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An effective architecture for learning and evolving flexible job-shop schedules [An article from: European Journal of Operational Research]
Book Details
Author(s)N.B. Ho, J.C. Tay, E.M.K. Lai
PublisherElsevier
ISBN / ASINB000PC0J0Y
ISBN-13978B000PC0J02
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In recent years, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using evolutionary algorithms. This paper proposes an architecture for learning and evolving of Flexible Job-Shop schedules called LEarnable Genetic Architecture (LEGA). LEGA provides an effective integration between evolution and learning within a random search process. Unlike the canonical evolution algorithm, where random elitist selection and mutational genetics are assumed; through LEGA, the knowledge extracted from previous generation by its schemata learning module is used to influence the diversity and quality of offsprings. In addition, the architecture specifies a population generator module that generates the initial population of schedules and also trains the schemata learning module. A large range of benchmark data taken from literature and some generated by ourselves are used to analyze the efficacy of LEGA. Experimental results indicate that an instantiation of LEGA called GENACE outperforms current approaches using canonical EAs in computational time and quality of schedules.
Description:
In recent years, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using evolutionary algorithms. This paper proposes an architecture for learning and evolving of Flexible Job-Shop schedules called LEarnable Genetic Architecture (LEGA). LEGA provides an effective integration between evolution and learning within a random search process. Unlike the canonical evolution algorithm, where random elitist selection and mutational genetics are assumed; through LEGA, the knowledge extracted from previous generation by its schemata learning module is used to influence the diversity and quality of offsprings. In addition, the architecture specifies a population generator module that generates the initial population of schedules and also trains the schemata learning module. A large range of benchmark data taken from literature and some generated by ourselves are used to analyze the efficacy of LEGA. Experimental results indicate that an instantiation of LEGA called GENACE outperforms current approaches using canonical EAs in computational time and quality of schedules.
