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Simulating the evolution of silverfish: Evolution modelled as an evolutionary algorithm
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

An area of Artificial Intelligence, used for instance in optimization, is evolutionary algorithms. By using mechanisms similar to those that cause evolution, evolutionary algorithms can improve e.g. problem solving algorithms by artificial evolution.The purpose of this study was to show that it’s possible to simulate evolution by modelling it as an evolutionary algorithm. This was achieved by simulating the evolution of silverfish’ genes in two environments with the only difference of the presence of a threat. The results were considered to be successful as the majorityof the genes which were presumed to be important for survival changed in such away. The results could be repeated between simulations indicating that random change of the genes and deterministic factors in the environment shaped the genes of the silverfish and that after simulation the silverfish were optimally fit for the environment.

Abstract [sv]

Ett område inom Artificiell Intelligens som bl a används för optimering är evolutionära algoritmer. Genom att använda mekanismer liknande de som orsakar evolution så kan t ex algoritmer förbättras genom artificiell evolution. Syftet med den här studien var att visa att det är möjligt att simulera evolutionen genom att modellera den som en evolutionär algoritm. Detta gjordes genom att simulera evolutionen av silverfiskars gener i två miljöer vars enda skillnad var en förekomst av ett hot. Resultaten ansågs vara lyckade då merparten av de gener som förmodades vara viktiga för överlevnad förändrades på ett sådant sätt. Resultaten kunde upprepas mellan simuleringar vilket indikerar att genom stokastisk förändring av gener och deterministiska faktorer i miljön så optimerades silverfiskarnas gener för att göra det möjligt att överleva i respektive miljö.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157546OAI: oai:DiVA.org:kth-157546DiVA: diva2:770624
Examiners
Available from: 2014-12-11 Created: 2014-12-11 Last updated: 2014-12-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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