Evolutionary & Adaptive Computing - COM00177M

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  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: M
  • 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.

  • 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