Melola Discovery

← Melola Discovery

Journal SINTA 1

Fodder composition optimization using modified genetic algorithm

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 7 No. 1 (2019) · pp. 68–75

DOI
10.52549/ijeei.v7i1.461
ISSN
2089-3272, 2089-3272
Published
2019-03-25
Publisher
IAES Indonesian Section
Language
en

Abstract

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

Keywords

Collaboration network of the authors

40 authors · 67 co-authorship links · 12 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Institut Teknologi Kalimantan
  • State University of Malang
  • Universitas Merdeka Malang
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yusuf Priyo AnggodoUniversity of Brawijaya3
Asyrofa RahmiUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Adyan Nur AlfiyatinUniversity of Brawijaya2
Kuncahyo Setyo NugrohoUniversity of Brawijaya2
Qoirul KotimahUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity of Brawijaya1
Ervin YohannesUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Muchammad Chandra Cahyo UtomoInstitut Teknologi Kalimantan1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
Gayatri Dwi SantikaUniversity of Brawijaya1
Agus NabaUniversity of Brawijaya1
Aji Prasetya WibawaState University of Malang1
Nadia Roosmalita SariUniversity of Brawijaya1
Elta SonalithaUniversitas Merdeka Malang1
Gusti Ahmad Fanshuri AlfarisyUniversity of Brawijaya1
Muhammad Halim NatsirUniversity of Brawijaya1
Agung Mustika RizkiInstitut Teknologi Sepuluh Nopember1
Gusti Eka YuliastutiUniversity of Brawijaya1
Andi HamdianahUniversity of Brawijaya1
Hilman TaufiqUniversity of Brawijaya1
Aprilia Nur FauziyahUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Fodder composition optimization using modified genetic algorithm</dc:title>
	<dc:creator xml:lang="en">Wijayaningrum, Vivi Nur</dc:creator>
	<dc:creator xml:lang="en">Mahmudy, Wayan Firdaus</dc:creator>
	<dc:subject xml:lang="en">Fodder composition</dc:subject>
	<dc:subject xml:lang="en">Genetic algorithm</dc:subject>
	<dc:subject xml:lang="en">Livestock</dc:subject>
	<dc:subject xml:lang="en">Numerical methods</dc:subject>
	<dc:description xml:lang="en">Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2019-03-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/461</dc:identifier>
	<dc:identifier>10.52549/ijeei.v7i1.461</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 7 No. 1: March 2019; 68-75</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/461/pdf</dc:relation>
</oai_dc:dc>