Computer Science

Traditional computation finds it either difficult or impossible to perform a wide range  of tasks  including product design, decision making, logistics and scheduling, pattern recognition and problem solving. However, nature is proven to be highly adept at solving problems making it possible to take inspiration from these methods and create computing techniques based on natural systems.  This module will provide you with the knowledge to create and apply techniques based on evolution, the intelligence of swarms of insects and flocks of animals, and the way the human brain is thought to process information.  This module is appropriate for any student with an interest in optimisation and data analysis who is has some programming and mathematical experience.

This module aims to provide you with the necessary expertise to create, experiment with and analyse modern nature-inspired algorithms and techniques  as applied to problems in industry and industrially motivated research fields such as Operations Research.

The module also aims to provide you with knowledge of the limitations and advantages of each algorithm and the expertise to determine which algorithm to select for a given problem,
 

Module Specific Skills and Knowledge:
1 Demonstrate deep understanding of the difficulties associated with certain intelligence-related tasks that we would wish to program computers to do
2 Understand and implement several diverse nature-inspired algorithms, and appreciate the circumstances and environments in which they are best employed
Discipline Specific Skills and Knowledge:
3 Implement software for addressing either large-scale real-world scheduling and optimisation problems or complex real-world pattern recognition problems
4 Implement software for producing lifelike simulations of certain natural behaviours
Personal and Key Transferable/ Employment Skills and  Knowledge:
5 Analyse and choose appropriate techniques for given problems from a very diverse toolbox of methods
6 Understand how new ideas in science and engineering can emerge from lateral thinking and ideas from other disciplines
7 Synthesise and succinctly communicate information from publications in the field to individuals unfamiliar with the material
8 Work effectively as part of team to design, implement and demonstrate a system to solve a problem

Syllabus divided broadly into three sections Evolutionary Computation: Classical vs. Nature-inspired computation, evolutionary algorithms including operators, representations and encodings and applications Neural networks: gradient search, perceptrons, backpropagation and multilayer perceptrons Other Techniques. To include at least two of: ant colony optimisation, swarm intelligence, artificial life & cellular automata, swarm behaviour, immune system methods.

None

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.
If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

ELE – http://vle.exeter.ac.uk

Reading list for this module: