Module 02411 (2013)

Module Description - Evolutionary Computation

The Module Description is a strict subset of the Syllabus Page, which gives more information

Module TitleEvolutionary Computation
SchoolComputer Science
Module Code06-02411
DescriptorCOMP/06-02411/LH
Member of StaffShan He
LevelH
Credits10
Semester2
Pre-requisitesNone
Co-requisitesNone
RestrictionsMay not be taken by anyone who has taken or is taking 06-22753 (Introduction to Evolutionary Computation).
Contact hours23
Delivery2 hrs per week; a combination of lectures and tutorials.
DescriptionEvolutionary computation is the study of computational systems that use ideas and get inspiration from natural evolution. Its techniques can be applied to optimisation, learning and design. Example topics covered in this module include natural and artificial evolution, evolutionary, chromosome representations, search operators, co-evolution, constraint handling techniques, niching and speciation, genetic programming, classifier systems and theoretical foundations.
Outcomes
On successful completion of this module, the student should be able to:Assessed by:
Demonstrate an understanding of the relations between the most important evolutionary algorithms presented in the module, new algorithms to be found in the literature now or in the future, and other search and optimisation techniques Examination
Demonstrate an understanding of the implementation issues of evolutionary algorithms Examination
Explain population based search methods inspired from physical systems, similarities with and differences from population based evolutionary systems, and ways of combining these Examination
Determine the appropriate parameter settings to make different evolutionary algorithms work well Examination
Design new evolutionary operators, representations and fitness functions for specific applications Examination
AssessmentSessional: 1.5 hr examination (100%).
Supplementary (where allowed): As the sessional assessment
TextsT. Baeck, D. B. Fogel, and Z. Michalewicz (eds.), Handbook on Evolutionary Computation, 1997
Z Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (3rd edition), 1996
Deb Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms, 2001
James C. Spall, Introduction to Stochastic Search and Optimization, 2003
W.R. Gilks, S. Richardson & D.J. Spiegelhalter , Markov Chain Monte Carlo in Practice , 1996
D E Goldberg, Genetic Algorithms in Search, Optimisation & Machine Learning, 1989
W Banzhaf, P Nordin, R E Keller & Frank D Francone, Genetic Programming: An Introduction, 1999
X Yao (ed), Evolutionary Computation: Theory and Applications, 1999
, Various articles in journals and conference proceedings,