Evolutionary Computation & Optimisation - 2024 entry
| MODULE TITLE | Evolutionary Computation & Optimisation | CREDIT VALUE | 15 |
|---|---|---|---|
| MODULE CODE | ECMM423 | MODULE CONVENER | Dr Ke Li (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);
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;
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;
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 – paper presentation & panel questions | 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 |
|---|---|---|---|
|
Coursework - paper presentation & panel questions |
Coursework – paper presentation & panel questions |
1,4,5,7,8,10 |
August Ref/Def period |
|
Coursework – project design, implementation & experimentation
|
Coursework – project design, implementation & experimentation
|
1,2,3,5,6,8,9 |
August Ref/Def period |
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|
|
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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 50%. 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
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 | Thursday 14th March 2024 | LAST REVISION DATE | Thursday 14th March 2024 |
| KEY WORDS SEARCH | Evolutionary Computation; Optimisation |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.
