COMP41190 Natural Computing & Applications 2011
Module Coordinator: Dr. Michael O'Neill
Description:
The field of Natural Computing has advanced rapidly over the past
decade. One offshoot of this progress has been the development of a large
family of algorithms inspired by Nature, including Biological, Social and
Physical systems. Broadly speaking, these algorithms draw metaphorical
inspiration from diverse sources, including the operation of biological
neurons, processes of evolution, models of social interaction amongst organisms,
and natural immune systems, in order to develop tools for solving real-world problems.
This module provides an introduction to a broad range of Natural Computing algorithms
and illustrates how they can be applied to real-world problems using a series of case studies.
In addition to teaching the essentials of Natural Computing, The module provides experience in the planning, executing, and writing up of research.
Learning Outcomes:
On completion of the module students should be able to:
- Outline the main Natural Computing algorithms
- Compare and Contrast the different Natural Computing methods
- Solve a problem using Natural Computing
- Design an experiment in Natural Computing
- Write an academic paper
Assessment Strategies:
This is a 50% continuous assessment, and 50% final exam module.
Each student undertakes an individual project, and writes up
their work in the form of a 10-page conference-style paper.
In addition, students sit a written end of semester examination.
Contact Hours:
- Tuesday 15h00-17h00 (B109)
- Thursday 15h00-16h00 (B109)
- Outside these times please email Module Coordinator for an appointment.
Deadlines & Announcements
- 10 page conference-style paper due Thur 1 December by 3pm
- Email a *PDF* to Michael by deadline.
- Hand in a printout at 3pm lecture on Thur 1st Dec.
- No lecture on Tue 29th November - free project time
- NEW: Latex template provided below.
Module Materials
- Week 1
- Week 2
- Week 3
- Week 4
- Week 5
- Project Clinic Session
- Project proposal submissions due Thur at 3pm
- Week 6
- Week 7
- Week 8
- Week 9
- Week 10
- Thursday Project Clinic Session
- Week 11
- Thursday Project Clinic Session
- Week 12
- Latex style file (close to the Word template but not exact!)
- 10 page paper due 3pm Thursday
Recommended Reading
The type of project you undertake in this module will largely guide the depth in which you approach one of the Natural Computing methods. Some recommendations for the main methods follow.
- Brabazon A., O'Neill M. (2006) Biologically Inspired Algorithms for Financial Modelling. Springer. (Part I of this book provides an overview of Natural Computing. Part III details case studies demonstrating application of a selection of Natural Computing methods to real world problems.)
- Poli R., Langdon W.B., McPhee N.F. (2008). A Field Guide to Genetic Programming.
- O'Neill M., Ryan C. (2003) Grammatical Evolution. Kluwer Academic Publishers.
- Dempsey I., O'Neill M., Brabazon A. (2009). Foundations in Grammatical Evolution for Dynamic Environments. Springer.
- Banzhaf W., et al. (1998) Genetic Programming: An Introduction. Morgan Kaufmann.
- Koza J.K. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. (There are three follow up books on GP by Koza which you should investigate)
- Goldberg D.E.G. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
- Goldberg D.E.G. (2002) The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers.
- Holland J.H. (1975) Adaptation in Natural and Artificial Systems. MIT Press. (2nd edition (1992) is the one available.)
- Dorigo M., Stutzle T. (2004) Ant Colony Optimization. MIT Press.
- Kennedy J., Eberhardt R. (2001) Swarm Intelligence. Morgan Kaufmann.
- Various conference and journal papers will be discussed over the course of the module. Specific details to follow in Module Materials above.
Useful Links