This project aims to solve the Knapsack Problem, a combinatorial optimization problem, using a Genetic Algorithm (GA) approach. The Knapsack Problem involves maximizing the total value of items that can be included in a knapsack, given a limited weight capacity. The Genetic Algorithm is a heuristic search technique inspired by the process of natural selection and evolution. Additionally, this project utilizes the Turtle graphics library for visualization.

• Genetic Algorithm Implementation: The project implements a Genetic Algorithm to solve the Knapsack Problem, evolving a population of candidate solutions over multiple generations.
• Knapsack Representation: Items and their properties (value, weight) are represented as genes, and individuals in the population represent potential solutions (sets of items).
• Crossover and Mutation: Genetic operators such as crossover and mutation are applied to create new offspring individuals with variations from parent individuals.
• Fitness Evaluation: The fitness of individuals is evaluated based on their ability to maximize the total value of items within the knapsack while not exceeding the weight capacity.