INNOVATIVE APPROACHES TO BIG DATA ANALYTICS: THE ROLE OF EVOLUTIONARY OPTIMIZATION IN MODEL DEVELOPMENT

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Dr. Narendra Sharma, Deepak Kumar

Vol 18, No 11 ( 2023 )   |  Licensing: CC 4.0   |   Pg no: 383-392   |   Published on: 30-11-2023

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Abstract
This paper explores the innovative approaches to Big Data analytics, focusing on the role of evolutionary optimization in model development. Through a mixed-methods research design, the study combines quantitative analysis of various evolutionary optimization techniques, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), with qualitative case studies from industry settings. The findings reveal that evolutionary optimization significantly enhances model performance, particularly in terms of accuracy, scalability, and computational efficiency. The study also highlights the practical challenges and opportunities associated with implementing these techniques in real-world scenarios. The results contribute to a deeper understanding of how evolutionary optimization can be effectively applied in Big Data analytics to foster innovation and improve outcomes.

Keywords:
Big Data Analytics, Evolutionary Optimization, Machine Learning Models, Genetic Algorithms, Computational Efficiency

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