Module 12416 (2004)

Syllabus page 2004/2005

Nature Inspired Optimisation

Level 4/M

Jon Rowe
10 credits in Semester 2

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus

The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

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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.


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:
1explain how nature-inspired optimisation techniques fit within the context of established optimisation theory Examination
2apply a range of nature-inspired algorithms to various real-valued and combinatorial optimisation problems Examination
3design and adapt nature-inspired algorithms to novel optimisation problems Examination
4describe the appropriate underlying theory and discuss its current limitations Examination

Restrictions, Prerequisites and Corequisites






06-12412 (Introduction to Neural Computation) (unless 06-02360 (Introduction to Neural Networks) has been taken previously); 06-12414 (Introduction to Evolutionary Computation) (unless 06-02411 (Evolutionary Computation) has been taken previously)


Teaching Methods:

2 hrs lectures/tutorials per week

Contact Hours:



  • Supplementary (where allowed): As the sessional assessment
  • 2 hr open book examination (100%).

Recommended Books

TitleAuthor(s)Publisher, Date
Modern Heuristic Techniques for Combinatorial ProblemsC ReevesMcGraw-Hill, 1995
How To Solve ItZ Michalewicz & D B FogelSpringer, 2000
Evolutionary Algorithms in Theory and PracticeT BaeckOUP, 1996

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).

Last updated: 2 Feb 2004

Source file: /internal/modules/COMSCI/2004/xml/12416.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus