Computer Science

ECM3412 - Nature Inspired Computation (2019)

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MODULE TITLENature Inspired Computation CREDIT VALUE15
MODULE CODEECM3412 MODULE CONVENERDr Alberto Moraglio (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 weeks 0 0
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content

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

 

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 the appropriate algorithm selection 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 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.
Discipline Specific Skills and Knowledge:
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.
Personal and Key Transferable / Employment Skills and Knowledge:
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.

 

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 21.00 Guided Independent Study 129.00 Placement / Study Abroad 0.00
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

 

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 30 Written Exams 70 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam – closed book 70 2 hours - Summer Exam Period 1, 2, 3, 4, 5, 8, 9 Oral, on request
Coursework – programming & report 30 30 hours 1,5, 10 and 1 of 6, 7 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 Written exam (100%) All August Ref/Def
       
       

 

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.
 

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
Set Eberhart, R. Shui, Y. and Kennedy, J. Swarm Intelligence Morgan Kaufmann 2001 [Library]
Set Bishop, C Neural Networks for Pattern Recognition Clarendon Press 1995 [Library]
Set Mitchell, M An Introduction to Genetic Algorithms MIT Press 1998 [Library]
Set Dorigo, M and Stutzle, T Ant Colony Optimization Bradford Book 2004 [Library]
Extended Corne, D., Bentley, P. (eds.) Creative Evolutionary Systems Morgan Kaufmann 2002 1558606734 [Library]
Extended Goldberg, D Genetic Algorithms in Search, Optimization and Machine Learning Addison Wesley 1989 [Library]
Extended Wolfram; S. Cellular Automata and Complexity Perseus Publishing 2002 9780201626643 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1410, ECM1414
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 3 (NQF Level 6) 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.