Optimizing Nutrient Combinations by Differential Evolution Algorithm: A Computational Approach to Dietary Planning

In this Paper we present a technique using differential evolution to optimize nutrient combinations in diet planning. The objective is to derive the best combination of components that satisfies particular nutritional demands while causing the slightest deviation from optimal ratios of carbohydrates, proteins, and fats. We started by generating a population of nutritional combinations and, through an iterative process, enhanced them using mutation and selection procedures. Our results illustrate how the algorithm converges to optimal nutrient distributions and is an effective optimization tool for diets.