Virtual Savant for the Knapsack Problem: learning for automatic resource allocation

  1. Massobrio, R. 1
  2. Dorronsoro Díaz, B. 2
  3. Nesmachnow Cánovas, S.E. 1
  1. 1 Universidad de la República
    info

    Universidad de la República

    Montevideo, Uruguay

    ROR https://ror.org/030bbe882

  2. 2 Universidad de Cádiz
    info

    Universidad de Cádiz

    Cádiz, España

    ROR https://ror.org/04mxxkb11

Revista:
Proceedings of the Institute for System Programming of the RAS

ISSN: 2079-8156,  2220-6426

Año de publicación: 2019

Volumen: 31

Número: 2

Páginas: 21-32

Tipo: Artículo

DOI: 10.15514/ISPRAS-2019-31(2)-2 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Proceedings of the Institute for System Programming of the RAS

Resumen

This article presents the application of Virtual Savant to solve resource allocation problems, a widely-studied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given problem from the solutions computed by a reference algorithm, and its design allows taking advantage of modern parallel computing infrastructures. The proposed approach is evaluated to solve the Knapsack Problem, which models different variant of resource allocation problems, considering a set of instances with varying size and difficulty. The experimental analysis is performed on an Intel Xeon Phi many-core server. Results indicate that Virtual Savant is able to compute accurate solutions while showing good scalability properties when increasing the number of computing resources used.