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Kursplan för

Läsår
FFR105 - Stochastic optimization algorithms
 
Kursplanen fastställd 2010-02-25 av programansvarig (eller motsvarande)
Ägare: MPCAS
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Bioteknik, Kemiteknik, Teknisk fysik
Institution: 16 - FYSIK


Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: D

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0199 Tentamen 7,5 hp Betygskala: TH   7,5 hp   25 Okt 2012 em V,  17 Jan 2013 em M,  27 Aug 2013 fm V

I program

MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Årskurs 1 (obligatorisk)
TKITE INFORMATIONSTEKNIK, CIVILINGENJÖR, Årskurs 3 (obligatoriskt valbar)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Årskurs 2 (valbar)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Årskurs 2 (valbar)

Examinator:

Professor  Mattias Wahde



Behörighet:

För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

Programming, basic engineering mathematics.

Syfte

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.

Lärandemål (efter fullgjord kurs ska studenten kunna)


  • 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.

Innehåll

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.

Litteratur

Wahde, M. Biologically inspired optimization methods: An introduction

Examination

The examination is based on a written exam and home problems.


Sidansvarig Publicerad: on 24 jan 2018.