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Abstract: <br /> <br /> <br /> <br /> <br /> Layouting problems are a common problem in our life. Introducing new item will increase the problem complexity because it will multiply the variation of possible layout in order to optimize space used for all items. &...

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Main Author: Syafri Tuloli (NIM 235 05 027) , Mohamad
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/8634
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:8634
spelling id-itb.:86342017-09-27T15:37:09Z#TITLE_ALTERNATIVE# Syafri Tuloli (NIM 235 05 027) , Mohamad Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/8634 Abstract: <br /> <br /> <br /> <br /> <br /> Layouting problems are a common problem in our life. Introducing new item will increase the problem complexity because it will multiply the variation of possible layout in order to optimize space used for all items. <br /> <br /> <br /> <br /> <br /> This thesis implements genetic algorithm as a method to solve layouting problems. Genetic algorithm operators used in this thesis are roulette wheel selection method, single-point crossover method, and binary mutation method. This thesis proves that genetic algorithm implementation used to solve layouting problems capable to archieve a 84% of solution quality for heterogen item cases and 100% for homogen item cases. <br /> text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Abstract: <br /> <br /> <br /> <br /> <br /> Layouting problems are a common problem in our life. Introducing new item will increase the problem complexity because it will multiply the variation of possible layout in order to optimize space used for all items. <br /> <br /> <br /> <br /> <br /> This thesis implements genetic algorithm as a method to solve layouting problems. Genetic algorithm operators used in this thesis are roulette wheel selection method, single-point crossover method, and binary mutation method. This thesis proves that genetic algorithm implementation used to solve layouting problems capable to archieve a 84% of solution quality for heterogen item cases and 100% for homogen item cases. <br />
format Theses
author Syafri Tuloli (NIM 235 05 027) , Mohamad
spellingShingle Syafri Tuloli (NIM 235 05 027) , Mohamad
#TITLE_ALTERNATIVE#
author_facet Syafri Tuloli (NIM 235 05 027) , Mohamad
author_sort Syafri Tuloli (NIM 235 05 027) , Mohamad
title #TITLE_ALTERNATIVE#
title_short #TITLE_ALTERNATIVE#
title_full #TITLE_ALTERNATIVE#
title_fullStr #TITLE_ALTERNATIVE#
title_full_unstemmed #TITLE_ALTERNATIVE#
title_sort #title_alternative#
url https://digilib.itb.ac.id/gdl/view/8634
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