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Title:IMPLEMENTACIJA GENETSKEGA ALGORITMA NA GRAFIČNEM PROCESORJU
Authors:ID Hauzer, Tomaž (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Guid, Nikola (Comentor)
Files:.pdf VS_Hauzer_Tomaz_2011.pdf (9,46 MB)
MD5: 70C84FDEB8A035F9E3D6E2BEC06FF6DA
PID: 20.500.12556/dkum/9c81655b-4e1e-47fd-a5a3-bff06d7ab41f
 
Language:Slovenian
Work type:Undergraduate thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo poskusili ugotoviti, kakšne pohitritve lahko dosežemo v izvajanju genetskega algoritma, če ga izvajamo na grafičnem procesorju računalnika. V obeh implementacijah, na CPU in GPU, uporabimo turnirsko selekcijo, križanje z delno preslikavo in vstavitveno mutacijo. Težimo seveda k čim večji pohitritvi na grafičnem procesorju. Najprej predstavimo genetski algoritem. Opišemo njegovo definicijo, zgodovino genetskih algoritmov in njihovo trenutno uporabo ter potek izvajanja genetskega algoritma. Sledi opis problema trgovskega potnika, nad katerim smo izvajali genetski algoritem. V nadaljevanju še opišemo grafični procesor in arhitekturo CUDA. Sledi razlaga implementacije genetskega algoritma. Implementirani genetski algoritem na grafičnem procesorju smo primerjali z implementacijo na centralnem procesorju in predstavimo rezultate.
Keywords:genetski algoritem, grafični procesor, CUDA, kromosom, gen, paralelno računanje, problem trgovskega potnika
Place of publishing:Maribor
Publisher:[T. Hauzer]
Year of publishing:2011
PID:20.500.12556/DKUM-17298 New window
UDC:519.876.5:004.42(043.2)
COBISS.SI-ID:15061526 New window
NUK URN:URN:SI:UM:DK:2RTCJWSU
Publication date in DKUM:14.02.2011
Views:2881
Downloads:231
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:IMPLEMENTATION OF GENETIC ALGORITHM ON GRAPHICS PROCESSOR
Abstract:In this thesis we will try to find out, what kind of speedups we can get if the genethic algorithm runs on the graphics processor. In both CPU implementation and GPU one, we use a tournament selection, partially-mapped crossovers and an insertion mutation. Of course we want to maximize the speedups on the graphics processor. First we present a genethic algorithm. We describe its definition, present its history and current use. We also consider the methodology of the genethic algorithm. Then we describe the travelling salesman problem, realised by the genethic algorithm. The next section provides the description of the graphics processor and CUDA architecture. Then we present our implementation of the genethic algorithm on GPU and we compare that implementation with the implementation on CPU. At the end results are presented.
Keywords:genethic algorithm, graphics processor, CUDA, genome, gen, parallel computing, traveling salesman problem


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