The descriptions are for modules currently being taught. They should be viewed as an example of the modules we provide. All modules are subject to change for later academic years.

Evolutionary Computation (EVCO) 2011/2

Workload - Private Study - Assessment - Description - Learning Outcomes - Content - Teaching Materials - Recommended Books

Module Code COM00071M
Lecturers John Clark
Taken By LSCITS 1, LSCITS 2, LSCITS 3, LSCITS 4, MEng CSESE 4, MEng CSSE 4, MMath 4, NC
Number of Credits 10
Part A
Teaching Autumn 2-5
Open Assessment [100%] Aut/5/Wed -> Spr/1/Wed
Feedback: Spr/5/Wed

Workload

  • Lectures: 16 x 1hr
  • Practicals: 8 x 1hr
  • Private Study: 36 x 1hr
  • Assessment: 40 x 1hr

Assessment

Open Assessment

Description

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).

Learning Outcomes

• 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.

Content

The following topics will be covered in this module:

• Biological evolution
• Search spaces, fitness landscapes, fitness functions
• Encodings and representations
• Genetic Algorithms: crossover, mutation, selection
• Genetic Programming
• Schema theorem and building block hypothesis
• Convergence, Co-evolution, niching
• Multi-objective EAs
• Applications

Recommended Books

Rating Author Title Publisher Year
++ 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
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Last updated: 20th April 2012