A Review of Genetic Algorithm: Operations and Applications

Authors

  • Faizatulhaida Md Isa Department of Mathematics, Science & Computer, Politeknik Tuanku Sultanah Bahiyah (PTSB), Kulim Hi-Tech Park, 09090 Kulim, Kedah, Malaysia
  • Wan Nor Munirah Ariffin Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Educational Hub, 84600 Pagoh, Johor, Malaysia
  • Muhammad Shahar Jusoh Faculty of Business and Comunication, Universiti Malaysia Perlis (UniMAP), Pusat Perniagaan Pengkalan Jaya Jalan Alor Setar-Kangar 01000 Kangar, Perlis, Malaysia
  • Erni Puspanantasari Putri Department of Industrial Engineering, University of 17 Agustus 1945 Surabaya, Indonesia

DOI:

https://doi.org/10.37934/araset.40.1.134

Keywords:

Review, genetic algorithm, operation, application

Abstract

This article presents a review of the Genetic Algorithm (GA), a prominent optimization technique inspired by natural selection and genetics. In the context of rapidly evolving computational methodologies, GA have gained considerable attention for their efficacy in solving complex optimization problems across various domains. The background highlights the growing significance of optimization techniques in addressing real-world challenges. However, the inherent complexity and diversity of problems necessitate versatile approaches like GA. The problem statement underscores the need to explore the underlying operations and applications of GA to provide a nuanced understanding of their capabilities and limitations. The objectives of this review encompass delving into the fundamental genetic operators, such as selection, crossover, and mutation, while examining their role in maintaining diversity and converging toward optimal solutions. Methodology-wise, a systematic analysis of existing literature is undertaken to distil key insights and trends in GA applications. The main findings show the adaptability of GA in tackling problems spanning engineering, economics, bioinformatics, and beyond. By facilitating the discovery of optimal or near-optimal solutions within large solution spaces, GA proves its mettle in scenarios where traditional methods fall short. The conclusion underscores the enduring relevance of GA in the optimization landscape, emphasizing their potential to remain a pivotal tool for addressing intricate real-world challenges, provided their parameters are fine-tuned judiciously to balance exploration and exploitation.

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Author Biographies

Faizatulhaida Md Isa, Department of Mathematics, Science & Computer, Politeknik Tuanku Sultanah Bahiyah (PTSB), Kulim Hi-Tech Park, 09090 Kulim, Kedah, Malaysia

faizatulhaida@studentmail.unimap.edu.my

Wan Nor Munirah Ariffin, Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Educational Hub, 84600 Pagoh, Johor, Malaysia

munirah@unimap.edu.my

Muhammad Shahar Jusoh, Faculty of Business and Comunication, Universiti Malaysia Perlis (UniMAP), Pusat Perniagaan Pengkalan Jaya Jalan Alor Setar-Kangar 01000 Kangar, Perlis, Malaysia

shahar@unimap.edu.my

Erni Puspanantasari Putri, Department of Industrial Engineering, University of 17 Agustus 1945 Surabaya, Indonesia

erniputri@untag-sby.ac.id

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Published

2024-02-19

How to Cite

Faizatulhaida Md Isa, Wan Nor Munirah Ariffin, Muhammad Shahar Jusoh, & Erni Puspanantasari Putri. (2024). A Review of Genetic Algorithm: Operations and Applications . Journal of Advanced Research in Applied Sciences and Engineering Technology, 40(1), 1–34. https://doi.org/10.37934/araset.40.1.134

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