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Course: Evolutional Algorithms

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Course title Evolutional Algorithms
Course code FES/AEVA
Organizational form of instruction no contact
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 10
Language of instruction English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
Course content
1. Fundamentals of evolutionary algorithms - from Darwin to present 2. Genetic algorithms 2.1. Representation of individuals 2.2. Selection mechanisms 2.3. Genetic operators 2.4. Reproduction strategies 2.5. Theoretical background 3. Hybrid genetic algorithms 4. Application of genetic algorithms 4.1. Combinatorial optimisation 4.2. Multicriteria optimisation 4.3. Scheduling problems 4.4. Transportation and distribution problems 5. Genetic programming and its applications

Learning activities and teaching methods
unspecified
Learning outcomes
To introduce our students into the dynamically growing area of softcomputing that is composed by evolutionary algorithms. Besides the theoretical concepts and description of fundamental principles of evolutionary computing, the close attention will be paid to various application opportunities of these algorithms when solving hard problems.

Prerequisites
unspecified

Assessment methods and criteria
unspecified
Recommended literature
  • Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D. Genetic Programming. An Introduction. On the Automatic Evolution of Computer Programs and Its Applications.. San Francisco, CA: Morgan Kaufmann, 1998.
  • Coley, A.D. An Introduction to Genetic Algorithms for Scientist and Engineers.. World Scientific, Singapore, 1999.
  • Eiben, A.E., Smith, J.E. Introduction to Evolutionary Computing.. Springer-Verlag, Berlin, 2003.
  • Gen, M., Cheng, R. Genetic Algorithms&Engineering Optimization.. John Wiley&Sons, Chichester, 2000.
  • Gottlieb, J. Evolutionary Algorithms for Constrained Optimization Problems.. Shaker Verlag, Aachen, 2000.
  • Hromkovič, J. Algorithmics for Hard Problems (2nd Edition).. Springer-Verlag, Berlin, 2003.
  • Koza, J. R. Genetic Programming II. Automatic Discovery of Reusable Programs.. Cambridge, MA: MIT Press, 1994.
  • Koza, J. R. Genetic Programming. On the Programming of Computers by Means of Natural Selection.. Cambridge, MA: MIT Press, 1992.
  • Kvasnička, V., Pospíchal, J., Tiňo, P. Evolučné algoritmy.. STU Bratislava, 2000.
  • Lažanský, J. Evoluční výpočetní techniky. In Mařík V., Štěpánková O., Lažanský J. a kol.: Umělá inteligence 3.. Academia, Praha, 2001.
  • Michalewicz, Z., Fogel, B.D. How to Solve It: Modern Heuristics.. Springer-Verlag, Berlin, 2000.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics within Public Administration (2013) Category: Economy - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics in Public Administration (2014) Category: Economy - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics within Public Administration (2013) Category: Economy - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Economics and Administration Study plan (Version): Informatics in Public Administration (2014) Category: Economy - Recommended year of study:-, Recommended semester: -
University of Pardubice, date of update: 30.05.2024 23:50.