A review on genetic algorithm: past, present, and future

By Sourabh Katoch, S. Chauhan, Vijay Kumar. Multimedia tools and applications, 2020

4,003 citations recorded by the source corpus.

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f33b5c51d64569c4b0192c9041fd8ccaf3849f52

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In Artificial Intelligence

Citations in the corpus

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends (2023, background): “These algorithms are frequently employed in AI and ML to tackle complex optimization challenges that traditional methods cannot address [138].”