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Module Title
LH Advanced Aspects of Nature-Inspired Search and Optimisation
School
Computer Science
Department
Computer Science
Module Code
06 27818
Module Lead
Christine Zarges
Level
Honours Level
Credits
20
Semester
Semester 2
Pre-requisites
Co-requisites
Restrictions
None
Exclusions
Description
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.
The aims of this module are to
- introduce the main concepts, techniques and applications in the field of randomised search
heuristics and nature-inspired computing with a focus on (but not limited to) optimisation
-give students some experience on when such techniques are useful, how to use them in practice and how to implement them with different programming languages
Learning Outcomes
By the end of the module students should be able to:
Describe different nature-inspired search and optimisation methods and explain how they are applied to solve real world problems
Discuss relations, similarities and differences between the most important heuristics and nature-inspired algorithms presented in the module and other search and optimisation techniques.
Design and adapt nature-inspired algorithms including operators, representations, fitness functions and potential hybridisations for non-trivial problems.
Implement nature-inspired algorithms using different programming languages and compare them experimentally