A Comparison of Learnheuristics Using Different Reward Functions to Solve the Set Covering Problem

Broderick Crawford, Ricardo Soto, Felipe Cisternas-Caneo, Diego Tapia, Hanns de la Fuente-Mella, Wenceslao Palma, José Lemus-Romani, Mauricio Castillo, Marcelo Becerra-Rozas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The high computational capacity that we have thanks to the new technologies allows us to communicate two great worlds such as optimization methods and machine learning. The concept behind the hybridization of both worlds is called Learnheuristics which allows to improve optimization methods through machine learning techniques where the input data for learning is the data produced by the optimization methods during the search process. Among the most outstanding machine learning techniques is Q-Learning whose learning process is based on rewarding or punishing the agents according to the consequences of their actions and this reward or punishment is carried out by means of a reward function. This work seeks to compare different Learnheuristics instances composed by Sine Cosine Algorithm and Q-Learning whose different lies in the reward function applied. Preliminary results indicate that there is an influence on the quality of the solutions based on the reward function applied.

Original languageEnglish
Title of host publicationOptimization and Learning - 4th International Conference, OLA 2021, Proceedings
EditorsBernabé Dorronsoro, Patricia Ruiz, Lionel Amodeo, Mario Pavone
PublisherSpringer Science and Business Media Deutschland GmbH
Pages74-85
Number of pages12
ISBN (Print)9783030856717
DOIs
StatePublished - 2021
Externally publishedYes
Event4th International Conference on Optimization and Learning, OLA 2021 - Virtual, Online
Duration: 21 Jun 202123 Jun 2021

Publication series

NameCommunications in Computer and Information Science
Volume1443
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference4th International Conference on Optimization and Learning, OLA 2021
CityVirtual, Online
Period21/06/2123/06/21

Keywords

  • Learnheuristic
  • Q-Learning
  • Reinforcement learning
  • Reward function
  • Sine cosine algorithm

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