Our research is concerned with the analysis, design, and application of evolutionary algorithms to solve various real-world problems. Our work covers both theoretical and applied aspects of Evolutionary Computation. The following lists our research interests and expertise:
A. Evolutionary Optimization
We are interested in solving various real-world problems that can be formulated as optimization problems. Most of these complex optimization problems are intractable or cannot be solved easily using classical methods. Our work focuses on developing cutting-edge evolutionary algorithms in conjunction with other modern techniques such as hyper-heuristics that are applied to solve complex, real-world optimization problems that include scheduling, cutting and packing, timetabling, and others.
- G. Kendall, "Scheduling English Football Fixtures over Holiday Periods," Journal of the Operational Research Society 59(6), pp. 743-755, 2008.
- E. K. Burke, M. Hyde, G. Kendall, and J. Woodward, "A Genetic Programming Hyper-Heuristic Approach for Evolving Two Dimensional Strip Packing Heuristics," IEEE Transactions on Evolutionary Computation 14(6), pp. 942-958, 2010.
- N. R. Sabar, M. Ayob, G. Kendall, R. Qu, "A Graph Coloring Constructive Hyper-Heuristic for Examination Timetabling Problems," Applied Intelligence, 2011.
- C. W. Kheng, S. Y. Chong, and M. H. Lim, "Centroid-Based Memetic Algorithm: Adaptive Lamarckian and Baldwinian Learning," International Journal of Systems Science, 2011.
B. Evolutionary Learning
We are also interested in solving real-world problems that can be formulated as learning problems. They include learning tasks such as regression and classification to game-plays. Our work focuses on theoretical aspects in evolutionary learning that involves quantitative performance analysis of algorithms as well as the applications of nature-inspired approaches to various real-world learning problems such as financial forecasting.
- G. Kendall and Y. Su, "Imperfect Evolutionary Systems. IEEE Transactions on Evolutionary Computation," 11(3), pp. 294-307, 2007.
- S. Y. Chong, P. Tino, and X. Yao, ``Measuring Generalization Performance in Co-evolutionary Learning,'' IEEE Transactions on Evolutionary Computation, 12(4), pp. 479-505, 2008.
- S. Y. Chong, P. Tino, and X. Yao, ``Relationship Between Generalization and Diversity in Co-evolutionary Learning,'' IEEE Transactions on Computational Intelligence and AI in Games, 1(3), pp. 214-232, 2009.
- J. M. Binner, P. Tino, J. Tepper, R. Anderson, B. Jones, and G. Kendall, "Does money matter in inflation forecasting?" Physica A: Statistical Mechanics and its Applications, 389(21), pp. 4793-4808, 2010.
C. Simulation and Modelling
Much of our work involves simulation and modelling of real-world systems through nature-inspired approaches such as evolutionary computation in conjunction with other approaches such as multi-agent systems. Most real-world problems involving strategic decision-making can be abstracted and modelled as games. Our complex computer simulations that involve populations of autonomous and adaptive agents have provided the means for in-depth studies on a variety of real-world problems such as understanding specific conditions that promote cooperation to risk regulation.
- S. Y. Chong and X. Yao, "Behavioral Diversity, Choices, and Noise in the Iterated Prisoner's Dilemma," IEEE Transactions on Evolutionary Computation, 9(6), pp. 540-551, Dec. 2005.
- S. Y. Chong and X. Yao, "Multiple Choices and Reputation in Multiagent Interactions," IEEE Transactions on Evolutionary Computation, 11(6), pp. 689-711, Dec. 2007.
- G. J. Davies, G. Kendall, E. Soane, J. Li, F. Charnley, and S. J. T. Pollard, "Regulators as ‘agents’: power and personality in risk regulation and a role for agent-based simulation," Journal of Risk Research, 13(8), pp. 961-982, 2010.
- J. Li, P. Hingston, and G. Kendall, "Engineering Design of Strategies for Winning Iterated Prisoner’s Dilemma Competitions," IEEE Transactions on Computational Intelligence and AI in Games, 3(4), pp. 348-360, 2011.
- Siang Yew Chong (webpage, e-mail: siang-yew.chong*)
- Graham Kendall (webpage, e-mail: Graham.Kendall*)
Below lists our recent major research funding:
Title: Towards More Effective Computational Search
Funding Body: EPSRC (Ref EP/H000968/1)
Start Date: February 2010
Amount Awarded: £1,001,333
Title: Next Generation Decision Support: Automating the Heuristic Design Process
Funding Body: EPSRC (EP/D061571/1)
Start Date: Mar 2006
Amount Awarded: £2,663,528