Project Objectives:

To understand and apply genetic algorithms in solving path planning problems.

To analyze algorithm performance under varying conditions.

To foster collaboration and teamwork in a technical project setting.

Project Phases:

Problem Formulation: Define initial state, goal state, and successor function for the path

planning problem.

Algorithm Design: Develop a genetic algorithm for path planning. This includes defining

the chromosome structure, fitness function, selection, crossover, and mutation methods.

Implementation: Write Python code for the genetic algorithm in a Jupyter Notebook.

Ensure the code can handle different grid sizes and obstacle densities.

Testing and Analysis:

Test your program on randomly generated test problems on grids of size $100$ and

varying the percentage of obstacles from $10$ to $90$ by steps of $10.$

For each type of problem $($the number of obstacles is fixed$)$ generate $20$ random

problem tests. The total number of runs of your code is $180.$

Analyze and comment your results. Provide for each type of problem the average

time, the worst case, the best case. Draw a figure summarizing all results. Interpret

your results.

Documentation and Presentation: Create a comprehensive Jupyter Notebook with code,

comments, and in$-$line analysis.

Submission Requirements:

Source Code and Documentation: A well$-$commented Jupyter Notebook containing

the implementation and inline analysis.

Technical Report: A comprehensive report detailing the approach, implementation,

results, and analysis of the algorithm performance $($It can be merged with Jupyter

Notebook$)$