This experiment is structured to demonstrate the Hill Climbing algorithm applied on a simple N queens problem. It provides a step-by-step understanding of the Hill Climbing algorithm, the heuristic function it uses, and how it can be used to solve problems like the N queens problem. The experiment also demonstrates how the algorithm can get stuck in local optima and how it can be improved by aloowing sideways moves.

Objectives:

• Understand the Hill Climbing Search Algorithm: Develop a thorough understanding of the hill climbing algorithm, including its step-by-step execution and the mathematical principles underlying its greedy approach of selecting the best immediate move based on the evaluation function.

• Visualize the Search Dynamics: Observe and interpret the hill climbing algorithm's operation through visual representations of node expansions, evaluation function scores, and the evolving search path.

• Trace Path Selection: Understand how the hill climbing algorithm iteratively selects and moves towards the most promising node, emphasizing its reliance on local information and the absence of backtracking mechanisms.

• Address Local Optima Challenges: Examine the challenges posed by local maxima, plateaus, and ridges, and explore strategies to overcome these issues, such as sideways moves.