|Taken By||ACS, MEng CS 4, MEng CS/Phil 4, MEng CSAI 4, MEng CSBES 4, MEng CSESE 4, MEng CSSE 4, MEng CSYS 4, MMath 4|
|Number of Credits||10|
Open Assessment [100%]
26th Oct → 25th Jan
Feedback: 22nd Feb
Knowledge of Python would be an aid to students, as the workshops will make use of it.
This module provides a foundation of theoretical and practical knowledge in the subject of evolutionary systems; that is, systems that embody algorithms inspired by natural evolutionary systems (e.g. genetic algorithms, genetic programming, evolutionary strategies, and co-evolutionary frameworks) to evolve solutions to problems.
Synthesis: Students will learn how to use biological knowledge to inspire the development of natural computation approaches.
Application: the practical sessions will prepare students for designing and coding their own system. During the assessment they will develop their own system to address a complex problem.
Analysis & Evaluation: Students will be expected to evaluate and develop the performance of their system, and critically and correctly evaluate their implementation.
Familiarity with the range of evolutionary algorithms in existence, and their biological underpinnings.
Understanding of the underlying principles, their performance and behaviour, and the various computational applications, of the various algorithms.
Understanding of how to set about applying evolutionary computation approaches to problems in an informed manner.
The following topics will be covered in this module:
|++||Banzhaf et al||Genetic Programming: An Introduction||Morgan Kaufmann||1999|
|++||Eiben & Smith||Introduction to Evolutionary Computing||Springer||2003|
|++||M. Mitchell||An Introduction to Genetic Algorithms||MIT Press||1998|
|+||D. Goldberg||Genetic Algorithms in Search, Optimisation & Machine Learning|
Last updated: 23rd June 2016