Evolutionary & Adaptive Computing - COM00177M
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 complex problem.
- Understand the biological underpinnings of a range 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 both hand-written and data-driven mathematical models
- Evaluate and develop the performance of evolutionary and 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
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