Module 28209 (2019)

Large Group Lectures

Contact Hours:

23

Natural Computation is the study of computational systems that use ideas and get inspiration from a variety of natural systems. Its powerful techniques can be applied not only to optimisation but also learning and design. Many such techniques can be characterised as general randomised search heuristics which are the method of choice in practical optimisation scenarios where no good problem-specific algorithms are available.Topics covered in this module focus on nature-inspired optimisation techniques. Where appropriate, the methods discussed are related to other approaches and application areas. Example topics covered include variants of local search, evolutionary computation, swarm intelligence and artificial immune systems. While the focus is on the applications of such techniques, theoretical foundations are also briefly studied.

On successful completion of this module, the student should be able to:

1. Describe different nature-inspired search and optimisation methods and explain how they are applied to solve real world problems
2. Discuss relations, similarities and differences between the most important heuristics and nature-inspired algorithms presented in the module and other search and optimisation techniques
3. Design and adapt nature-inspired algorithms including operators, representations, fitness functions and potential hybridisations for non-trivial problems

Sessional: 1.5 hr Examination (90%), Continuous Assessment (10%)

Supplementary (where allowed): 1.5 hr Examination (100%)