SYLLABUS PAGE, 2009/10
06-12416
Nature Inspired Optimisation
Level 4/M
|
Dr J E Rowe |
10 credits in Sem2 |
Programmes | Modules | Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The School of Computer Science Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
None.
Outline
| This module introduces a range of nature-inspired algorithms for both real-valued and combinatorial optimisation. Examples of such algorithms include: Evolutionary Algorithms, Ant Colony Algorithms, Simulated Annealing, Tabu Search. The study of these techniques and the problems for which they are designed will take place within the broader context of established optimisation theory. Such theory as currently exists for the new techniques will also be presented. |
Aims
The aims of this module are to:
- Introduce nature-inspired optimisation techniques.
- Show how these techniques can be used.
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | Explain how nature-inspired optimisation techniques fit within the context of established optimisation theory. | Examination |
| 2 | Apply a range of nature-inspired algorithms to various real-valued and combinatorial optimisation problems. | Examination, Continuous Assessment |
| 3 | Design and adapt nature-inspired algorithms to novel optimisation problems. | Examination, Continuous Assessment |
| 4 | Describe the appropriate underlying theory and discuss its current limitations. | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
| None |
Prerequisites:
| None |
Co-requisites:
| None |
Teaching
Teaching methods:
| 3 hrs lectures/tutorials per week |
Contact hours:
| 35 |
Assessment
Normal (sessional): 1.5 hr examination (80%), continuous assessment (20%).
Resit (supplementary) assessment (where allowed): 1.5 hr examination (100%)
Recommended Books
| Title | Author(s) | Publisher, Date |
| Modern Heuristic Techniques for Combinatorial Problems | C Reeves | McGraw-Hill, 1995 |
| How To Solve It | Z Michalewicz & D B Fogel | Springer, 2000 |
| Stochastic Local Search | H Hoos & T Stuzle | Elsevier, 2005 |
| New Ideas in Optimization | D Corne, M Dorigo & F Glover | McGraw-Hill, 1999 |
Detailed Syllabus
- Combinatorial optimisation - an introduction to problems and algorithms.
- Local search algorithms - a study of their limitations.
- Simulated annealing - algorithms inspired by the physics of metals.
- Genetic algorithms - algorithms inspired by evolution.
- Ant colony optimisation - algorithms inspired by co-operative societies.
- Tabu search - using memory to guide the search process.
- Hybrid methods - combining the best of different approaches.
- Real-valued optimisation - an introduction to problems and algorithms.
- Evolutionary strategies - applying evolutionary principles to real-valued problems.
- Gray-code algorithms - the effect of changing representations.
- CHC - a state of the art optimisation algorithm.
- No Free Lunch - why all search algorithms are the same (on average).
Programmes | Modules | Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus