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This work aims to provide a comprehensive overview of genetic algorithms and their applications in optimization.

A Review of Genetic Algorithms: Principles, Procedures, and Applications in Optimization

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This work aims to provide a comprehensive overview of genetic algorithms and their applications in optimization.
Examined principles and operational mechanisms of genetic algorithms.
Outlined sequential phases including problem formulation, solution encoding, and algorithm evaluation.
Analyzed applications and limitations of genetic algorithms in various fields.
Demonstrated the effectiveness of GAs in solving complex, multi-dimensional optimization problems.
Highlighted the improvements achieved through hybridization with algorithms like memetic algorithms and swarm intelligence.
Identified ongoing gaps in research regarding algorithm performance and theoretical convergence.

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This review explores genetic algorithms for optimization, indicating their extensive applications across various fields.

M.A. El‐Shorbagy (2026) studied this question.

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