Evolutionary Algorithms and Swarm-based Optimization Methods

Lecture in Winter 2017/2018

Overview

• Description
• Times and Rooms
• Lecturer
• Exam
• Lecture Slides
• Exercise Sheets
• Literature

Description

On this web page you can find information about the lecture "Evolutionary Algorithms and Swarm-based Optimization Methods" that is given by Christian Borgelt in winter 2017/2018 at the University of Konstanz. This page will be updated in the course of the semester.

Type of activity:		Lecture/Exercise Lesson
Semester week hours:		2+1
Credits:		5
Language:		German or English according to preference
Requirements:		Basic knowledge in algorithms and data structures as well as mathematics
Certificate:		Oral exam (20—30 minutes), see Exam

Contents:

• Biological background / Metaheuristics
• Elements of Evolutionary Algorithms: encoding, fitness and selection, genetic operators
• Fundamental Evolutionary Algorithms: genetic algorithms, evolution strategies, genetic programming, multi-criteria optimization, special techniques
• Computational Swarm Intelligence: basic principles, particle swarm optimization, ant-colony optimization

Learning Objectives:

• Knowledge of the basic principles of evolutionary algorithms and swarm-based optimization methods
• Fields of application of evolutionary algorithms and swarm-based optimization methods
• Strength and limitations of evolutionary algorithms and swarm-based optimization methods
• Understanding of the advantages and disadvantages of the different methods relative to the problem to be solved
• Ability to choose a proper metaheuristic to apply to a given problem

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Times and Rooms

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Lecturer

If you have questions about the lecture or the exercises, please refer (by email or in person in office Z811) to:

• Christian Borgelt, christian@borgelt.net

There are no formal office hours. Simply check whether the office door is open (Z811)!

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Exam

Exam Admission

The lecture is accompanied by exercise sheets. At the beginning of each exercise lesson, the exercises are voted for. By voting for an exercise, one expresses one's willingness to present something about it. (Suggestions for a solution will be discussed, they need not be correct right away.) To the exam will be admitted, who

• voted for at least half of the exercises and
• presented something for at least two exercises.

Exams

The exam will be held orally (20—30 minutes).

The exam date can be scheduled freely with the lecturer.

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Lecture Slides

The lecture slides are under construction and will be made available here as soon as a first English version is completed.

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Exercise Sheets

Here you can find all already prepared exercise sheets as well as the corresponding earliest exercise lesson date at which these exercise sheets will be discussed. However, the exercise sheets may, depending on how the exercise lessons proceed, be (partially) discussed later than the stated date.

• Exercise Sheet 1: ea01_eng.pdf ea01_eng.tex (from 2017.11.09)
• Exercise Sheet 2: ea02_eng.pdf ea02_eng.tex (ab 2017.11.23)
• Exercise Sheet 3: ea03_eng.pdf ea03_eng.tex (ab 2017.12.07)
• Exercise Sheet 4: ea04_eng.pdf ea04_eng.tex (ab 2017.12.21)
• Exercise Sheet 5: ea05_eng.pdf ea05_eng.tex (ab 2018.01.18)
• Exercise Sheet 6: ea06_eng.pdf ea06_eng.tex (ab 2018.02.01)

Zusätzliches Material:

• Programms to solve Exercise 8: queens.c qea.c
  Linux executables: queens qea
  Windows executables: queens.exe qea.exe
• Programs for genetic programming (stimulus response agent):
  sra-gp.scm (Scheme)
  sra-gp.el (Emacs Lisp)
  sra-gp.c (C)
  

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Literature

• Computational Intelligence - A Methodological Introduction
  Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim and Matthias Steinbrecher
  Springer-Verlag, Berlin/Heidelberg, Germany 2016, ISBN 978-1-4471-5012-1
  (2nd edition 2016, 564 pages, in English)
• Computational Intelligence - Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze
  Rudolf Kruse, Christian Borgelt, Christian Braune, Frank Klawonn, Christian Moewes, und Matthias Steinbrecher
  Springer-Vieweg, Berlin/Heidelberg, Germany 2015, ISBN 978-3-658-10904-2
  (2. Auflage 2015, 515 pages, in German)

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