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Polygence Scholar2022
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Soham Patil

Sarah W Gibbons Middle SchoolClass of 2026Westborough, Massachusetts



  • "Can a genetic algorithm be trained to optimize the routes for buses in a variable environment?" with mentor Cody (Oct. 9, 2022)

Project Portfolio

Can a genetic algorithm be trained to optimize the routes for buses in a variable environment?

Started May 27, 2022

Abstract or project description

When there is a task in which there is freedom in the options available, humans can have a hard time finding the solution in all of the other options. Computers, however, are faster at finding the solutions because they can analyze all of the available options more rapidly than humans. In this paper, we will use genetic algorithms to determine the optimal route a bus should take to pick up students in a particular area. For this we would need to consider the bus stops, the distance from one stop to the other, and the traffic in the area. Using all of that information we can draw the best bus route for every bus in a district. In other words, we can take the time these bus route workers use to draw routes and use a genetic algorithm that does the work for them. In the event of a changing environment such as a missing bus driver or changes in traffic, a genetic algorithm can likely adapt better than a human and still find the best routes available. In addition, in the event that a machine does not provide the best route because it didn’t have enough time to find all of the alternate routes, we should still have a fairly better route than a human given the same amount of time. In conclusion, an algorithm that can find the best route is needed because it saves time and can adapt to special circumstances in which bus map drawers can not. Our use case demonstrates that a machine learning algorithm is capable of performing a task with more efficiency and better results than humans alone. These results provide evidence that machine learning using a genetic algorithm may be used throughout the commercial industry to improve productivity in many fields.