[SOLVED] 41040 Introduction to Artificial Intelligence – Spring 2025 Lab 3 Search Algorithms for Solving

Lab 3. Search Algorithms for Solving Optimisation Problems

Aims: This lab provides an opportunity for you to exercise three local search algorithms, ie., hill climbing, simulated annealing, and genetic algorithm, to solve a simple optimisation problem.

Tasks:

Task 1. Download the student lab3 pack from the Lab3 page and unpack the files to your lab3 folder in your own google drive.

Task 2. Learn from your tutor about how to “mount drive” in your notebook in order to access files in your google drive.

Task 3. Learn from your tutor about how to upload datasets through reading local csv files.

Task 4. Learn from your tutor’s demo of how to create required data structure from the given data files for using the local search algorithms.

Task 5. Learn from your tutor’s demo of how the Genetic Algorithm (GA) algorithm works in solving the given local search problem. You should learn how the population evolves, how the objective function or fitness values change over generations in the search process and understand how the search process terminates.

Task 6.  Work in a small group to first choose one from the Hill Climbing (HC) and Simulated Annealing (SA) algorithms, then run the code segments for the chosen algorithm and lastly explain how the search process unfolds and how the cost/energy function values change during the search process using the outputs of running the code.

Task 7. Present your work in Task 6 in a group, i.e., your understanding of how the chosen algorithm works in the context of solving the given problem. Pay attention to the problem representation, connection between the problem representation and the search algorithm, meta-parameters used and how the algorithm works using the problem information.