Parameter Tuning of Groceries Scheduling by Genetic Algorithm

  • Abstract
  • Keywords
  • References
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  • Abstract

    Genetic Algorithm (GA) is a promising optimization algorithm that can provide effective solutions to help overcome optimization problems.  However, applying the GA without parameter tuning may result in a low efficiency. Thus, this study investigates the importance of parameter tuning and its applications in solving an optimization problem. The parameter tuning was experimented on groceries scheduling problems to find the shortest time required in delivering the groceries. The experiments were performed on different values of mutation operator, crossover operator and number of iterations on groceries scheduling. The knowledge representation and the architecture of the problem are presented.  The improvements of the search ability are verified by a series of experiments. Our results show that the best mutation operator is the Inversion Mutation, while the best crossover operator is Order 1 Crossover.


  • Keywords

    Genetic Algorithm; Optimization; Parameter Tuning; Scheduling.

  • References

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Article ID: 23405
DOI: 10.14419/ijet.v7i4.31.23405

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