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Genetic Algorithms: Comparing Evolution With and Without a Simulated Annealing-inspired Selection
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Genetiska algoritmer : En jämförelse av evolution med och utan simulerad härdning i urvalet (Swedish)
Abstract [en]

The Genetic Algorithm (GA) is an interesting problem solving algorithm which takes inspiration from evolution in order to self-improve and reach good solutions to problems by reproduction and mutation. This thesis compares a GA with and without a Simulated Annealing (SA) inspired selection when it comes to solving three different instances of the Traveling Salesman Problem (TSP). SA was found to be able to help the GA reach better solutions, but the results also depended on other parameters within the GA itself.

Abstract [sv]

Den genetiska algoritmen (GA) är en intressant problemlösningsalgoritm som hämtat inspiration från evolutionen och förbättrar sig själv genom fortplantning och mutering. Denna uppsats jämför två implementationer av en GA. Den ena är en ren GA och den andra är samma GA med tillagd urvalsfunktionalitet inspirerad från simulerad härdning (SA). De båda algoritmerna jämfördes med varandra på att lösa tre olika instanser av handelsresandeproblemet (TSP). SA visade sig kunna hjälpa en GA att nå bättre resultat, men resultatet berodde även på andra parametrar inom GA:n

Place, publisher, year, edition, pages
2019. , p. 38
Series
TRITA-EECS-EX ; 2019:388
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259207OAI: oai:DiVA.org:kth-259207DiVA, id: diva2:1350811
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Examiners
Available from: 2019-09-13 Created: 2019-09-12 Last updated: 2019-09-13Bibliographically approved

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CiteExportLink to record
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  • apa
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