This module introduces a range of biologically-inspired approaches to computing.
Occurrence | Teaching period |
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A | Semester 2 2023-24 |
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.
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.
Understand and 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
Evaluate the performance and implementation of multi agent systems
Task | % of module mark |
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Essay/coursework | 100 |
None
Task | % of module mark |
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Essay/coursework | 100 |
Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines.
++ Banzhaf et al, Genetic Programming: An Introduction, Morgan Kaufmann , 1999
++ M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1998