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Título : | Anytime automatic algorithm selection for the Pseudo-Boolean Optimization problem. |
Autor : | Godoy del Campo, Julio Asín Achá, Roberto Pezo Vergara, Catalina |
Fecha de publicación : | 2023 |
Editorial : | Universidad de Concepción |
Resumen : | Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers, based on predicted performance. These techniques have been applied to various problems, such as Boolean Satisfiability, Traveling Salesperson, Graph Coloring, and others. These methods, known as meta-solvers, take an instance of a problem and a portfolio of solvers as input, then predict the best-performing solver and execute it to deliver a solution. Typically, the quality of the solution improves with a longer computational time. This has led to the development of anytime selectors, which consider both the instance and a user-prescribed computational time limit. Anytime meta-solvers predict the best-performing solver within the specified time limit. In this study, we focus on the task of designing anytime meta-solvers for the NP-hard optimization problem of Pseudo-Boolean Optimization (PBO). The effectiveness of our approach is demonstrated via extensive empirical study in which our anytime meta-solver improves dramatically on the performance of Mixed Integer Programming solver Gurobi, the best-performing single solver in the portfolio. |
Descripción : | Tesis presentada para optar al grado de Magíster en Ciencias de la Computación. |
URI : | http://repositorio.udec.cl/jspui/handle/11594/11588 |
Aparece en las colecciones: | Ingeniería Informática y Ciencias de la Computación - Tesis Magister |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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Pezo Vergara_Catalina Tesis.pdf | 1,96 MB | Adobe PDF | Visualizar/Abrir |
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