Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence

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S.M.A.N.M Subasinghe
S.M.A.N.M Subasinghe
1 University of Kelaniya Sri Lanka

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Data has become a crucial element for contemporary enterprises; however, deriving practical insights from its immense volume remains an intricate obstacle. This paper examines the capabilities of three bio-inspired computational intelligence (CI) methods -Genetic Algorithms (GAs), Fuzzy Logic (FL), and Swarm Intelligence (SI) -in improving data analytics for business optimization and decision-making. The researcher thoroughly examines the fundamental principles of each technique, emphasizing their inherent advantages and appropriateness for addressing practical business challenges. By reviewing recent research and real-world examples, the researcher illustrates how Genetic Algorithms (GAs) can enhance the efficiency of resource allocation, Fuzzy Logic (FL) can effectively handle uncertainty in risk assessment, and Swarm Intelligence (SI) can streamline logistics and scheduling processes.

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No external funding was declared for this work.

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The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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S.M.A.N.M Subasinghe. 2026. \u201cLeveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 24 (GJCST Volume 24 Issue D1): .

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GJCST Volume 24 Issue D1
Pg. 21- 26
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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August 28, 2024

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Data has become a crucial element for contemporary enterprises; however, deriving practical insights from its immense volume remains an intricate obstacle. This paper examines the capabilities of three bio-inspired computational intelligence (CI) methods -Genetic Algorithms (GAs), Fuzzy Logic (FL), and Swarm Intelligence (SI) -in improving data analytics for business optimization and decision-making. The researcher thoroughly examines the fundamental principles of each technique, emphasizing their inherent advantages and appropriateness for addressing practical business challenges. By reviewing recent research and real-world examples, the researcher illustrates how Genetic Algorithms (GAs) can enhance the efficiency of resource allocation, Fuzzy Logic (FL) can effectively handle uncertainty in risk assessment, and Swarm Intelligence (SI) can streamline logistics and scheduling processes.

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Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence

S.M.A.N.M Subasinghe
S.M.A.N.M Subasinghe University of Kelaniya Sri Lanka

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