In this post, I explore solving a Traveling Salesman Problem (TSP) involving 200 cities using genetic algorithms within a Micronaut framework. Leveraging techniques like inversion, insertion, and swap mutations, I illustrate how to maintain genetic diversity and improve solution quality over generations. The implementation showcases significant performance improvements compared to previous solvers. This approach combines simulated annealing, genetic algorithms, and local search to tackle complex optimization challenges effectively.