Using an Evolutionary Heuristics for Solving the Outdoor Advertising Optimization Problem

Zheldak T.A.^1, and Redko V.¹

¹Department of Systems Analysis and Control, National Mining University, Dnipropetrovs’k, Ukraine

Cite this paper:
Zheldak T.A. and Redko V.. Using an Evolutionary Heuristics for Solving the Outdoor Advertising Optimization Problem. Journal of Computer Sciences and Applications. 2014; 2(2):23-30. doi: 10.12691/jcsa-2-2-2

Abstract

In this paper we consider the problem of selecting a carrier of the plurality of outdoor advertising offers available in the formulation of the knapsack problem, namely, when restricted to the amount of funding. The paper proposes a variant of the heuristic based on the modeling method of artificial immune system of the human body. The basis algorithm immune operators comprise cloning, mutation, selective compression of the memory and repeating the natural processes in lymphocytes. The proposed simulation method of artificial immune systems can be used to solve a wide range of combinatorial search and constrained optimization problems.

Keywords:
artificial immune system (AIS) constrained optimization problems clone selection advertising

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References:

[1]	Paul, C. L., Redman, S., and Sanson-Fisher, R. W. (2003). Print material content and design: Is it relevant to effectiveness? Health Education Research: Theory and practice, ch. 18, pp. 181-190.
[2]	Andriyashkin C. Comparative analysis of Russian and foreign experience of social advertising – Available: http://www.socreklama.ru/sr_article.php?arti_id=81.
[3]	Atkin, C. and Schiller, L. (2002). The impact of public service advertising. In L. Schiller & T. Hoff (Eds.), Shouting to be heard: Public service advertising in a new media age (pp. 21-30). Retrieved February 19, 2003, Available: http://www.kaisernetwork.org/health_cast/uploaded_files/BackgroundPapers.KaiserPSAs.pdf.pdf.
[4]	Golota I. (2007) Issues of state regulation of social advertising. – Available: http://fppr.org.ua/netgorod/SSR.htm.
[5]	Law of Ukraine from 03.07.1996, № 270/967VR “About Advertising”.
[6]	Feige U., Immorlica N., Mirrokni V. and Hamid Nazerzadeh (2008) A combinatorial allocation mechanism with penalties for banner advertising. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 169-178.
[7]	Zhou Y., Chakrabarty D. and Lukose R. (2008) Budget constrained bidding in keyword auctions and online knapsack problems. Internet and Network Economics, volume 5385 of Lecture Notes in Computer Science, pages 566-576.
[8]	Dasgupta S., Papadimitriou C. H. and Vazirani U. (2006) Algorithms. McGraw-Hill Science/Engineering/Math. pp. 336.
[9]	Davis, Joel J. (2012) Advertising Research: Theory and Practice: Second Edition –San Diego State University.
[10]	Çetin, E., Esen, S.T. (2006) A weapon–target assignment approach to media allocation. Applied Mathematics and Computation, Volume 175, Issue 2, Pages 1266-1275.
[11]	Shtovba, S.D., Yakovenko, A.A. and Shtovba, O.V. (2011) Genetic optimization of outdoor advertising brands. Vestnik of Vinnytsia National Technical University. Vol 2. pp. 134-138.
[12]	Martello S. (1990) Knapsack Problems: Algorithms and Computer Implementations / Silvano Martello, Paolo Toth. John Wiley & Sons, 296 p.
[13]	Haupt, R.L., Haupt, S.E. (2004) Practical Genetic Algorithms. New Jersey: John Wiley & Sons, 253 p.
[14]	Tarakanov A. and Dasgupta, D. (2000) A formal model of an artificial immune system. Biosystems, Volume 55, Issues 1–3, Pages 151-158.
[15]	Lucinska M., Wierzchon S.T. (2009) Hybrid Immune Algorithm for Multimodal Function Optimization // Recent Advances in Intelligent Information Systems, pp. 301-313.
[16]	Rowan T.H. (1990) Functional Stability Analysis of Numerical Algorithms, Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin. Available: http://reference.kfupm.edu.sa/content/f/u/functional_stability_analysis_of_numeric_1308737.pdf.
[17]	Molga M., Smutnicki C. (2005) Test functions for optimization needs. Available: http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
[18]	De Castro, L.N., Timmis, J. (2002) An Artificial Immune Network for Multimodal Function Optimization. Proceedings of IEEE Congress on Evolutionary Computation (CEC'02). Hawaii, vol. 1, pp. 699-674.
[19]	Yildiz A.R. (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, vol. 25, pp. 261-270.
[20]	De Castro, L.N., Von Zuben, F.J. (2002) Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, vol. 6, No. 3, pp. 231-259.
[21]	Brownlee, J. (2007) Clonal Selection Algorithms / Technical Report 070209A, Complex Intelligent Systems Laboratory (CIS), Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology. Available: http://pdf.aminer.org/000/315/118/how_to_escape_traps_using_clonal_selection_algorithms.pdf.