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Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2008 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

Genetic algorithms are commonly used to solve combinatorial optimization problems. The implementation evolves using genetic operators (crossover, mutation, selection, etc.). Anyway, genetic algorithms like some other methods have parameters (population size, probabilities of crossover and mutation) which need to be tune or chosen. In this paper, our project is based on an existing hybrid genetic algorithm working on the multiprocessor scheduling problem. We propose a hybrid Fuzzy- Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. The algorithm consists in adding a fuzzy logic controller to control and tune dynamically different parameters (probabilities of crossover and mutation), in an attempt to improve the algorithm performance. For this purpose, we will design a fuzzy logic controller based on fuzzy rules to control the probabilities of crossover and mutation. Compared with the Standard Genetic Algorithm (SGA), the results clearly demonstrate that the FLGA method performs significantly better.

Place, publisher, year, edition, pages
Borlänge, 2008. , 59 p.
Keyword [en]
fuzzy logic, genetic algoithms, combinatorial optimization
Identifiers
URN: urn:nbn:se:du-3286OAI: oai:dalea.du.se:3286DiVA: diva2:518421
Uppsok
Technology
Supervisors
Available from: 2008-06-12 Created: 2008-06-12 Last updated: 2012-04-24Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf