ECMM423 | 2021/2 | University of Exeter

MODULE TITLE	Evolutionary Computation & Optimisation	CREDIT VALUE	15
MODULE CODE	ECMM423	MODULE CONVENER	Prof Edward Keedwell (Coordinator)

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

Number of Students Taking Module (anticipated)	30

DESCRIPTION - summary of the module content

Evolutionary computation is the study of computational systems that use ideas and derive their inspiration from natural evolution. Its techniques can be applied to optimisation, learning and design. The main focus of this module is on optimisation problems. Example topics covered in this module include natural and artificial evolution, chromosome representations and search operators for continuous and combinatorial optimisation, co-evolution, techniques for constrained optimisation, multi-objective optimisation, dynamic optimisation, evolution of neural networks, genetic programming and theoretical foundations. This is a research-led module appropriate for students with an interest and a background in bio-inspired problem-solving techniques and optimisation who have adequate programming and mathematical experience.

Prerequisite module: ECM3412 or ECMM409 or equivalent and ECM1400 or equivalent

AIMS - intentions of the module

The aims of this module are to:

Introduce the main and advanced concepts and techniques in the field of evolutionary computation and their application to optimisation problems;

Provide students with practical experience on the development and implementation of evolutionary techniques, and their appropriate usage.

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 and deep understanding of the main flavours of evolutionary algorithms and of types of optimisation problems;
2. Design new evolutionary operators, representations and fitness functions for specific applications (e.g., combinatorial/real, multi-objective, constrained);

3. Implement evolutionary algorithms and determine appropriate parameter settings to make them work well;

Discipline Specific Skills and Knowledge

4. Describe the role of evolutionary computation in the context of computer science, artificial intelligence, and optimisation;
5. Demonstrate familiarity with the main trends in evolutionary computation research;

6. Implement software for addressing real-world optimisation problems;

Personal and Key Transferable / Employment Skills and Knowledge

7. Read and digest research papers from conferences and journals;
8. Relate theoretical knowledge to practical concerns;

9. Conduct a research project including sound statistical analysis of experimental results, and contrast the results found with those expected given previously published material;

10.Communicate succinctly information from publications to individuals unfamiliar with the material.

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

Indicative list of topics:

Summary of traditional optimisation techniques
History of evolutionary computation and biological background
Basic structure of an evolutionary algorithm
Genetic representation, search operators, selection schemes and selection pressure
Optimisation problems, fitness landscapes and multi-modality
Multi-population methods, co-evolution
Niching and speciation
Multi-objective evolutionary optimisation
Dynamic optimisation
Robust and noisy optimisation
Genetic programming
Evolving learning-machines, e.g. neural networks
Theoretical analysis of evolutionary algorithms
Experimental design

LEARNING AND TEACHING

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

Scheduled Learning & Teaching Activities	34	Guided Independent Study	116	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled learning and teaching activities	24	Lectures
Scheduled learning and teaching activities	10	Workshop/tutorials
Guided independent study	50	Project and Coursework
Guided independent study	66	Wider reading

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

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
Coursework – literature review & project definition	40	20 hours	1,4,5,7,8,10	Comments directly on report and on individual feedback sheet
Coursework – project design, implementation & experimentation	60	40 hours preparation	1, 2, 3, 5, 6, 8, 9	Individual feedback sheet

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-assessment
All above	Coursework (100%)	All	Ref/def Exam Period

RE-ASSESSMENT NOTES

Since this is assessed entirely by coursework, all referred assessments will be by the assignment of a new piece of coursework. Deferred assignments will be done by the original piece of coursework combining elements of the module.

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:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Articles in journals and conference proceedings

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year	ISBN
Set	Goldberg, D	Genetic Algorithms in Search, Optimization and Machine Learning		Addison Wesley	1989
Set	Banzhaf W, Nordin P, Keller R E and Francone F D	Genetic Programming: an introduction		Morgan Kaufmann	1998	978-1558605107
Set	T. Baeck, D. B. Fogel, and Z. Michalewicz	Handbook on Evolutionary Computation			1997
Set	Z Michalewicz	Genetic Algorithms + Data Structures = Evolution Programs	3rd	Springer	1996
Set	Kalyanmoy Deb	-Objective Optimization Using Evolutionary Algorithms			2001
Set	James C. Spall	Introduction to Stochastic Search and Optimization			2003

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	ECM3412, ECMM409
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Tuesday 10th July 2018	LAST REVISION DATE	Friday 14th May 2021

KEY WORDS SEARCH	Evolutionary Computation; Optimisation

Evolutionary Computation & Optimisation - 2021 entry