School of Computer Science

Module 06-12416 (2010)

Nature Inspired Optimisation

Level 4/M

Jonathan Rowe Semester 2 10 credits
Co-ordinator: Jonathan Rowe
Reviewer: Ata Kaban

The Module Description is a strict subset of this Syllabus Page.

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:

  • 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

Teaching methods

3 hrs lectures/tutorials per week


Assessment

  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary: 1.5 hr examination (100%)

Detailed Syllabus

  1. Combinatorial optimisation - an introduction to problems and algorithms.
  2. Local search algorithms - a study of their limitations.
  3. Simulated annealing - algorithms inspired by the physics of metals.
  4. Genetic algorithms - algorithms inspired by evolution.
  5. Ant colony optimisation - algorithms inspired by co-operative societies.
  6. Tabu search - using memory to guide the search process.
  7. Hybrid methods - combining the best of different approaches.
  8. Real-valued optimisation - an introduction to problems and algorithms.
  9. Evolutionary strategies - applying evolutionary principles to real-valued problems.
  10. Gray-code algorithms - the effect of changing representations.
  11. CHC - a state of the art optimisation algorithm.
  12. No Free Lunch - why all search algorithms are the same (on average).

Programmes containing this module