ECMM409 | 2025%2F6 | University of Exeter

MODULE TITLE	Nature-Inspired Computation	CREDIT VALUE	15
MODULE CODE	ECMM409	MODULE CONVENER	Dr Alberto Moraglio (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS	11 weeks	0	0

Number of Students Taking Module (anticipated)	103

DESCRIPTION - summary of the module content

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.

Non-requisites (cannot be taken with): ECM3412 Nature-Inspired Computation

AIMS - intentions of the module

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.

MEng AHEP3 ILOs covered on this module:

SM1m-SM6m, EA1m-EA6m, D1m, D3m-D8m, ET3m, EP1m, EP3m, EP4m, EP8m-EP11m, G1m-G4m

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module, you should be able to:

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 comprehend 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 comprehend 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 PLAN - summary of the structure and academic content of the module

• 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).

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	82	Guided Independent Study	68	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled learning and teaching activities	20	Lectures
Scheduled learning and teaching activities	60	Individual-assessed work
Scheduled learning and teaching activities	2	Workshop/tutorials
Guided independent study	68	Private study

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Not applicable

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Individual algorithm report	30	30 hours	1-5, 7	Written
Team project	70	3,000-word individual report, team report and program code (shared)	1, 5-6, 8	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-reassessment
Individual algorithm report	Individual algorithm report (30 hours, 30%)	1-5, 7	Referral/Deferral Period
Team project	Team project (3,000-word individual report, team report and program code (shared), 70%)	1, 5-6, 8	Referral/Deferral Period

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

• Wolfram, S., Cellular Automata and Complexity, Perseus Publishing, 2002, ISBN 9780201626643

• Eberhart, R, Shui Y., and Kennedy J., Swarm Intelligence, Morgan Kaufmann, 2001

• Corne, D., Bentley P. (eds), Creative Evolutionary Systems, Morgan Kaufmann, 2002, ISBN 1558606734

• Bishop, C., Neural Networks for Pattern Recognition, Clarendon Press, 1995

Web-based and electronic resources:

• ELE

Reading list for this module:

Type	Author	Title	Publisher	Year	ISBN
Reference	Wolfram; S.	Cellular Automata and Complexity	Perseus Publishing	2002	9780201626643
Reference	Eberhart, R. Shui, Y. and Kennedy, J.	Swarm Intelligence	Morgan Kaufmann	2001
Reference	Corne, D., Bentley, P. (eds.)	Creative Evolutionary Systems	Morgan Kaufmann	2002	1558606734
Reference	Bishop, C	Neural Networks for Pattern Recognition	Clarendon Press	1995

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Thursday 14th March 2024	LAST REVISION DATE	Wednesday 7th May 2025

KEY WORDS SEARCH	Evolutionary computation; neural networks; swarm intelligence; ant colony optimisation; particle swarm optimisation; artificial immune systems.

Nature-Inspired Computation - 2025 entry