Module 02411 (2001)
Syllabus page 2001/2002
06-02411
Evolutionary Computation
Level 3/H
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
Outline
Evolutionary Computation is a new discipline whose main objective is to try and imitate reproduction genetics and natural selection to produce powerful global optimisation algorithms. These algorithms can be used to solve engineering problems such as parameter optimisation and combinatorial optimisation. However, as many processes considered part of intelligence (e.g. learning, motor control, scientific discovery, automatic design) can be reframed as hard combinatorial optimisation problems, evolutionary computation can also provide new solutions to problems in artificial intelligence. The course gives an introduction to the main techniques and applications of evolutionary computation.
Aims
The aims of this module are to:
- introduce the main concepts, techniques and applications in the field of evolutionary computation
- give students some practical experience on when evolutionary techniques are useful, how to use them in practice and how to implement them with different programming languages.
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | understand the relations between the most important evolutionary algorithms presented in the course, new algorithms to be found in the literature now or in the future, and other search and optimisation techniques | Examination |
| 2 | implement evolutionary algorithms | Examination |
| 3 | determine the right parameter settings to make different evolutionary algorithms work well | Examination |
| 4 | design new evolutionary operators, representations and fitness functions for specific practical and scientific applications | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
1 hr lecture, 1 hr exercise class/tutorial per week
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 2 hr examination (100%).
Recommended Books
| Title | Author(s) | Publisher, Date |
| Genetic Algorithms in Search, Optimisation & Machine Learning | D E Goldberg | Addison-Wesley, 1989 |
| Genetic Programming: On the Programming of Computers by Means of Natural Selection | J R Koza | MIT Press, 1992 |
| Genetic Programming: An Introduction | W Banzhaf, P Nordin, R E Keller & Frank D Francone | Morgan Kaufmann, 1999 |
| Handbook of Genetic Algorithms | L Davis (ed) | Van Nostrand Reinhold, 1991 |
| Genetic Algorithms + Data Structures = Evolution Programs (2nd ed) | Z Michalewicz | 1994 |
Detailed Syllabus
-
Natural and Artificial Evolution.
- How nature does it: theories of evolution, how natural selection works, cell, DNA, reproduction, growth
- Evolutionary Computation: motivations for simulating the process of evolution, nature-to-computer mapping, generic evolutionary algorithm.
- Genetic Algorithms.
- Introduction: simple generational GA, an example
- Solution encoding in GAs: Gray code in GAs, dynamic parameter encoding
- Selection in GAs: fitness proportionate selection, rank selection, tournament selection, elitist selection
- Crossover in GAs
- Mutation in GAs.
- Optimisation with Genetic Algorithms.
- Mapping objective functions into fitness functions
- GA for unconstrained optimisation: examples
- GAs for constrained optimisation
- GAs for combinatorial optimisation: solving TSP with standard GAs, position dependent representations, design of genetic operators, permutation-respecting crossover.
- Schemata in Genetic Algorithms.
- Introduction
- Geometric Interpretation of Schemata: fitness of schemata/hyperplanes, some observations on schemata
- Schema Theorem: effect of fitness proportionate selection on schemata, effect of one-point crossover on schemata, effect of mutation on schemata, schema theorem.
- Genetic Programming.
- Introduction
- Function set and terminal set in GP
- Initial population in GP
- Crossover in GP
- Mutation in GP
- Running Programs in GP
- Fitness functions in GP
- Symbolic Regression
- Automatically Defined Functions
- Classifier Systems
- Introduction
- Components of a CFS: message list, classifier list, input and output interfaces
- The CFS main cycle
- What CFSs can do: Default hierarchies
- Learning classifier systems: good and bad classifiers, the need for competition, quality of classifiers, adaption by credit assignment, adaption by rule discovery
- Main cycle of learning classifier systems.
Last updated: 29 July 2001
Source file: /internal/modules/COMSCI/2001/xml/02411.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus