Module 06-12416 (2010)
Nature Inspired Optimisation
|Jonathan Rowe||Semester 2||10 credits|
Co-ordinator: Jonathan Rowe
Reviewer: Ata Kaban
The Module Description is a strict subset of this Syllabus Page.
The aims of this module are to:
- introduce nature-inspired optimisation techniques
- show how these techniques can be used
On successful completion of this module, the student should be able to:
- explain how nature-inspired optimisation techniques fit within the context of established optimisation theory
- apply a range of nature-inspired algorithms to various real-valued and combinatorial optimisation problems
- design and adapt nature-inspired algorithms to novel optimisation problems
- describe the appropriate underlying theory and discuss its current limitations
3 hrs lectures/tutorials per week
- Sessional: 1.5 hr examination (80%), continuous assessment (20%).
- Supplementary: 1.5 hr examination (100%)
- 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).