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Evolutionary & Adaptive Computing - COM00177M

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  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2022-23

Module summary

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

Module will run

Occurrence Teaching period
A Spring Term 2022-23

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

  • Model processes in populations of agents using both hand-written and learned mathematical models

  • Evaluate and develop the performance of multi agent systems, and critically and correctly evaluate their implementation

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 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 constantly explores 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. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.