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<p>Since its inception in the 1970s, Genetic Algorithms (GA’s) have been a central method in evolutionary computation and heuristic optimization. This paper presents a comprehensive bibliometric and thematic review of GA research spanning five decades (1975–2025). Based on the analysis of over 26,707 publications indexed in the Scopus, Web of Science (WoS), and IEEE Xplore databases, the study captures the historical development, methodological advances, and widespread applications of GA’s. The review uses advanced visualization techniques such as Sankey diagrams (to trace country-institution-keyword linkages), Choropleth maps (to illustrate global research contributions), coauthorship and citation networks, and cross-domain heatmaps, the review identifies leading scholars, dominant themes, and emerging research trends. Coverage includes publication patterns, high-impact contributors, core methodological innovations, and the algorithm’s deployment across 12 major domains, including artificial intelligence, engineering design, robotics, bioinformatics, and energy systems. The study highlights the evolution of GA variants, the rise of hybrid and memetic models, integration with deep learning and quantum paradigms, and the increasing prominence of Asia, particularly China, India and Iran, as global leaders in GA research. Through keyword co-occurrence analysis, thematic clustering, and citation dynamics, this review synthesizes the trajectory of GA scholarship and presents a forward-looking roadmap for future exploration and interdisciplinary growth.</p>

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Five Decades of Genetic Algorithms: A Systematic and Bibliometric Review (1975–2025)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

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  • Reshu Chaudhary,
  • Pooja Verma,
  • Rohit Salgotra,
  • Amir H. Gandomi

摘要

Since its inception in the 1970s, Genetic Algorithms (GA’s) have been a central method in evolutionary computation and heuristic optimization. This paper presents a comprehensive bibliometric and thematic review of GA research spanning five decades (1975–2025). Based on the analysis of over 26,707 publications indexed in the Scopus, Web of Science (WoS), and IEEE Xplore databases, the study captures the historical development, methodological advances, and widespread applications of GA’s. The review uses advanced visualization techniques such as Sankey diagrams (to trace country-institution-keyword linkages), Choropleth maps (to illustrate global research contributions), coauthorship and citation networks, and cross-domain heatmaps, the review identifies leading scholars, dominant themes, and emerging research trends. Coverage includes publication patterns, high-impact contributors, core methodological innovations, and the algorithm’s deployment across 12 major domains, including artificial intelligence, engineering design, robotics, bioinformatics, and energy systems. The study highlights the evolution of GA variants, the rise of hybrid and memetic models, integration with deep learning and quantum paradigms, and the increasing prominence of Asia, particularly China, India and Iran, as global leaders in GA research. Through keyword co-occurrence analysis, thematic clustering, and citation dynamics, this review synthesizes the trajectory of GA scholarship and presents a forward-looking roadmap for future exploration and interdisciplinary growth.