Research Topic · Peer-Reviewed

Genetic Algorithms

Genetic algorithms are a type of computing algorithm that use the principles of natural selection and genetics to solve complex problems. By combining the power of evolutionary algorithms with the reliability of machine learning, they can identify the most optimal solution to complicated challenges. Genetic algorith…

Curated from this journal's research 📚 2 peer-reviewed articles cited Cited 5× across the literature 🔖 ISSN 2643-2811 🗓 Reviewed June 2026

Overview

Genetic algorithms are a type of computing algorithm that use the principles of natural selection and genetics to solve complex problems. By combining the power of evolutionary algorithms with the reliability of machine learning, they can identify the most optimal solution to complicated challenges. Genetic algorithms are highly versatile and can be used to provide solutions for a wide range of applications, including optimization of projects, scheduling tasks, searching large datasets, and machine learning. They are also used in artificial intelligence (AI) to allow computers to “evolve” their own solutions to complex problems. Furthermore, they provide a fast and efficient alternative to traditional methods of problem-solving.

Research published in this journal

2 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 2 articles above have been cited 5 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Genetic Algorithms, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Model Based Research (ISSN 2643-2811).

Journal editorial board
Yoshiaki Kikuchi · Japan Yung-Yao Chen · Taiwan Yang Chen · United States

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