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Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms.

Bibliographic Details
Title: Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms.
Authors: Martinez, Alejandro Moreno, Landassuri Moreno, Victor Manuel, Chau, Asdrúbal López, Morales Escobar, Saturnino Job
Source: International Journal of Combinatorial Optimization Problems & Informatics; Jan-Apr2026, Vol. 17 Issue 1, p263-274, 12p
Subject Terms: Genetic algorithms, School schedules, Scheduling, Mathematical functions, Mathematical optimization, Subroutines (Computer programs)
Abstract: The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Combinatorial Optimization Problems & Informatics is the property of International Journal of Combinatorial Optimization Problems & Informatics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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Header DbId: edb
DbLabel: Complementary Index
An: 191788350
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AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1041.06604003906
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  Data: Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Martinez%2C+Alejandro+Moreno%22">Martinez, Alejandro Moreno</searchLink><br /><searchLink fieldCode="AR" term="%22Landassuri+Moreno%2C+Victor+Manuel%22">Landassuri Moreno, Victor Manuel</searchLink><br /><searchLink fieldCode="AR" term="%22Chau%2C+Asdrúbal+López%22">Chau, Asdrúbal López</searchLink><br /><searchLink fieldCode="AR" term="%22Morales+Escobar%2C+Saturnino+Job%22">Morales Escobar, Saturnino Job</searchLink>
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  Data: International Journal of Combinatorial Optimization Problems & Informatics; Jan-Apr2026, Vol. 17 Issue 1, p263-274, 12p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22School+schedules%22">School schedules</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+functions%22">Mathematical functions</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Subroutines+%28Computer+programs%29%22">Subroutines (Computer programs)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Combinatorial Optimization Problems & Informatics is the property of International Journal of Combinatorial Optimization Problems & Informatics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.61467/2007.1558.2026.v17i1.1144
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 263
    Subjects:
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: School schedules
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Mathematical functions
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Subroutines (Computer programs)
        Type: general
    Titles:
      – TitleFull: Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms.
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            NameFull: Martinez, Alejandro Moreno
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            NameFull: Landassuri Moreno, Victor Manuel
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            NameFull: Chau, Asdrúbal López
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            NameFull: Morales Escobar, Saturnino Job
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          Dates:
            – D: 01
              M: 01
              Text: Jan-Apr2026
              Type: published
              Y: 2026
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              Value: 17
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            – TitleFull: International Journal of Combinatorial Optimization Problems & Informatics
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