2110742 Evolutionary Computation
Prabhas Chongstitvatana (email me)
Time: Mon 13:00-14:30, Thurs 13:00-14:30
Room: meeting room, floor 20, Eng. building 4.
What's new
26 Jan 2004 Paper: No
Free Lunch Theorem
Application: GA experiment One-max
(executable file by Nathee)
20 Feb 2004 GA experiment (the missing
files ) : ex1 gp373w32
Outline
Evolutionary Computation is an approach to computation that emphasize on
a general-purpose search algorithm that use principles inspired by population
genetics to evolve solutions to problems. Two most well-known methods
are Genetic Algorithm (GA) and Genetic Programming (GP). GA
and GP has become increasingly popular in recent years as a method
for solving complex search problems in a large number of disciplines.
This course will illustrate the basic concept of GA/GP and their
current applications. The topics include Evolutionary Strategies (ES),
the method of real-value optimization, and Classifier System (CFS), one
of the most advanced study in Complex Adaptive System (CAS).
The content of the course will be a mixture of mathematical material
and up-to-date research materials. The lecture will be mainly the foundation
of the subject. Students are expected to do a number of self-study
on to answer the questions posted weekly. Those questions concern
the subject at a deeper level.
Weekly schedule
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Introduction to Evolutionary Computation
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Simple Genetic Algorithms
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Foudation of Genetic Algorithms : Schemata Theory
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Genetic Algorithms as Search in Hyperplane, k-arm bandit
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Niching and other genetic operators
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Genetic Algorithms Applications
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Genetic Programming
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Some examples of solving problems by GP
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Schemata theorem for GP
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Classifier systems
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Evolutionary strategy
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Social Intelligence
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Multiple objectives optimisation
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Current issues in GA/GP
Assesment
theory: written exam, mid/final 25/25
appliations: class discussion 25
practical: project 25
homework every week, no score (each takes 3-5 hours)
Text
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Goldberg, D., Genetic algorithms, Addison-Wesley, 1989.
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Holland, J. H., Adaptation in natural and artificial systems, MIT press,
1992.
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Vose, M., The simple genetic algorithm: foundation and theory, MIT press,
1999.
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Winter, G., Periaux, J., Galan, M., Cuesta, P. (eds), Genetic algorithms
in engineering and computer science, John Wiley, 1995.
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Mitchell, M., An introduction to genetic algorithms, MIT press, 1996.
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Koza, J., "Genetic Programming Vol 1, 2, 3", MIT Presss, 1992, 1994, 1999.
Uptodate handouts on various current researches will be distributed in
the class.
Resources
Assignments
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1 Dec 2003 Select a paper on GA application from conference
proceedings, write a summary for it.
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12 Jan 2004 Try the GA experiment, varying
parameters of the one-max problem, answer the question: What is the
relation ship between problem-size, population-size and convergence rate?
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20 Feb 2004 GA experiment (the missing files ) : ex1
gp373w32
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26 Jan 2004 Read the NFL paper
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last update 20 Feb 2004
P. Chongstitvatana