FOGA: Flag Optimization with Genetic Algorithm

Tagtekin, Burak; Höke, Berkan; Sezer, Mert Kutay; Öztürk, Mahiye Uluyağmur

doi:10.1109/inista52262.2021.9548573

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Algoritma genetika merupakan algoritma yang secara otomatis dapat melakukan penentuan sumber daya yang digunakan untuk dapat meminimalkan waktu yang diperlukan [11].…”

Section: Algoritma Genetikaunclassified

Pengimplementasian Algoritma Genetika Dalam Sistem Penjadwalan Praktikum

Nazarius,

Delon Pratama,

Aryapati Soebagijo

et al. 2024

jati

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Penelitian ini melakukan pengimplementasian algoritma genetika pada sistem penjadwalan praktikum untuk mencapai solusi yang akurat dan efisien. Proses inisialisasi populasi dilakukan dengan menghasilkan kromosom yang berisi gen-gen yang merepresentasikan kombinasi data mata kuliah, asisten praktikum, jam, hari, dan ruangan. Evaluasi fitness dilakukan dengan menggunakan rumus Fitness Poin = 1/(1 + Penalty) untuk menentukan seberapa baik suatu individu dalam memenuhi kriteria yang telah ditetapkan. Percobaan menunjukkan kromosom terbaik bernilai fitnes = 1, dengan populasi, generasi, dan tingkat mutasi yang semakin besar akan membutuhkan waktu eksekusi yang lebih lama dan sebaliknya. Juga ukuran populasi, generasi dan tingkat mutasi juga mempengaruhi kemungkinan untuk ditemukan hasil yang optimal. Hasil penelitian ini didapatkan bahwa algoritma genetika efektif dalam proses penyelesaian permasalahan penjadwalan praktikum yang kompleks, memberikan jadwal praktikum yang akurat dan efisien. Implikasi penelitian ini dapat menjadi acuan penting dalam pengembangan algoritma genetika khususnya dalam bidang penjadwalan praktikum

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“…Algoritma genetika merupakan algoritma yang secara otomatis dapat melakukan penentuan sumber daya yang digunakan untuk dapat meminimalkan waktu yang diperlukan [11].…”

Section: Algoritma Genetikaunclassified

Pengimplementasian Algoritma Genetika Dalam Sistem Penjadwalan Praktikum

Nazarius,

Delon Pratama,

Aryapati Soebagijo

et al. 2024

jati

View full text Add to dashboard Cite

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“…The great majority of studies and technological activities are centered on this second performance objective, which has been referred to as optimization. The objective was mislabeled because, until recently, most people rejected obtaining an ideal translation as a difficult and impractical task [3].Compilers are now being improved so that every code block in a programmed may be transformed into an efficient application [4]. Traditional compiler optimization seems to be a difficult process with no assurances of producing the most effective and quickest target code [5].A compiler enables a multitude of code optimizations that could be activated or disabled via a compilation flag in order to enhance the throughput of compiled applications.…”

Section: Introductionmentioning

confidence: 99%

Compiler Optimization Prediction with New Self-Improved Optimization Model

Shewale¹,

Shinde²,

Gurav³

et al. 2023

IJACSA

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Users may now choose from a vast range of compiler optimizations. These optimizations interact in a variety of sophisticated ways with one another and with the source code. The order in which optimization steps are applied can have a considerable influence on the performance obtained. As a result, we created a revolutionary compiler optimization prediction model. Our model comprises three operational phases: model training, feature extraction, as well as model exploitation. The model training step includes initialization as well as the formation of candidate sample sets. The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features. These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features. In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.

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