Nature Inspired Computation

Module description

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 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 you if you have an interest in optimisation and data analysis, and have some programming and mathematical experience.

Pre-requisite - ECM3412 or equivalent

Full module specification

Module title:Nature Inspired Computation
Module code:ECMM409
Module level:M
Academic year:2014/5
Module lecturers:
  • Professor Edward Keedwell - Convenor
Module credit:15
ECTS value:



ECM3412 or equivalent

Duration of module: Duration (weeks) - term 1:


Duration (weeks) - term 2:


Duration (weeks) - term 3:


Module aims

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.

ILO: Module-specific skills

  • 1. demonstrate deep understanding of the difficulties associated with certain intelligence-related tasks that we would wish to program computers to do;
  • 2. comprehend and implement several diverse nature-inspired algorithms, and appreciate the circumstances and environments in which they are best employed.

ILO: Discipline-specific skills

  • 3. implement software for addressing either large-scale real-world scheduling and optimisation problems or complex real-world pattern recognition problems;
  • 4. comprehend software for producing lifelike simulations of certain natural behaviours.

ILO: Personal and key skills

  • 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.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activities18Lectures
Scheduled learning and teaching activities60Individual-assessed work
Scheduled learning and teaching activities10Supervisory meetings for team project
Guided independent study62Private study

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Not applicable

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Programming2020 hours2,3,4Written
Presentation of technical report 1012-15 slides + handout1,5,7Written
Team project703,000 word individual report, team report and program code (shared)1,5,6,8Written

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
All aboveCoursework (100%)AllCompleted over the summer with a deadline in August

Re-assessment notes

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.

Syllabus plan

- 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


  1. Cellular Automata and Complexity,Wolfram; S.,Perseus Publishing,2002,511.35 WOL,9780201626643
  2. Swarm Intelligence,Eberhart, R. Shui, Y. and Kennedy, J.,Morgan Kaufmann,2001,001.535 KEN
  3. Creative Evolutionary Systems,Corne, D., Bentley, P. (eds.),Morgan Kaufmann,2002,001.642 CRE,1558606734
  4. Neural Networks for Pattern Recognition,Bishop, C.,Clarendon Press,1995,001.534 BIS

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