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Optimizing Genetic Algorithms for Time Critical Problems
Blekinge Institute of Technology, Department of Software Engineering and Computer Science.ORCID iD: 0000-0003-4875-391X
Blekinge Institute of Technology, Department of Software Engineering and Computer Science.
2003 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesisAlternative title
Optimering av genetiska algoritmer för tidskritiska system (Swedish)
Abstract [en]

Genetic algorithms have a lot of properties that makes it a good choice when one needs to solve very complicated problems. The performance of genetic algorithms is affected by the parameters that are used. Optimization of the parameters for the genetic algorithm is one of the most popular research fields of genetic algorithms. One of the reasons for this is because of the complicated relation between the parameters and factors such as the complexity of the problem. This thesis describes what happens when time constraints are added to this problem. One of the most important parameters is population size and we have found by testing a well known set of optimization benchmark problems that the optimal population size is not the same when time constraints were involved.

Abstract [sv]

Genetiska algoritmer har många egenskaper som gör dem till ett bra val när man ska lösa väldigt komplicerade problem. Prestandan för genetiska algoritmer påverkas av de parametrar som används. Optimering av parametrarna för genetiska algoritmer är ett av de mest populära forskningsområdena för genetiska algoritmer. En av anledningarna till detta är den komplexa relationen mellan parametrarna och faktorer så som komplexiteten av problemet. Detta arbete beskriver vad som händer när tidsfaktorn läggs till detta problem. En av de viktigaste parametrarna är populationsstorlek och vi har sett genom att testa en grupp med väl testade optimiseringsproblem att optimal populationsstorlek inte är samma när tidsfaktorn är inblandat.

Place, publisher, year, edition, pages
2003. , 42 p.
Keyword [en]
genetic algorithms, population size, real-time systems, Optimization
National Category
Computer Science
URN: urn:nbn:se:bth-4993Local ID: diva2:832349
Available from: 2015-04-22 Created: 2003-06-13 Last updated: 2016-09-20Bibliographically approved

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