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Syllabus for

Academic year
FFR105 - Stochastic optimization algorithms
Stokastiska optimeringsmetoder
 
Syllabus adopted 2020-02-20 by Head of Programme (or corresponding)
Owner: MPCAS
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
Department: 30 - MECHANICS AND MARITIME SCIENCES


Teaching language: English
Application code: 11115
Open for exchange students: Yes
Block schedule: D
Maximum participants: 200

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0199 Examination 7,5c Grading: TH   7,5c   28 Oct 2020 pm J   04 Jan 2021 am J,  26 Aug 2021 am J

In programs

MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
MPAME APPLIED MECHANICS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
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:

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 %).


Published: Mon 28 Nov 2016.