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Exploring the Potential of Genetic Algorithms for Optimizing Academic Schedules at the School of Mechatronic Engineering: Preliminary Results

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Applied Informatics (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1874))

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Abstract

The generation of schedules is a complex challenge, particularly in academic institutions aiming for equitable scheduling. The goal is to achieve fair and balanced schedules that meet the requirements of all parties involved, such as workload, class distribution, shifts, and other relevant criteria. To address this challenge, a genetic algorithm specifically designed for optimal schedule generation has been proposed as a solution. Adjusting genetic algorithm parameters impacts performance, and employing parameter optimization techniques effectively tackles this issue. This work introduces a genetic algorithm for optimal schedule generation, utilizing suitable encoding and operators, and evaluating quality through fitness techniques. Optimization efforts led to reduced execution time, improved solution quality, and positive outcomes like faster execution, fewer generations, increased stability, and convergence to optimal solutions.

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Author information

Authors and Affiliations

  1. Department of Mechatronics, Universidad Internacional del Ecuador, Av. Simon Bolivar, 170411, Quito, Ecuador

    Johan Alarcón, Samantha Buitrón, Alexis Carrillo, Mateo Chuquimarca, Alexis Ortiz, Robinson Guachi & Lorena Guachi-Guachi

  2. College of Computing, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    D. H. Peluffo-Ordóñez

  3. SDAS Research Group, 43150, Ben Guerir, Morocco

    D. H. Peluffo-Ordóñez & Lorena Guachi-Guachi

Authors
  1. Johan Alarcón
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  2. Samantha Buitrón
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  3. Alexis Carrillo
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  4. Mateo Chuquimarca
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  5. Alexis Ortiz
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  6. Robinson Guachi
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  7. D. H. Peluffo-Ordóñez
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  8. Lorena Guachi-Guachi
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Corresponding author

Correspondence to Lorena Guachi-Guachi .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Ecotec, Guayaquil, Ecuador

    Marcelo Leon

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Alarcón, J. et al. (2024). Exploring the Potential of Genetic Algorithms for Optimizing Academic Schedules at the School of Mechatronic Engineering: Preliminary Results. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-46813-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46812-4

  • Online ISBN: 978-3-031-46813-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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