Research Topic · Peer-Reviewed

Genetic Algorithms

Genetic Algorithms (GAs) are a type of artificial intelligence algorithm that mimics the processes in natural selection. They are used to solve complex problems that are too difficult to solve using traditional methods. GAs use principles of evolution that are inspired by nature’s methods of survival and reproductio…

Curated from this journal's research 📚 3 peer-reviewed articles cited Cited 6× across the literature 🔖 ISSN 2689-4602 🗓 Reviewed June 2026

Overview

Genetic Algorithms (GAs) are a type of artificial intelligence algorithm that mimics the processes in natural selection. They are used to solve complex problems that are too difficult to solve using traditional methods. GAs use principles of evolution that are inspired by nature’s methods of survival and reproduction. In GAs, solutions to problems are represented as chromosomes containing coded bits of information and “survival of the fittest” principles are used to find the best solution. GAs are used for a wide range of applications, from solving global optimization problems to scheduling tasks, from machine learning to simulating cellular automata.

Research published in this journal

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

How this research is being cited

The 3 articles above have been cited 6 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 Evolutionary Science (ISSN 2689-4602).

Journal editorial board
Maria Luisa Chiusano · Italy Adina-Elena Segneanu · Romania George Mikhailovsky · United States

This page summarises published research for orientation; it is not medical or professional advice.