PERSONALIZED NUTRITION PLANS USING GENETIC ALGORITHMS: OPTIMIZING DIETS BASED ON INDIVIDUAL GENOMIC DATA

Abstract

Abstract: Personalized nutrition aims to tailor dietary recommendations based on individual genetic profiles, optimizing health outcomes by addressing unique genetic variations. This paper explores the application of genetic algorithms (GAs) to develop personalized nutrition plans, leveraging genomic data to refine dietary recommendations. GAs, inspired by natural selection, offer a robust optimization framework for solving complex problems involving numerous variables, making them suitable for personalized nutrition. The study integrates genetic data with dietary guidelines to form a comprehensive dataset, then applies a GA to generate optimal nutrition plans that align with genetic predispositions and nutritional needs. Simulations demonstrate the GA's effectiveness in producing practical and individualized dietary recommendations, improving upon traditional, one-size-fits-all dietary guidelines. Results indicate that GAs can enhance dietary personalization by systematically exploring a vast space of potential dietary combinations. This approach represents a significant advancement in personalized nutrition, offering scalable solutions for integrating genetic information into dietary practices. The findings underscore the potential of genetic algorithms to transform dietary planning and improve health outcomes through tailored nutrition strategies.