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Syllabus for

Academic year
FFR105 - Evolutionary computation
 
Owner: FCMAS
5,0 Credits (ECTS 7,5)
Grading: TH - Five, Four, Three, Not passed
Level: A
Department: 0707 - Physical resource theory


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0199 Examination 5,0 c Grading: TH   5,0 c    

In programs

TTFYA ENGINEERING PHYSICS, Year 4 (elective)
TITEA INFORMATION ENGINEERING, Year 3 (elective)
FCMAS MSc PROGRAMME IN COMPLEX ADAPTIVE SYSTEMS, Year 1 (compulsory)

Examiner:




Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

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.


Page manager Published: Thu 03 Nov 2022.