Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia

Research Article

Analysis of Crossover Probability on Genetic Algorithm Performance in Optimizing Course Scheduling in the Unimed Electrical Engineering Study Program

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  • @INPROCEEDINGS{10.4108/eai.24-10-2023.2342105,
        author={Rudi  Salman and Irfandi  Irfandi and Suprapto  Suprapto and Sayuti  Rahman and Herdianto  Herdianto},
        title={Analysis of Crossover Probability on Genetic Algorithm Performance in Optimizing  Course Scheduling in the Unimed Electrical Engineering Study Program},
        proceedings={Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia},
        publisher={EAI},
        proceedings_a={ICIESC},
        year={2024},
        month={1},
        keywords={crossover probability genetic algorithm computation time optimization course scheduling},
        doi={10.4108/eai.24-10-2023.2342105}
    }
    
  • Rudi Salman
    Irfandi Irfandi
    Suprapto Suprapto
    Sayuti Rahman
    Herdianto Herdianto
    Year: 2024
    Analysis of Crossover Probability on Genetic Algorithm Performance in Optimizing Course Scheduling in the Unimed Electrical Engineering Study Program
    ICIESC
    EAI
    DOI: 10.4108/eai.24-10-2023.2342105
Rudi Salman1,*, Irfandi Irfandi2, Suprapto Suprapto3, Sayuti Rahman4, Herdianto Herdianto5
  • 1: Departement of Electrical Engineering, Universitas Negeri Medan, Medan, North Sumatera
  • 2: Department of Physics, Faculty of Mathematics and Natural Sciences Medan State University, Indonesia
  • 3: Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Medan, Indonesia.
  • 4: Departement of Information System, Universitas Harapan Medan, Medan, North Sumatera
  • 5: Department of Information Technology, Universitas Panca Budi Medan,North Sumatera
*Contact email: rudisalman@unimed.ac.id

Abstract

Genetic Algorithm (GA) speed is determined by computation time. Computing time in GA for finding the optimum value is strongly influenced by the following parameters: population size, Crossover Probability (Pc), Mutation Probability (Pm), and the selected selection method. Determining the appropriate and correct Pc value indicates how large the parent chromosome will experience crossover. The method used to analyze the effect of Pc on GA performance is changing the Pc value between 0.80-0.95. The simulation used MATLAB R2012a to obtain the best computational time for each Pc value. The test results show that the fastest computing time is in the range of Pc values between 0.85-0.95 with an average computation time of 0.14564s. This indicates that for the case of optimizing the scheduling of courses in the Unimed Electrical Engineering study program, the Pc value between 0.85-0.95 will provide the fastest computation time.