The given task is to implement the N-Queens Problem in Artificial Intelligence using a 5x5 board which holds 5 queens. In order to solve this problem I was tasked with implementing the hill-climbing algorithm and genetic algorithm in order to find the most optimal solutions given the beginning state for the board.

The project focuses on the following concepts: Genetic Algorithm: Search heuristic inspired by Charles Darwin's theory of evolution. The algorithm reflects the process of natural selection in order to find the most optimal "candidate." Hill-Climbing Algorithm: Simple optimization algorithm used to find the best possible solution for a given problem by, making small changes until the optimal solution is found. Hill-Climbing belongs to the local search family in Artificial Intelligence.

Board.py: Given file that will create the board and choose the random starting state for the five queens Genetic.py: Genetic algorithm implementation that finds the most optimal solution for the board and prints out the run time Hill.py: Hill-Climbing algorithm implementation that finds the most optimal solution for the board given the starting state