Towards More Effective Computational Search
Lead Research Organisation:
University of Nottingham
Department Name: School of Computer Science
Abstract
The ASAP group has set the international research agenda in exploring the development of computational systems that can automatically build decision support systems. The group addresses a broad range of scientifically challenging problems, many of which are drawn from the real world where the complexities of the problem have not been abstracted away in order to make the problem easier to model/solve.The group's key research goals include:- Automating the Heuristic Design Process: We lead the international community in hyper-heuristics (heuristics to choose heuristics) research, with the aim being to investigate the extent to which we can replace human decision making by computer algorithms.- Closing the gap between industrial and real world issues and academic practice: We aim to explore dynamic and complex computational modeling and intelligent decision support within the context of real world problems such as aircraft scheduling, timetabling, manufacturing, bioinformatics, production scheduling and healthcare rostering. ASAP aims to establish new decision support methodologies that explore the use of automated search methodologies and the complexity that they are able to handle.- Closing the gap between theoretical understanding and the construction of search methodologies: We aim to theoretically analyse complex real world scenarios with a view to deepening our understanding of search methodology development. The state of the art in theoretical study in this field tends to deal with models that are too simple to be placed into real world practice. We aim to study the theory of real world applications.Our core research on modeling and search methodologies has redefined the inter-disciplinary interface between Computer Science and Operational Research, while our grounding in diverse applications involves dialogue with many other disciplines spanning biomedical science (new computational methodologies in bioinformatics, systems and synthetic biology as well as in nanoscience) through to the built environment (search methodologies for office space allocation). In this renewal proposal to our current Platform award, we are requesting support for 132 months of research assistant funding (at varying levels of seniority), over a five year period. This would enable us to conduct (and continue) a programme of transformative and innovative research that is not only high risk and high return, but which also has a clear multi-disciplinary and industrial focus.A Platform award would enable the ASAP group to retain key personnel at the interface of Computer Science and Operational Research. The potential benefits in providing the grounding for tomorrow's decision support systems could be far reaching in laying the foundations for more efficient, effective, cheaper, easier-to-implement and easier-to-use systems across many industries and sectors.
Organisations
Publications
Lwin K.
(2013)
Multi-objective scatter search with external archive for portfolio optimization
in IJCCI 2013 - Proceedings of the 5th International Joint Conference on Computational Intelligence
Maashi M
(2015)
Choice function based hyper-heuristics for multi-objective optimization
in Applied Soft Computing
Maashi M
(2014)
A multi-objective hyper-heuristic based on choice function
in Expert Systems with Applications
Martin S
(2013)
Cooperative search for fair nurse rosters
in Expert Systems with Applications
Mesgarpour M
(2013)
Activities of Transport Telematics
Ouelhadj D
(2013)
Fairness and cooperation in nurse rostering
Ozcan E
(2013)
Memetic algorithms for Cross-domain Heuristic Search
Phillips T
(2014)
The effects of extra-somatic weapons on the evolution of human cooperation towards non-kin.
in PloS one
Pillay N
(2018)
Hyper-Heuristics: Theory and Applications
Qu R
(2012)
Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems
in Journal of Heuristics
Sabar N
(2013)
Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
in IEEE Transactions on Evolutionary Computation
Sabar N
(2015)
Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems
in IEEE Transactions on Evolutionary Computation
Sabar NR
(2015)
A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems.
in IEEE transactions on cybernetics
Shambour M
(2013)
Neural Information Processing
Soghier A
(2013)
Adaptive selection of heuristics for assigning time slots and rooms in exam timetables
in Applied Intelligence
Swan J
(2013)
Searching the Hyper-heuristic Design Space
in Cognitive Computation
Turk S
(2014)
Interval type-2 fuzzy sets in supplier selection
Uludag G
(2013)
A hybrid multi-population framework for dynamic environments combining online and offline learning
in Soft Computing
Xing H
(2017)
A path-oriented encoding evolutionary algorithm for network coding resource minimization
in Journal of the Operational Research Society
Xing H
(2013)
A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems
in Information Sciences
Xing H
(2014)
On minimizing coding operations in network coding based multicast: an evolutionary algorithm
in Applied Intelligence
Xu Y
(2013)
A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems
in Annals of Operations Research
Zhou S
(2011)
Alpha-Level Aggregation: A Practical Approach to Type-1 OWA Operation for Aggregating Uncertain Information with Applications to Breast Cancer Treatments
in IEEE Transactions on Knowledge and Data Engineering
Özcan E
(2013)
Bidirectional best-fit heuristic considering compound placement for two dimensional orthogonal rectangular strip packing
in Expert Systems with Applications
Özcan E
(2013)
Special issue on maintenance scheduling: theory and applications
in Journal of Scheduling
| Description | The optimisation research conducted on this grant have informed a number of further activities. In particular the research group has applied for and received funding to carry out optimisation R&D in the logistics field. We received significant funding from Innovate UK to develop optimisation systems to inform driver behaviour. We have also received funding for Knowledge Transfer Partnerships in optimisation which was aided by the research funded here. We also received funding for optimisation of factory performance from the EU - again informed by the research here. |
| Exploitation Route | Optimisation and data science are growing areas in all industries and the numerous research papers that have appeared from this grant will help these organisations. |
| Sectors | Construction Digital/Communication/Information Technologies (including Software) Environment Financial Services and Management Consultancy Healthcare Manufacturing including Industrial Biotechology Transport |