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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
FFR105 - Stochastic optimization algorithms
 
Syllabus adopted 2014-02-12 by Head of Programme (or corresponding)
Owner: MPCAS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
Department: 16 - PHYSICS


Teaching language: English
The course is open for exchange students
Block schedule: D

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0199 Examination 7,5c Grading: TH   7,5c   29 Oct 2014 pm M,  05 Jan 2015 pm M,  27 Aug 2015 am M

In programs

MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)
TKITE SOFTWARE ENGINEERING, Year 3 (compulsory elective)

Examiner:

Professor  Mattias Wahde

Course evaluation:

http://document.chalmers.se/doc/c8b17434-df2e-41d1-b5c5-c0a3e3f2d55c


Homepage missing

 

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.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016