Genetic Optimization Algorithm

A Genetic algorithm that uses the concept of Chromosome Crossover to generate new, higher-fitness solutions. Solutions are generated until a generation that contains an individual matching the target is produced.

Limitations of genetic algorithms: Solutions may converge to local maxima driven by the fitness function that don't represent the target solution. This requires editing some of the hyperparameters including population size, and mutation & survival rate.

Usage

Run make to compile the C++ genetic algorithm
Navigate to /server and run node server.js to run the server
Run npx vite in /frontend to run the web app

Sources: https://www.cs.cmu.edu/Groups/AI/util/html/faqs/ai/genetic/part2/faq-doc-2.html