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A New Genetic Improvement Operator Based on Frequency Analysis for Genetic Algorithms Applied to Job Shop Scheduling Problem

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Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12854))

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Abstract

Many researchers today are using meta-heuristics to treat the class of problems known in the literature as Job Shop Scheduling Problem (JSSP) due to its complexity since it consists of combinatorial problems and it is an NP-Hard computational problem. JSSPs are a resource allocation issue and, to solve its instances, meta-heuristics as Genetic Algorithm (GA) are widely used. Although the GAs present good results in the literature, it is very common for these methods that they are stagnant in solutions that are local optima during their iterations and that have difficulty in adequately exploring the search space. To circumvent these situations, we propose in this work the use of an operator specialized in conducting the GA population to a good exploration: the Genetic Improvement based on Frequency Analysis (GIFA). GIFA makes it possible to manipulate the genetic material of individuals by adding characteristics that are believed to be important, with the proposal of directing some individuals who are lost in the search space to a more favorable subspace without breaking the diversity of the population. The proposed GIFA is evaluated considering two different situations in well-established benchmarks in the specialized JSSP literature and proved to be competitive and robust compared to the methods that represent the state of the art.

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Acknowledgments

This study was financed in part by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil” (CAPES) - Finance Code 001, and by the Brazilian National Council for Scientific and Technological Development, process #381991/2020-2.

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Authors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Monique Simplicio Viana & Orides Morandin Junior

  2. University of São Paulo, São Carlos, Brazil

    Rodrigo Colnago Contreras

Authors
  1. Monique Simplicio Viana
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  2. Rodrigo Colnago Contreras
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  3. Orides Morandin Junior
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Corresponding author

Correspondence to Monique Simplicio Viana .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Viana, M.S., Contreras, R.C., Junior, O.M. (2021). A New Genetic Improvement Operator Based on Frequency Analysis for Genetic Algorithms Applied to Job Shop Scheduling Problem. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_39

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  • DOI: https://doi.org/10.1007/978-3-030-87986-0_39

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