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Comparing Different Genetic Algorithms through Solving Steiner Networks.
KTH, School of Computer Science and Communication (CSC).
2012 (English)Independent thesis Advanced level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The purpose of this paper is to inspect the behavior of different genetic algorithms. Some different characteristics, of selection and reproduction of individuals, are implemented in a genetic algorithm and the results are then compared to see if some characteristics are more important than others. More specifically the reproduction methods mutation and crossover are compared and the selection methods elitism selection and biased random selection are compared. The problem, which the genetic algorithms are tested on, is called the Steiner network problem. The results indicates that using elitism selection together with mutations is the best method when solving trivial problems. For more difficult problems mixing mutations and crossovers and using biased random selection seems to be the best alternative though this result is not as certain as the fast convergence of elitism-mutation algorithms for trivial problems.

Abstract [sv]

I denna rapport undersöks olika sorters genetiska algoritmer. Olika sorters selektion och fortplantning av individer implementeras i en genetisk algoritm. Resultaten av användandet av dessa olika implementationer jämförs sedan för att se hur mycket de olika egenskaperna påverkar algoritmen. Fortplantningmetoder som tas upp är mutationer och blandningar av olika individer medan de selektionmetoder som tas upp är elitist-selektion och partisk slumpselektion. Problemet som den genetiska algorimen testas på kallas Steinernätverk. Enligt de resultat som tas fram är elitistselektion kombinerat med mutationer den bästa metoden för att lösa simpla problem. När det kommer till svårare problem verkar det bäst att använda både mutationer och blandningar av individer tillsammans med partisk slumpselektion.

Place, publisher, year, edition, pages
Kandidatexjobb CSC, K12060
National Category
Computer Science
URN: urn:nbn:se:kth:diva-131065OAI: diva2:654511
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2013-10-07 Created: 2013-10-07

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