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Genetic Diversity in the Multiobjective Optimization of Paths in Graphs

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Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 432))

Abstract

Existing systems that allow Users to plan the route, usually do not support the multiple criteria during the search process. In the sense of the multi-criteria optimization, such a situation involves the search for the Pareto-optimal solutions, but the present services use a weighted sum of the supported criteria. For solving Multiobjective Shortest Path problem we incorporate genetic algorithms with modified genetic operators, what allows the reduction of the search space. In this paper we compare genetic diversity in the algorithms which incorporate our method. Conducted research shown that proposed modifications allowed to obtain better diversity without either changing parameters or apply some rules to the algorithm.

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References

  1. Abraham, I., Delling, D., Goldberg, V.A., Werneck, R.: A hub-based labeling algorithm for shortest paths in road networks. LNCS 5038, 319–333 (2011)

    Google Scholar 

  2. Bauer, R., Delling, D.: SHARC: fast and robust unidirectional routing. J. Exp. Algorithmics (JEA) 14(4) (2009)

    Google Scholar 

  3. Ramadan, S.Z.: Reducing premature convergence problem in genetic algorithm: Application on traveling salesman problem. Comput. Inf. Sci. 6(1), 47–57 (2013)

    Google Scholar 

  4. Rocha, M., Neves, J.: Preventing premature convergence to local optima in genetic algorithms via random offspring generation. In: Multiple Approaches to Intelligent Systems, pp. 127–136. Springer, Berlin (1999)

    Google Scholar 

  5. Nicoara, E.S.: Mechanisms to avoid the premature convergence of genetic algorithms. In: PetroleumGas University of Ploiesti Bulletin. Mathematics–Informatics–Physics, series 61, pp. 87–96 (2009)

    Google Scholar 

  6. Muhlenbein, H.: Evolution in time and space—the parallel genetic algorithm. In: Foundations of Genetic Algorithms (1991)

    Google Scholar 

  7. Kureichick, V., Melikhov, A.N., Miaghick, V.V., Savelev, O.V., Topchy, A.P.: Some new features in the genetic solution of the traveling salesman problem. In: Parmee, I., Denham, M.J. (eds.) Adaptive Computing in Engineering Design and Control 96 (ACEDC’96), 2nd International Conference of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Computing with Current Engineering Practice, Plymouth, UK (1996)

    Google Scholar 

  8. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  10. Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 1128–1141 (2004)

    Article  Google Scholar 

  11. Hu, J., Goodman, E., Seo, K., Fan, Z., Rosenberg, R.: The hierarchical fair competition (hfc) framework for sustainable evolutionary algorithms. Evol. Comput. 13(2), 241–277 (2005)

    Article  Google Scholar 

  12. Munetomo, M., Takai, Y., Sato, Y.: A migration scheme for the genetic adaptive routing algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, 1998, vol. 3, pp. 2774–2779 (1998)

    Google Scholar 

  13. Kanoh, H., Hara, K.: Hybrid genetic algorithm for dynamic multiobjective route planning with predicted traffic in a real-world road network. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 657–664. ACM (2008)

    Google Scholar 

  14. Kubota, N., Shimojima, K., Fukuda, T.: The role of virus infection in virus-evolutionary genetic algorithm. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 182–187 (1996)

    Google Scholar 

  15. Maria, J., Pangilinan, A., Janssens, G.K.: Evolutionary algorithms for the multiobjective shortest path planning problem. Int. J. Comput. Inf. Sci. Eng. 1, 54–59 (2007)

    Google Scholar 

  16. Chakraborty, B.: Simultaneous multiobjective multiple route selection using genetic algorithm for car navigation. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) Pattern Recognition and Machine Intelligence, LNCS 3776, pp. 696–701. Springer, Berlin (2005)

    Google Scholar 

  17. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, Pittsburgh, USA, pp. 93–100 (1985)

    Google Scholar 

  18. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  19. Mori, N., Kita, H., Nishikawa, Y.: Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In: Parallel Problem Solving from Nature PPSN V, pp. 149–158. Springer, Berlin (1998)

    Google Scholar 

  20. Morrison, R.W., De Jong, K.A.: Measurement of population diversity. In: Artificial Evolution, pp. 31–41. Springer, Berlin (2002)

    Google Scholar 

  21. Hien, N.T., Hoai, N.X.: A brief overview of population diversity measures in genetic programming. In: Proceedings of 3rd Asian-Pacific Workshop on Genetic Programming, Hanoi, Vietnam, pp. 128–139 (2006)

    Google Scholar 

  22. Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. Kdd workshop on data cleaning and object consolidation 3, 73–78 (2003)

    Google Scholar 

  23. Chomatek, L., Efficient algorithm for multi-criteria optimisation in the road networks. In: Information Systems Architecture and Technology: System Analysis Approach to the Design, Control and Decision Support, pp. 233–242 (2014)

    Google Scholar 

  24. Chomatek, L., Zakrzewska, D.: Search space reduction in the combinatorial multi-agent genetic algorithms. In: Intelligent Systems’ 2014, pp. 461–472. Springer International Publishing (2015)

    Google Scholar 

  25. Sanders, P., Schultes, D.: Engineering highway hierarchies. In: Algorithms ESA 2006, pp. 804–816. Springer, Berlin (2006)

    Google Scholar 

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Acknowledgments

This research was funded by the Dean’s of the Faculty of Technical Physics, Information Technology and Applied Mathematics grant supporting the skills of young scientists. Development of the application for handling the simulation data was financed from the project “Platforma Informatyczna TEWI” funded from the European Union Innovative Economy, grant no. POIG.02.03.00-00-028/09

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Lodz, Poland

    Łukasz Chomątek

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  1. Łukasz Chomątek
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Correspondence to Łukasz Chomątek .

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Editors and Affiliations

  1. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wrocław, Poland

    Zofia Wilimowska

  2. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wroclaw, Poland

    Leszek Borzemski

  3. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wrocław, Poland

    Adam Grzech

  4. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wrocław, Poland

    Jerzy Świątek

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Chomątek, Ł. (2016). Genetic Diversity in the Multiobjective Optimization of Paths in Graphs. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. Advances in Intelligent Systems and Computing, vol 432. Springer, Cham. https://doi.org/10.1007/978-3-319-28567-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-28567-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28565-8

  • Online ISBN: 978-3-319-28567-2

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