Implementación de un Algoritmo Genético para el UFLP

Summary

A genetic-algorithm implementation for the uncapacitated facility location problem (UFLP), with the objective of minimizing total operating and assignment cost. It compares four configurations while fixing population size \(N=100\) and maximum generations \(G=30\) during hyperparameter tuning.

Context

The report studies sequential hyperparameter tuning for mutation, crossover, tournament size, and operator type, with a shared reproducibility seed set \(s=(2,3,5,7,11,13,17,19,23)\).

Main contributions

• Implements a genetic algorithm for the uncapacitated facility location problem.
• Compares optimized, speed-oriented, and randomly parameterized configurations.
• Uses common population and generation settings during tuning to support reproducible comparisons.