The Computational Intelligence research group has a primary focus in the three related areas of evolutionary algorithms, probabilistic modelling, and parallel computing.

We specialise in adaptive, intelligent computational approaches to problem-solving. Many real-world problems are complex, involving the consideration of vast amounts of data, the balancing of multiple objectives within challenging constraints and the shifting demands of a fast-moving world. We research powerful computational approaches to discover key relationships in data, to intelligently search for solutions in complex scenarios and to provide high performance computation that best adapts resources to demands.

The work of the group involves a variety of approaches to problems falling under the general category of learning and optimisation.

The aims of the group are:

• to apply intelligent algorithms to solve learning and/or optimisation problems;
• to develop improved or novel intelligent algorithms;
• to contribute to the theoretical understanding of computational intelligence.

Group members have interests in the fields of:

• Data modelling and inference using probabilistic graphical models such as Bayesian and Markov network models.
• Theory and applications of a wide range of naturally inspired techniques for single- and multi-objective optimisation, including evolutionary algorithms, particle swarms, ant colonies and estimation of distribution algorithms.
• Parallel and high performance computing.

Our research has found application in several areas including:

• prediction of pathological staging in prostate cancer
• chemotherapy treatment design and optimisation
• care visit scheduling
• agricultural bio-control
• data modelling for rig operations
• concurrent mining of neuro-oncological data

Current Projects in the Computational Intelligence Group

• A parallel distributed representation of cellular intelligence
• Rig Data Modelling using Bayesian Networks
• Novel multi-objective evolutionary approaches to optimisation
• Probabilistic Graphical Modelling for Medical Decision Making Under Uncertainty
• Bayesian Network Structure Learning from Data
• High Performance Scientific Modelling
• Parallel Computing

Previous Projects in the Computational Intelligence Group

• Bio-control for Mushroom Farming
• Cancer Chemotherapy Optimisation
• Domiciliary Care Scheduling
• Markov Networks in EDAs
• Modelling Patient Pathways for Prostate Cancer
• Using Machine Learning to Discover Diagnostic Sequence Motifs
• Closed-loop Machine Learning
• Efficient Biological Grammar Acquisition

People in the Computational Intelligence Group

Academic staff:

Eyad Elyan

Dr. Elyan is an active researcher at the Institute for Innovation, Design & Sustainability at Robert Gordon University. His principal research interests are in the areas of 3D object Modelling and Recognition.  

Jean-Claude Golovine

Lecturer  

Horacio Gonzalez-Velez

Research entails the deployment of structured parallelism approaches in heterogeneous computing systems such as clouds, grids, and clusters.  

John McCall

John is a Principal Member of the IDEAS Research Institute where he is responsible for the Digital Technologies theme.  

Andrei Petrovski

Andrei's primary research interests lie in the field of Computational Intelligence (CI) - particularly, in the application of CI heuristics.  

PhD students:

Noura Al Moubayed

Researching the methods and applications of multi-objective particle swarm optimisation.  

Lee Christie

Researching Markov Networks and Metaheuristic Search.  

Francois Fournier

Researching Oil Rig Data Modelling using Bayesian Networks.  

Michael Garba

Researching Adaptive Coordination Frameworks for Heterogeneous Parallel CPU/GPU Platforms.  

Claire Gerrard

Researching into artificial reaction networks.  

Mehdi Goli

Providing Parallel Patterns for Adaptive Heterogeneous Multicore-Manycore Systems.  

Olivier Regnier-Coudert

Research into advanced data modelling for medical treatment optimisation.  

Yanghui Wu

Researching Bayesian Network Structure Learning from Data  

KTP Associates:

Frederic Bouchet

Researching Adaptive Fault Detection Tools for Real-Time Integrity monitoring of Subsea Control Systems.  

Visiting researchers:

• Petr Kopyrulov
• Andrei Malakhov

Former members/Collaborators:

• Garry Brindley (Robert Gordon University)
• Deryck Brown
• Sandy Brownlee (Loughborough University)
• Chris Bryant (University of Salford, Manchester)
• Niccolo Capanni (Robert Gordon University)
• Olivier Delbouve (BT Research Centre, Ipswich)
• Daniel Fredouille
• Frank Herrmann
• Ratiba Kabli
• Thierry Mamer (BT Research Centre, Ipswich)    
• Roger McDermott (Robert Gordon University)   
• Ann Reddipogu
• Siddhartha Shakya (BT Research Centre, Ipswich)