Hochschule Darmstadt - Fb Informatik

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Modulbeschreibung
Course:Genetic Algorithms
Attached to module:
Genetische Algorithmen deutsch 30.2280
Module numbers:30.2536 [PVL 30.2537]
Language:english
Study programme:Bachelor 2021 - Wahlpflichtkatalog I
Bachelor dual KITS 2021 - Wahlpflichtkatalog I
Bachelor dual KoSI 2021 - Wahlpflichtkatalog I
Bachelor KMI 2021 - Wahlpflichtkatalog I
Bachelor 2014 - Katalog I: Anwendungs- und systemorientierte Module
Bachelor dual KoSI 2014 - Katalog I: Anwendungs- und systemorientierte Module
Bachelor KMI 2014 - Katalog I: Anwendungs- und systemorientierte Module
Bachelor 2007 - Vertiefung AE: Application Engineering
Bachelor 2007 - Vertiefung TI: Technische Informatik
Bachelor 2007/2004/2002/99 - Wahlpflichtfächer aus dem Informatikbereich
KoSI 2007 - Vertiefung AE: Application Engineering
KoSI 2007 - Vertiefung TI: Technische Informatik
KoSI 2007/2004/2003/2002/99 - Wahlpflichtfächer aus dem Informatikbereich
Type of course:V+P = Lecture+Practical
Weekly hours:2+2
Credit Points:5
Exam:written exam
Registering for examexplicitly and independent of booking
PVL (e.g. Practical):not graded
not graded.
Successful participation in the laboratory.The successful participation in the laboratory consists of implementing a genetic algorithm. The genetic operators for mutation and recombination, as well as the fitness proportionate and rank based selection must be implemented, and the suitability of the algorithm must be shown with the help of test instances.
Frequency of offering:(lastly in SS 2020)
Required knowledge:basic bachelor-level programming skill (C++ or Java)
Learning objectives:
  • Knowledge
    • The students understand the structure of algorithms which rely on the the concept of evolution.
  • Skills
    • In the laboratory the students have learned to implement a genetic algorithm to solve an underlying search or optimization problem.
  • Competencies
    • The students have learned, how to solve optimization, search, and other problems with genetic algorithms and know how to deal with problem specific challenges.
Content:
  • Required key concepts from biology, such as evolution, chromosome, genotype, phenotype, etc..
  • The structure of a genetic algorithm and genetic operators.
  • Differences between genetic algorithms and other heuristics, such as hill climbing, simulated annealing, etc..
  • The theory behind genetic algorithms (schema theorem, implicit parallelism, etc.).
  • Practical applications for genetic algorithms and specialized genetic operators.
  • Genetic Programming as an advanced branch of genetic algorithms.
Literature:
  • M. Mitchell: An lntroduction to Genetic Algorithms, MIT Press, 1996
  • Z. Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, 3rd edition, 1999
  • D. E. Goldberg : Genetic Algorithms in Search, Optimization and Machine Learning , Addison-Wesley 1989
  • W. Banzhaf et al .: Genetic Programming, Morgan Kaufmann Publishers , 1998
  • K. O. Stanley, J. Lehman: Why Greatness Cannot Be Planned, Springer Verlag, 2015
  • Various publications from scientific journals
Responsibility:Alexander del Pino
Released:WS 2014/2015

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