Abstract
The optimization of industrial processes is a key factor in improving efficiency, reducing costs, and enhancing overall performance. Genetic Algorithms (GAs), inspired by the principles of natural selection, have been extensively applied to solve complex optimization problems in various industrial domains. This paper explores the application of GAs in industrial process optimization, highlighting their potential to address challenges such as resource allocation, production scheduling, and supply chain management. Through the analysis of case studies and comparative approaches, the effectiveness of GAs in optimizing industrial processes is demonstrated. The paper also discusses the advantages and limitations of GAs, suggesting possible improvements and future research directions.
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