Syllabus for |
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| FFR105 - Stochastic optimization algorithms |
| Stokastiska optimeringsmetoder |
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| Syllabus adopted 2021-02-26 by Head of Programme (or corresponding) |
| Owner: MPCAS |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Main field of study: Bioengineering, Chemical Engineering, Engineering Physics
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Department: 30 - MECHANICS AND MARITIME SCIENCES
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Course round 1
Teaching language: English
Application code: 11111
Open for exchange students: Yes
Block schedule:
D
Maximum participants: 200
| Module |
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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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27 Oct 2021 pm J, |
03 Jan 2022 am J, |
25 Aug 2022 am J |
In programs
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
MPAME APPLIED MECHANICS, MSC PROGR, Year 2 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Mattias Wahde
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Basic programming and courses in linear algebra and mathematical analysis or corresponding
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. Ethical aspects of machine learning
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 including compulsory elements
The examination is based on a written exam (50 %) and compulsory home problems (50 %).
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.