Computational Intelligence in Business, Economics & Finance - CO656

Location Term Level Credits (ECTS) Current Convenor 2019-20
Medway Autumn
View Timetable
6 15 (7.5) DR M Kampouridis


CO320 Introduction to Object-Oriented Programming





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


This module appears in:

Contact hours

Total contact hours: 33 hours
Private study hours: 117 hours
Total study hours: 150 hours

Method of assessment

Assessment GA (50%)
2-hour unseen written examination (50%)

Indicative reading

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:
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)

Learning outcomes

Understand the concept of Computational Intelligence and its relationship to real-world problems
Give a description of different CI algorithms with some examples of their applications
Identify strategies for the design, implementation and evaluation of a CI system to a given business problem
Present and deliver innovative solutions to a range of real-world problems from the fields of business, economics and finance
Implement a basic genetic algorithm on the computer, and apply this program to different business problems

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