Biologically Inspired Computation - ELE00017M

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  • Department: Electronic Engineering
  • Module co-ordinator: Prof. Stephen Smith
  • 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

  • To understand principles and techniques in bio-inspired computing, particularly, the techniques of evolutionary algorithms and genetic programming
  • To be able to use these techniques to solve computational problems
  • To complete a small research project (either practical or survey) and write a research paper
  • Introduction to genetic algorithms, evolutionary strategies, genetic programming, evolvable hardware, and evolutionary circuit design. Conventional logic optimisation versus evolutionary circuit design. Evolution of antennas. Cellular automata. Conventional fault tolerance and recovery with bio-inspired approaches. Artificial Immune systems. Swarms and swarm robotics.

Formative Feedback

Consultation with module teachers about possible research topics and practical research investigations (course tutors). Formative feedback and instruction in laboratories. Advice and consultation regarding data analysis and investigation presentations for practical investigation assignments.

Module learning outcomes

  • Design and write evolutionary algorithms to find solutions to search problems.
  • Compare the differences in conventional design with evolutionary design.
  • Compare critically, conventional and bio-inspired approaches to fault tolerant circuit design.
  • To be aware of, and make informed decisions using, the state-of-the-art biologically-inspired computation methods.

Assessment

Task Length % of module mark
Essay/coursework
Research Paper
N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Research Paper
N/A 100

Module feedback

Students will receive written feedback on their coursework within four weeks of submission.

Indicative reading

  • Mitchell, M, An Introduction to Genetic Algorithms, The MIT Press, 1998. ISBN 0-262-63185-7
  • Banzhaf, W, Genetic Programming. An Introduction, Morgan Kaufmann, 1997. ISBN 1-558-60510-X
  • Various current research papers are recommended during module.



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.