Computer Science

ECMM423 - Evolutionary Computation & Optimisation (2019)

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MODULE TITLEEvolutionary Computation & Optimisation CREDIT VALUE15
MODULE CODEECMM423 MODULE CONVENERDr Alberto Moraglio (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 module is appropriate for any student with an interest in bio-inspired problem-solving techniques and optimisation who has some programming and mathematical experience.

Prerequisite module: ECM3412 or ECMM409 or equivalent

AIMS - intentions of the module

The aims of this module are to:

introduce the main 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 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 34.00 Guided Independent Study 116.00 Placement / Study Abroad 0.00
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 80 Written Exams 0 Practical Exams 20
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework – evolutionary computation project & technical report 80 50 hours 1,2,3,4,5,6,7,8,9 Comments directly on project report and on individual feedback sheet
Coursework - Presentation & demonstration 20 10 hours preparation 1, 5, 10 Written, verbal
         
         

 

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 Search
Set Goldberg, D Genetic Algorithms in Search, Optimization and Machine Learning Addison Wesley 1989 [Library]
Set Banzhaf W, Nordin P, Keller R E and Francone F D Genetic Programming: an introduction Morgan Kaufmann 1998 978-1558605107 [Library]
Set T. Baeck, D. B. Fogel, and Z. Michalewicz Handbook on Evolutionary Computation 1997 [Library]
Set Z Michalewicz Genetic Algorithms + Data Structures = Evolution Programs 3rd Springer 1996 [Library]
Set Kalyanmoy Deb -Objective Optimization Using Evolutionary Algorithms 2001 [Library]
Set James C. Spall Introduction to Stochastic Search and Optimization 2003 [Library]
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 10 July 2018 LAST REVISION DATE Tuesday 10 July 2018
KEY WORDS SEARCH Evolutionary Computation; Optimisation