Evolutionary Computation

Summary module description:

Aims:
The aim of this module is develop an understanding of the main types of evolutionary algorithms, their component features, and application to optimisation problems.

Assessable learning outcomes:
Students will gain knowledge of basic AI problem formulation and search. They will appreciate the distinctions between:
(i) systematic, stochastic and heuristic search techniques
(ii) different types of evolutionary algorithms
(iii) alternative methods for implementing the evolutionary operators, and
(iv) the main features of fitness landscapes. They will be able to recognise the type of problem suited to evolutionary algorithms, and be able to design and implement an appropriate solution.

Additional outcomes:
Outline content:
Evolutionary Computing Metaphor and Biological Inspiration
Components of Evolutionary Algorithms
Evolutionary Computation and Global Optimisation
Genetic Algorithms
Evolutionary Programming
Learning Classifier Systems
Parameter Control (Exploitation vs Exploration)
Multimodal problems
Mimetic Algorithms
Plus case studies and further reading to support the above.

Brief description of teaching and learning methods:
Lectures to provide notes on main learning areas to be supplemented by further reading and coursework tasks to provide demonstration of understanding of the main topic areas.

Summative Assessment Methods:
Written exam 50%
Set exercise 50%

Other information on summative assessment:

Formative assessment methods:

Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late, in accordance with the University policy.
where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Length of examination:
One 90-mins examination paper in May/June.

Requirements for a pass:
40%