Syllabus for |
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| FFR105 - Evolutionary computation |
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| Owner: FCMAS |
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5,0 Credits (ECTS 7,5) |
| Grading: TH - Five, Four, Three, Not passed |
| Level: C |
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Department: 16 - PHYSICS
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Teaching language: English
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
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No Sp |
| 0199 |
Examination |
5,0 c |
Grading: TH |
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5,0 c
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Contact examiner |
In programs
TTFYA ENGINEERING PHYSICS, Year 4 (elective)
FCMAS MSc PROGRAMME IN COMPLEX ADAPTIVE SYSTEMS, Year 1 (compulsory)
TITEA SOFTWARE ENGINEERING, Year 3 (elective)
Examiner:
Professor
Mattias Wahde
Eligibility:
For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.
Course specific prerequisites
Computer science adv. course or equivalent
Aim
The course provides basic knowledge of new methods in computer science inspired by evolutionary processes in nature, such as genetic algorithms, genetic programming, and artificial life. These are both relevant to technical applications, for example in optimization and design of autonomous systems, and for understanding biological systems, e.g., through simulation of evolutionary processes.
Content
The course consists of the following topics:
- Evolutionary algorithms. Fundamentals of genetic algorithms, representations, genetic operators, selection mechanisms. Theory of genetic algorithms. The schema theorem and extensions. Genetic programming: representation and genetic operators.
- Applications of evolutionary algorithms: Optimization problems. Data mining. Evolving neural networks. Design of autonomous systems. Cognitive models, such as classifier systems; the credit assignment problem.
- Artificial life: Self-organization in evolutionary processes. Game theory and multi-agent systems. Models of coevolution. Collective behavior.
The course consists of lectures and a large project which includes both model buildning and programming, where students use the techniques from the course to study an application problem.
Literature
Melanie Mitchell, Introduction to genetic algorithms, MIT Press, 1996. Wolfgang Banzhaf, Peter Nordin, Robert E Keller, Frank D. Francone, Genetic Programming - An Introduction, Morgan Kaufmann, 1997. Articles from the research literature.
Examination
Through homework problems during the course, a written report on a major project, and an oral exam.