Estrategias Adaptativas de Reinicio y Clustering para Branch and Bound mediante Q-Learning

Student: Tomás Francisco Díaz Calderón
Degree: Undergraduate Thesis (Memoria de Título)
Primary Advisor: Víctor Reyes
My Role: Committee Member
Institution: Universidad Diego Portales, Escuela de Informática y Telecomunicaciones
Location: Santiago de Chile
Status: In progress

Overview

This thesis explores adaptive restart and clustering strategies for Branch and Bound optimization algorithms, using Q-Learning to dynamically guide the search process. The work aims to improve convergence and solution quality on combinatorial optimization problems by learning restart policies from experience.