Syllabus for |
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FFR105 - Stochastic optimization algorithms
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| Syllabus adopted 2014-02-12 by Head of Programme (or corresponding) |
| Owner: MPCAS |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Fail |
| Education cycle: Second-cycle |
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Major subject: Bioengineering, Chemical Engineering, Engineering Physics
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Department: 30 - MECHANICS AND MARITIME SCIENCES
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Teaching language: English
Open for exchange students
Block schedule:
D
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0199 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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25 Oct 2017 pm M, |
19 Dec 2017 am M
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30 Aug 2018 am M |
In programs
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
TKITE SOFTWARE ENGINEERING, Year 2 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)
Examiner:
Professor
Mattias Wahde
Go to Course Homepage
Eligibility:
In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
Course specific prerequisites
Programming, basic engineering mathematics. .
Aim
The aim of the course is for the students to attain 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.
Learning outcomes (after completion of the course the student should be able to)
- Implement and use several different classical optimization methods, e.g. gradient descent and penalty methods.
- Describe and explain the basic properties of biological evolution, with emphasis on the parts that are relevant for evolutionary algorithms.
- Define and implement (using Matlab) different versions of evolutionary algorithms, particle swarm optimization, and ant colony optimization, and apply the algorithms in the solution of optimization problems.
- Compare different types of biologically inspired computation methods and identify suitable algorithms for a variety of applications.
Content
The course consists of the following topics:
- Classical optimization methods. Gradient descent. Convex functions. The lagrange multiplier method. Penalty methods.
- Evolutionary algorithms. Fundamentals of genetic algorithms, representations, genetic operators, selection mechanisms. Theory of genetic algorithms. Analytical properties of evolutionary algorithms. (Linear) genetic programming: representation and genetic operators.
- Particle swarm optimization. Fundamentals and applications.
- Ant colony optimization. Fundamentals and applications.
- Comparison of the different algorithms
Organisation
The course is organized as a series of lectures. Some lectures are devoted to problem-solving.
Literature
Wahde, M. Biologically inspired optimization methods: An introduction
Examination
The examination is based on a written exam and home problems.