Developing a Genetic Algorithm Based Daily Calorie Recommendation System for Humans

Abstract

Lately, there has been an increasing fascination with employing genetic algorithms (GAs) to tackle intricate optimization issues. Genetic algorithms (GAs) draw inspiration from natural selection and have demonstrated efficacy in discovering optimal solutions for many problems, such as diet optimization. This research presents a genetic algorithm (GA) approach to estimate individuals' optimal daily calorie intake. The proposed approach considers the individual's age, gender, height, weight, exercise level, and dietary limitations. In addition, it considers the nutritional composition of various dietary items. The strategy aims to create a daily meal plan that fulfils the individual's calorie requirements and supplies all necessary nutrients. The suggested technique was assessed using a dataset consisting of 100 people. The findings demonstrated that the approach successfully produced dietary regimens that satisfied the individual's specific caloric requirements and encompassed all vital elements. The technique also produced diverse and captivating food menus. Additionally, we recommend a fitness function that assesses each suggestion's appropriateness for a given user. Ultimately, to completely comprehend the characteristics and functionality of our system, we conducted experimental research using both synthetic data and actual users with varying requirements, preferences, and ambitions.