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Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm

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Advances in Soft Computing (MICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11835))

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Abstract

In recent years, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristic algorithms have been used to find good solutions in a reasonably low time, in addition, the use of self-adaptive strategies has increased considerably because they have proved to be a good option to improve performance in these algorithms. In this research, a self-adaptive mechanism is developed to improve the performance of the genetic algorithm for dynamic combinatorial problems, using the strategy of genotype-phenotype mapping and probabilistic distributions. Results demonstrate the capability of the mechanism to help algorithms to adapt in dynamic environments.

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Author information

Authors and Affiliations

  1. Instituto Mexicano del Transporte, Querétaro, Mexico

    Cesar J. Montiel Moctezuma

  2. Tecnologico de Monterrey, Monterrey, Mexico

    Jaime Mora & Miguel Gonzalez-Mendoza

Authors
  1. Cesar J. Montiel Moctezuma
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  2. Jaime Mora
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  3. Miguel Gonzalez-Mendoza
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Corresponding author

Correspondence to Miguel Gonzalez-Mendoza .

Editor information

Editors and Affiliations

  1. Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Instituto Politecnico Nacional, Mexico, Mexico

    Ildar Batyrshin

  3. Universidad Veracruzana, Xalapa, Mexico

    Antonio Marín-Hernández

Appendix A

Appendix A

Fig. A1.
figure 12

Best solutions for static One-Max problem (50 bits);

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Fig. A2.
figure 13

Average solutions for static One-Max problem (50 bits)

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Fig. A3.
figure 14

Best solutions for dynamic One-Max problem (50 bits);

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Fig. A4.
figure 15

Average solutions for dynamic One-Max problem (50 bits)

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Fig. A5.
figure 16

Best solutions for static TSP problem (ulysses22);

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Fig. A6.
figure 17

Average solutions for static TSP problem (ulysses22)

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Fig. A7.
figure 18

Average solutions for dynamic TSP problem (ulysses22);

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Fig. A8.
figure 19

Average solutions for dynamic TSP problem (ulysses22)

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Fig. A9.
figure 20

Best-average results for static One-Max problem (maximization);

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Fig. A10.
figure 21

Best-average results for dynamic One-Max problem (maximization)

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Fig. A11.
figure 22

Best-average results for static TSP problem (minimization);

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Fig. A12.
figure 23

Best-average results for dynamic TSP problem (minimization)

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Montiel Moctezuma, C.J., Mora, J., Gonzalez-Mendoza, M. (2019). Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-33749-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33748-3

  • Online ISBN: 978-3-030-33749-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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