Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.
Original languageEnglish
Title of host publicationICMLA '13
Subtitle of host publicationProceedings of the 2013 12th International Conference on Machine Learning and Applications
Pages374-377
Number of pages4
Volume2
ISBN (Electronic)978-0-7695-5144-9
DOIs
Publication statusPublished - 04 Dec 2013
Externally publishedYes
Event2013 12th International Conference on Machine Learning and Applications - Miami, United States of America
Duration: 04 Dec 201307 Dec 2013

Conference

Conference2013 12th International Conference on Machine Learning and Applications
Country/TerritoryUnited States of America
CityMiami
Period04 Dec 201307 Dec 2013

Keywords

  • job-shop problem
  • helper-objectives
  • multi-objective optimization
  • adaptive selection

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