Computer Science

There is a wide range of tasks, including product design, decision making, logistics and scheduling, pattern recognition and problem solving, which traditional computation finds it either difficult or impossible to perform. 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 has some programming and mathematical experience.

Pre-requisite modules: ECM1401, ECM1701 and/or ECM1707


 

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 a clear 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 large-scale real-world optimisation problems;
4 implement software for addressing certain complex real-world pattern recognition problems;
5 implement software for producing lifelike simulations of certain natural behaviours.
Personal and Key Transferable/ Employment Skills and  Knowledge:
6 choose appropriate techniques for given problems from a very diverse toolbox of methods;
7 understand how new ideas in science and engineering can emerge from lateral thinking and ideas from other disciplines.
8 digest and communicate succinctly information from publications in the field to individuals unfamiliar with the material.

Classical vs. Nature-inspired computation, evolutionary algorithms, ant colony optimisation, swarm intelligence, neural computation, artificial life, cellular automata, reaction-diffusion systems, swarm behaviour, behaviour-based robotics, immune system methods.

None

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: