As one of the general search algorithms, hyper-heuristics can also be modelled using the new standard, findings on which provide insights for future research on the new standard addressing various optimisation problems.

Biography

Dr Rong Qu is an Associated Professor at the University of Nottingham. She received her PhD in Computer Science from the University of Nottingham in 2002, and BSc in Computer Science and Its Applications from XiDian University in 1996. She has published more than 60 peer-refereed papers at international journals since 2000. Her research interests include the modelling and optimization algorithms in scheduling and optimization algorithms for logistics transport scheduling, personnel scheduling, network routing, portfolio optimization and timetabling problems by using hyper-heuristics, evolutionary algorithms, mathematical programming, constraint programming in operational research and artificial intelligence.

Dr Qu has been collaborating with a range of industries in transport logistic scheduling and workforce scheduling sponsored by EU, EPSRC, Royal Society and KTP, etc. Her research has underpinned intelligent algorithms in the spin-out companies EventMAP and StaffRoster®. She has been the program chair of symposium, workshops and special sessions at IEEE CEC and IEEE WCCI, and guest editor of a special issues at Journal of Scheduling and IEEE Computational Intelligence Magazine. She is the vice-chair of IEEE Task Committee on Evolutionary Computation and several task forces at IEEE Computational Intelligence Society, and elected as an IEEE Senior Member in 2012. She is an associated editor at IEEE Computational Intelligence Magazine since 2016.