The following is indicative of topics/themes this module will include:
• An overview of basic concepts related to Computational Intelligence (CI) techniques, such as heuristic search and optimisation
• Presentation of different CI algorithms, such as hill climbing, simulated annealing, genetic algorithms and genetic programming
• An overview of basic concepts related to real-world problems related to business, economics and finance, such as financial forecasting, automated bargaining, portfolio
optimisation, and timetabling
• The use of Computational Intelligence techniques to solve real-world problems
• Computational Intelligence decision support systems and software wind tunnels for testing new markets and strategies
Total contact hours: 33 hours
Private study hours: 117 hours
Total study hours: 150 hours
Main assessment methods
3 courseworks (40 hours total) ( 50%)
2 hour unseen written examination (50%)
Reassessment methods
Like for like.
Bentley, P. (2002). Digital Biology. Hodder-Headline.
Brabazon, A. O'Neill, M. (2006). Biologically inspired algorithms for Financial Modelling. Springer-Verlag.
De Jong, K. (2006). Evolutionary Computation: A Unified Approach. MIT Press.
Gendreau, M., Ptovin, J.-Y. (Eds.) (2010). Handbook of Metaheuristics, International Series in Operations Research & Management, Vol. 146, Second Edition.
Gil-Lafuente, A., Merigo, J. (Eds) (2010). Computational Intelligence in Business and Economics, Proceedings of the MS'10 International Conference, World Scientific Proceedings Series on Computer Engineering and Information Science, Volume 3.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley.
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. A Bradford Book, volume 1.
Mitchell, M. (1998). An Introduction to Genetic Algorithms (Complex Adaptive Systems), A Bradford Book, Third Edition.
Poli, R., Langdon, W.B., McPhee, N.F. A Field Guide to Genetic Programming. Available at: http://www.gp-field-guide.org.uk
Papadimitriou, C., Steiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity. Dover Publications.
Wang, P. (Ed.) (2004). Computational Intelligence in Economics and Finance. Springer.
See the library reading list for this module (Canterbury)
See the library reading list for this module (Medway)
8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 Understand the concept of Computational Intelligence and its relationship to real-world problems [B1, C2, C11]
8.2 Give a description of different CI algorithms with some examples of their applications [B2, C2, C10, C11, D2]
8.3 Identify strategies for the design, implementation and evaluation of a CI system to a given business problem [A4, B3, B5, C1, C2, C9, D3]
8.4 Present and deliver innovative solutions to a range of real-world problems from the fields of business, economics and finance [C11, D2, D3, D5]
8.5 Implement a basic genetic algorithm on the computer, and apply this program to different business problems [A2, A5, B1, C1, D3]
9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Demonstrate an understanding of theory and be able to deploy it in design, implementation, information management and evaluation of computer based systems [B7, C3]
9.2 Demonstrate effective use of general IT facilities [D3]
9.3 Be able to exploit library and online resources to support investigations into the relevant problem areas [D3]
9.4 Be able to write coherently and critically about the topics studied in the course, based on readings from the scientific literature and demonstrating an awareness of how to write in a scientific manner [C2, D3]
9.5 Be able to apply appropriate computer programming techniques [A2, C1]
9.6 Be able to apply appropriate scientific principles and methodology [C2]
9.7 Show communication skills in delivering messages to a range of audiences about technical problems and their solutions [B2, C11, D2]
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