Evolutionary & Adaptive Computing - COM00037H
Module summary
This module introduces a range of biologically-inspired approaches to computing.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2025-26 |
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
Eiben, Agoston E., Smith, J. E., Introduction to evolutionary computing, Second edition., Berlin, Springer, 2015.
M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1998