Evolutionary Computation - COM00071M

« Back to module search

  • Department: Computer Science
  • Module co-ordinator: Dr. Daniel Franks
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2017-18

Module will run

Occurrence Teaching cycle
A Autumn Term 2017-18

Module aims

Evolutionary computation is an optimisation technique inspired by biological evolution. This module provides a foundation of both theoretical and practical knowledge on the subject of evolutionary computation. Students will have plenty of hands-on experience of implementing a number of types of evolutionary algorithms using Python and the library DEAP: Distributed Evolutionary Algorithms in Python, to solve a number of different types of problems.

Module learning outcomes

  • Synthesis: Students will learn how to use biological knowledge to inspire the development of natural computation approaches.
  • Application: the practical sessions will prepare students for designing and coding their own system in DEAP. During the assessment they will develop their own system to address a complex problem.
  • Analysis & Evaluation: Students will be expected to evaluate and develop the performance of their system, and critically and correctly evaluate their implementation.
  • Familiarity with the range of evolutionary algorithms in existence, and their biological underpinnings.
  • Understanding of the underlying principles, their performance and behaviour, and the various computational applications, of the various algorithms.
  • Understanding of how to set about applying evolutionary computation approaches to problems in an informed manner.

Assessment

Task Length % of module mark
Essay/coursework
Open Assessment
N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Open Assessment
N/A 100

Module feedback

Students will receive formative feedback throughout the workshops, and full summative written feedback following the open assessment.

Indicative reading

DEAP User guide: http://deap.readthedocs.io/en/master/

++ Banzhaf et al, Genetic Programming: An Introduction, Morgan Kaufmann , 1999

++ M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1998

+ D. Goldberg, Genetic Algorithms in Search, Optimisation & Machine Learning



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.