Studying at York
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 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 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
++ Banzhaf et al, Genetic Programming: An Introduction, Morgan Kaufmann , 1999
++ M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1998