Computer Science

ECMM409 - Nature-Inspired Computation (2019)

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MODULE TITLENature-Inspired Computation CREDIT VALUE15
MODULE CODEECMM409 MODULE CONVENERDr Alberto Moraglio (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 weeks 0 0
Number of Students Taking Module (anticipated) 3
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-requisite module - ECM3412

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,
 

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);
- artificial life;
- cellular automata;
- immune system methods.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 88.00 Guided Independent Study 62.00 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 60 Individual-assessed work
Scheduled learning and teaching activities 10 Supervisory meetings for team project
Guided independent study 62 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 70 Written Exams 0 Practical Exams 30
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Programming 30 30 hours 1,2,3,4,5,7 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
All above Coursework (100%) All Completed over the summer with a deadline in August
       
       

 

RE-ASSESSMENT NOTES

Referred and deferred assessment will normally be by coursework. For referrals, only the examination will count, a mark of 50% 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.

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

ELE – http://vle.exeter.ac.uk


 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Reference Wolfram; S. Cellular Automata and Complexity Perseus Publishing 2002 9780201626643 [Library]
Reference Eberhart, R. Shui, Y. and Kennedy, J. Swarm Intelligence Morgan Kaufmann 2001 [Library]
Reference Corne, D., Bentley, P. (eds.) Creative Evolutionary Systems Morgan Kaufmann 2002 1558606734 [Library]
Reference Bishop, C Neural Networks for Pattern Recognition Clarendon Press 1995 [Library]
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 Tuesday 10 July 2018 LAST REVISION DATE Tuesday 10 July 2018
KEY WORDS SEARCH Evolutionary computation; neural networks; swarm intelligence; ant colony optimisation; particle swarm optimisation; artificial immune systems.