There are 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 has proven to be highly adept at solving problems, making it possible to take inspiration from these methods and to 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 natural systems, optimisation and data analysis who has some programming and mathematical experience.
Prerequisite module: ECM1410 and ECM1414 or equivalent
Full module specification
|Module title:||Nature-Inspired Computation|
ECM1410 and ECM1414 or equivalent
|Duration of module:||
Duration (weeks) - term 1: |
12Duration (weeks) - term 2:
0Duration (weeks) - term 3:
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 the appropriate algorithm selection for a given problem.
ILO: Module-specific skills
- 1. demonstrate a clear understanding of the difficulties associated with certain intelligence-related tasks that we would wish to program computers to do;
- 2. describe in broad terms, the execution of each nature-inspired algorithm;
- 3. discuss the circumstances and environments in which each algorithm is best employed;
- 4. define the different underlying natural mechanisms of each algorithm and explain how this leads to improved computational performance;
- 5. evaluate a difficult problem and determine the likely best algorithm selection.
ILO: Discipline-specific skills
- 6. implement software for addressing real-world optimisation problems with nature-inspired methods;
- 7. create software for addressing certain complex real-world pattern recognition problems.
ILO: Personal and key skills
- 8. choose appropriate techniques for given problems from a very diverse toolbox of methods;
- 9. explain how new ideas in science and engineering can emerge from lateral thinking and ideas from other disciplines;
- 10. digest and communicate succinctly information from publications in the field to individuals unfamiliar with the material.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled learning and teaching activities||18||Lectures|
|Scheduled learning and teaching activities||3||Workshops/tutorials|
|Scheduled learning and teaching activities||30||Individual assessed work|
|Guided independent study||99||Guided independent study|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Written exam closed book||70||2 hours||1, 2, 3, 4, 5, 8, 9||Oral, on request|
|Coursework programming||20||20 hours||1,5 and 1 of 6, 7||Written|
|Coursework presentation||10||12-15 slides||1, 3, 9, 10||Written|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|All above||Written exam (100%)||All||August Ref/Def|
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.
- classical vs. nature-inspired computation;
- evolutionary algorithms (including genetic programming and multi-objective evolutionary algorithms);
- ant colony optimisation;
- particle swarm optimisation;
- swarm intelligence;
- neural computation (including multi-layer perceptrons and self-organising maps);
- artificial life;
- cellular automata;
- immune system methods.
Indicative learning resources - Basic reading
Swarm Intelligence, Eberhart, R., Shui, Y. and Kennedy, J., Morgan Kaufmann,2001,001.535 KEN
Neural Networks for Pattern Recognition,Bishop, C,,Clarendon Press,1995,001.534 BIS
An Introduction to Genetic Algorithms,Mitchell, M,,MIT Press,1998,001.535 MIT
Ant Colony Optimization,Dorigo, M and Stutzle, T,,Bradford Book,2004,519.7 DOR
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
Creative Evolutionary Systems,Corne, D., Bentley, P. (eds.),Morgan Kaufmann,2002,001.642 CRE,1558606734
Genetic Algorithms in Search, Optimization and Machine Learning,Goldberg, D.,Addison Wesley,1989,001.535 GOL
Cellular Automata and Complexity,Wolfram; S.,Perseus Publishing,2002,511.35 WOL,9780201626643
Last revision date