See module specification for other years:
2022-232023-24
Module summary
This module introduces a range of biologically-inspired approaches to computing.
Module will run
Occurrence
Teaching period
A
Semester 2 2024-25
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
Design and implement evolutionary systems to address a given problem.
Understand the biological underpinnings of evolutionary algorithms, and use them to optimise mathematical functions and agent behaviours.
Define a range of agent behaviours and represent them in a form that is well suited to natural selection
Model processes in populations of agents using hand-written mathematical models
Critically evaluate the performance and implementation of evolutionary and multi agent systems.
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