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Evolutionary & Adaptive Computing - COM00037H

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  • Department: Computer Science
  • Module co-ordinator: Dr. Simon O'Keefe
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2023-24

Module summary

This module introduces a range of biologically-inspired approaches to computing.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

This module introduces a range of biologically-inspired approaches to computing. It provides a foundation of both theoretical and practical knowledge on the subject of evolutionary computation, an optimisation technique inspired by biological evolution. Students will have hands-on experience implementing a number of types of evolutionary algorithms using Python and the library DEAP: Distributed Evolutionary Algorithms in Python, to solve a range of different types of problems. The module also studies the use of Agents and Multi-agent Systems as a modelling paradigm, with a focus on evolutionary adaptation and learning.

Module learning outcomes

  • Use biological knowledge to inspire the development of natural computation approaches

  • Design and implement systems in DEAP to address a complex problem, and critically evaluate the performance of their system

  • Use a range of evolutionary algorithms, and understand their biological underpinnings.

  • Understand and modify an existing rule-based multi-agent system;

  • Define a range of agent behaviours and represent them in a form that is well suited to machine learning/selection

  • Apply a selected range of advanced evolutionary algorithms and machine learning techniques in the context of the intelligent (learning, evolving) agent paradigm

  • Evaluate the performance and implementation of multi agent systems

Assessment

Task Length % of module mark
Essay/coursework
Essay : Evolutionary and Adaptive Computing 1		 N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Essay : Evolutionary and Adaptive Computing 1		 N/A 100

Module feedback

Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines.

Indicative reading

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

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


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.