Research Topic · Peer-Reviewed

Applied Evolution

ary Algorithms Applied Evolutionary Algorithms (AEAs) are powerful problem-solving techniques that use principles from evolution to find optimal solutions to a given problem. AEAs simulate the evolutionary process to evolve increasingly better solutions over time by mimicking the process of evolution. These algorit…

Curated from this journal's research 📚 1 peer-reviewed article cited Cited 25× across the literature 🔖 ISSN 2689-4602 🗓 Reviewed June 2026

Overview

ary Algorithms Applied Evolutionary Algorithms (AEAs) are powerful problem-solving techniques that use principles from evolution to find optimal solutions to a given problem. AEAs simulate the evolutionary process to evolve increasingly better solutions over time by mimicking the process of evolution. These algorithms employ a combination of natural selection, mutation, crossover and other operations to improve the quality of solutions found. AEAs are used in a wide range of fields such as computational finance, multi-objective optimization, machine learning, engineering design, statistic data analysis and artificial intelligence. In short, AEAs are an invaluable tool when it comes to designing and optimizing complex systems.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

How this research is being cited

The 1 article above has been cited 25 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 Applied Evolution, 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.