The Use of Genetic Algorithms in Mobile Applications

Plechawska-Wojcik Malgorzata

2014

Abstract

The goal of the paper is to present the application of genetic algorithm in practice. The main result is the mechanism based on genetic algorithm applied in the application dedicated to tourists. The goal of the mechanism is to propose the most effective route between points - tourist facilities. Those objects are also chosen automatically based on user’s interest as well as on his and his friends opinions expressed via social networking services Facebook. Genetic algorithm was implemented to obtain efficient way of solving the problem of matching the appropriate route regarding requirements concerning time and location. The results are obtained in short time by the genetic algorithm running on the web server. The paper presents also results of the application and mechanisms testing, including performance testing.

References

  1. Dorronsoro, B., 2004. Cellular evolutionary algorithms. Springer-Verlag.
  2. El-Sharkawi, M. A., Ngatchou, P., Zarei, A., 2005. Pareto multi objective optimization. Intelligent Systems Application to Power Systems. Proceedings of the 13th International Conference, 84 - 91.
  3. Fox, B., McMahon, M., 1991. Genetic operators for sequencing problems, in Foundations of Genetic Algorithms, G. Rawlins, Ed. Morgan Kaufmann Publishers, San Mateo, CA, 284-300.
  4. Goebel, R., Poole, D., Mackworth A., 1998. Computional Intelligence: A Logical Approach. Oxford University Press.
  5. Guanci, Y., Quingsheng, X., Shaobo, Li., 2006. Studies on fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme. Global Design to Gain a Competitive Edge An Holistic and Collaborative Design Approach based on Computational Tools. Ed. Xiu-Tian Yan, Benoit Eynard, William J. Ion.
  6. Kumar, A., 2013. Encoding schemes in genetic algorithm. International Journal of Advanced Research in IT and Engineering, 2(3).
  7. Kumar, R, Jyotishree, 2012, Novel Encoding Scheme in Genetic Algorithms for Better Fitness, International Journal of Engineering and Advanced Technology (IJEAT), 1(6).
  8. Holland, J., 1992. Adaptation in Natural and Artificial Systems. Cambridge, MA: MIT Press.
  9. McCarthy, J., 2007. What is artificial intelligence? http://wwwformal.stanford.edu/jmc/whatisai/whatisai.html.
  10. Shang, Y., Zhang, J., 2009. Improved multi-objective adaptive niche genetic algorithm based on Pareto front. Advance Computing Conference, 2009. IACC 2009. IEEE International, 300 - 304.
  11. Streichert, F., 2001. Introduction to evolutionary algorithms. Frankfurt MathFinance Workshop.
  12. Yang, Y., Li, J., Dai, W., 2008. Multi-objective genetic algorithm based on the correlation coefficient and its application. Control and Decision Conference, 3898 - 3902.
  13. Chakraborttya, R., Hasinb M, 2013. Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International Journal of Industrial Engineering Computations, 4, 1- 12.
Download


Paper Citation


in Harvard Style

Malgorzata P. (2014). The Use of Genetic Algorithms in Mobile Applications . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 520-525. DOI: 10.5220/0004952805200525


in Bibtex Style

@conference{iceis14,
author={Plechawska-Wojcik Malgorzata},
title={The Use of Genetic Algorithms in Mobile Applications},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={520-525},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004952805200525},
isbn={978-989-758-027-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - The Use of Genetic Algorithms in Mobile Applications
SN - 978-989-758-027-7
AU - Malgorzata P.
PY - 2014
SP - 520
EP - 525
DO - 10.5220/0004952805200525