Genetic algorithms often tend to exhibit premature convergence in solving the Flexible Job-shop Scheduling Problem (FJSP). To overcome this, we propose an improved genetic algorithm guided by gene repetition rates and formulate a mathematical model for dynamic adjustment of genetic operation parameters. The algorithm employs a two-layer chromosome encoding for operation sequences and machine assignments, integrating a gene repetition feedback mechanism to adaptively regulate crossover and mutation probabilities, thereby enhancing population diversity and global search capability. Experiments on benchmark datasets show that the proposed method achieves superior optimization accuracy and faster convergence compared to traditional genetic algorithms, providing an effective approach for solving FJSP.

Bin Li. Application of Improved Genetic Algorithm Based on Gene Duplication Rate in Flexible Job Shop Scheduling Problem. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 9-16. https://doi.org/10.25236/AJCIS.2025.080902.

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