ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM

Tarık Üveys Şen; Mehmet Gökhan Bakal

doi:10.17780/ksujes.1475168

EN TR

ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM

Abstract

Deep learning has shown remarkable success in various applications, such as image classification, natural language processing, and speech recognition. However, training deep neural networks is challenging due to their complex architecture and the number of parameters required. Genetic algorithms have been proposed as an alternative optimization technique for deep learning, offering an efficient alternative way to find an optimal set of network parameters that minimize the objective function. In this paper, we propose a novel approach integrating genetic algorithms with deep learning, specifically LSTM models, to enhance performance. Our method optimizes crucial hyper-parameters including learning rate, batch size, neuron count per layer, and layer depth through genetic algorithms. Additionally, we conduct a comprehensive analysis of how genetic algorithm parameters influence the optimization process and illustrate their significant impact on improving LSTM model performance. Overall, the presented method provides a powerful mechanism for improving the performance of deep neural networks, and; thus, we believe that it has significant potential for future applications in the artificial intelligence discipline.

Keywords

GENETİK ALGORİTMA DESTEKLİ BİR YAKLAŞIM İLE DERİN ÖĞRENME PERFORMANSININ GELİŞTİRİLMESİ: LSTM ODAKLI

Öz

Derin öğrenme, görüntü sınıflandırma, doğal dil işleme ve konuşma tanıma gibi çeşitli uygulamalarda dikkat çekici başarılar elde etmiştir. Ancak, derin sinir ağlarını eğitmek, karmaşık mimarileri ve gereken parametre sayısı nedeniyle zorlu bir süreçtir. Genetik algoritmalar, derin öğrenme için alternatif bir optimizasyon teknik olarak önerilmiştir ve optimal bir ağ parametre setini minimize eden bir amaç fonksiyonu bulmak için etkili bir alternatif yöntem sunar. Bu makalede, derin öğrenme ile genetik algoritmaları entegre eden, özellikle LSTM modellerini kullanarak performansı artırmayı amaçlayan yeni bir yaklaşım öneriyoruz. Yöntemimiz, genetik algoritmalar aracılığıyla öğrenme hızı, grup boyutu, katman başına nöron sayısı ve katman derinliği gibi kritik hiper-parametreleri optimize eder. Ayrıca, genetik algoritma parametrelerinin optimizasyon sürecini nasıl etkilediğine dair kapsamlı bir analiz yaparak, LSTM model performansını iyileştirmedeki önemli etkilerini gösteriyoruz. Genel olarak, sunulan yöntem, derin sinir ağlarının performansını artırmak için güçlü bir mekanizma sunmakta olup bu nedenle yapay zekâ disiplininde gelecekteki uygulamalar için önemli bir potansiyele sahip olduğuna inanıyoruz.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Derin Öğrenme , Nöral Ağlar , Evrimsel Hesaplama

Bölüm

Araştırma Makalesi

Yazarlar

Tarık Üveys Şen
0009-0000-0297-6064
Türkiye

Mehmet Gökhan Bakal ^*
0000-0003-2897-3894
Türkiye

Yayımlanma Tarihi

3 Aralık 2024

Gönderilme Tarihi

29 Nisan 2024

Kabul Tarihi

25 Temmuz 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1475168

IZ

https://izlik.org/JA95SY82AZ

Kaynak Göster

RIS / Bibtex

APA

Şen, T. Ü., & Bakal, M. G. (2024). ENHANCING DEEP LEARNING PERFORMANCE THROUGH A GENETIC ALGORITHM-ENHANCED APPROACH: FOCUSING ON LSTM. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1350-1360. https://doi.org/10.17780/ksujes.1475168