Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm

  • Kim Soon Gan
  • Patricia Anthony
  • Jason Teo
  • Kim On Chin
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 5)


Online auctions play an important role in today’s e-commerce for procuring goods. The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. An autonomous agent that exploits a heuristic decision making framework was developed to tackle the problem of bidding across multiple auctions with varying start and end time and with varying protocols (including English, Dutch and Vickrey). This flexible and configurable framework enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user’s preferences. However, the proposed bidding strategy is based on four bidding constraints which is polynomial in nature such that there are infinite solutions that can be deployed at any point in time. Given this large space of possibilities, a genetic algorithm is employed to search offline for effective strategies in particular class of environment. The strategies that emerge from this evolution are then codified into the agent’s reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. In this paper, the parameters of the crossover and mutation are tuned in order to come up with an optimal rate for this particular environment. The proposed framework is implemented in a simulated marketplace environment and its effectiveness is empirically demonstrated. The relative performance of the evolved bidding strategies is discussed in this paper.


Genetic Operators Genetic Algorithms (GAs) Crossover Mutation 


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  1. 1.
    Anthony, P., Jennings, N.R.: Agent for Participating in Multiple Online Auctions. ACM Transaction on Internet Technology 2(3) (2001)Google Scholar
  2. 2.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  3. 3.
    Engelbrecht, A.P.: Computational Intelligence An Introduction. John Wiley & Sons, Chichester (2002)Google Scholar
  4. 4.
    Janikow, C.Z., Michalewiz, Z.: An experimental comparison of Binary and Floating Point Representations in Genetic Algorithms. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the 4th International Conferences in Genetic Algorithms, pp. 31–36. Morgan Kaufmann, San Francisco (1991)Google Scholar
  5. 5.
    Davis, L.: Hybridization and Numerical Representation. In: Davis, L. (ed.) The handbook of Genetic Algorithm, pp. 61–71. Van Nostrand Reinhold, New York (1991)Google Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization & machine learning. Addison Wesley, Reading (1989)MATHGoogle Scholar
  7. 7.
    De Jong, K.A.: Evolutionary computation: A unified approach. MIT Press Book, Cambridge (2006)MATHGoogle Scholar
  8. 8.
    Hinterding, R., Gielewski, H., Peachey, T.C.: The nature of mutation in genetic algorithms. In: Eshelman, L. (ed.) Proceedings of the Sixth ICGA, pp. 65–72. Morgan Kaufmann, San Francisco (1995)Google Scholar
  9. 9.
    Cervantes, J., Stephens, C.R.: Optimal mutation rates for genetic search. In: Proceedings of the 8th Annual Conference on GECCO, Seattle, pp. 1313–1320 (2006)Google Scholar
  10. 10.
    Beasley, D., Bull, D.R., Martin, R.R.: An Overview of Genetic Algorithms: Part 1, Fundamentals. University Computing 15(2), 58–69 (1993)Google Scholar
  11. 11.
    Coley, D.A.: An Introduction to Genetic Algorithms for Scientists and Engineers. World Scientific, New Jersey (1999)Google Scholar
  12. 12.
    Watson, R.A., Jansen, T.: A Building-Block Royal Road Where Crossover is Provably Essential. In: Genetic And Evolutionary Computation Conference Proceedings of the 9th annual conference on Genetic and evolutionary computation, London, England, pp. 1452–1459 (2007)Google Scholar
  13. 13.
    Rand, W., Riolo, R., Holland, J.H.: The effect of crossover on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions. In: Proceedings of the 8th annual conference on, GECCO, Seattle (2006)Google Scholar
  14. 14.
    Eldershaw, Cameron, S.: Using genetic algorithms to Solve the motion planning problem. Journal of Universal Computer Science (April 2000)Google Scholar
  15. 15.
    De Jong, K.A.: The Analysis of the Behaviour of a Class of Genetic Adaptive Systems. Ph.D. dissertation. Department of Computer Science. University of Michigan, Ann ArborGoogle Scholar
  16. 16.
    Grefenstette, J.J.: Optimization of control parameters for genetic algorithm. IEEE Transaction on Systems, Man and Cybernetics 16(1), 122–128 (1975/1986)Google Scholar
  17. 17.
    Schaffer, L., Caruana, R., Eshelman, L., Das, R.: A Study of Control Parameters Affecting Online Performance of Genetic Algorithm for Function Optimization. In: Schaffer, J.D. (ed.) Proceeding 3rd International Conference Genetic Algorithms, pp. 51–60. Morgan Kaufmann, San Francisco (1989)Google Scholar
  18. 18.
    Cliff, D.: Minimal Intelligence Agents for Bargaining Behaviours in Market Environment. Technical Report HPL-97-91. Hewlett Packard Laboratories (1997)Google Scholar
  19. 19.
    Cliff, D.: Genetic optimization of adaptive trading agents for double-auction markets. In: Proceedings Computing Intelligent Financial Engineering (CIFEr), pp. 252–258 (1998)Google Scholar
  20. 20.
    Cliff, D.: ZIP60: Further Explorations in the Evolutionary Design of Trader Agents and Online Auction-Market Mechanisms. IEEE Transactions on Evolutionary Computation (2006)Google Scholar
  21. 21.
    Cliff, D.: Evolution of market mechanism through a continuous space of auction types. In: Proceeding Congress Evolutionary Computation, pp. 2029–2034 (2002)Google Scholar
  22. 22.
    Cliff, D.: Visualizing search-spaces for evolved hybrid auction mechanisms. Presented at the 8th Int. Conf. Simulation and Synthesis of Living Systems (ALifeVIII) Conf. Beyond Fitness: Visualizing Evolution Workshop, Sydney (2002)Google Scholar
  23. 23.
    Choi, J.H., Ahn, H., Han, I.: Utility-based double auction mechanism using genetic algorithms. Expert System Applicationl, 150–158 (2008)Google Scholar
  24. 24.
    Anthony, P.: Bidding Agents for Multiple Heterogeneous Online Auctions. PhD thesis. University of Southampton, UK (2003)Google Scholar
  25. 25.
    Anthony, P., Hall, W., Dang, V.D., Jennings, N.R. : Autonomous agents for participating in multiple online auctions. In: Proc. of the IJCAI Workshop on EBusiness and the Intelligent Web, Seattle (2001)Google Scholar
  26. 26.
    Beasley, D., Bull, D.R., Martin, R.R.: An Overview of Genetic Algorithms: Part 2, Research Topics. University Computing 15(4), 170–181 (1993)Google Scholar
  27. 27.
    Anthony, P., Jennings, N.R.: Evolving bidding strategies for multiple auctions. In: Proceedings of the 15th ECAI, pp. 178–182 (2002)Google Scholar
  28. 28.
    de Silva, U.C., Suzuki, J.: On the Stationary Distribution of Gas with Fixed Crossover Probability. In: Genetic And Evolutionary Computation Conference Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA, pp. 1147–1151 (2005)Google Scholar
  29. 29.
    Alvarez, G.: Can we make genetic algorithms work in high-dimensionality problems? Stanford Exploration Project (SEP) report 112 (2002)Google Scholar
  30. 30.
    Taniguchi, N., Ando, N., Okamoto, M.: Dynamic Vehicle Routing and Scheduling With Real Time Travel Times on Road Network. Journal of the Eastern Asia Society for Transportation Studies 7, 1138–1153 (2007)Google Scholar
  31. 31.
    Ho, W., Ji, P., Dey, P.K.: Optimization of PCB component placements for the collect-and-place machines. The International Journal of Advanced Manufacturing Technology 37(7), 828–836 (2008)CrossRefGoogle Scholar
  32. 32.
    Gen, M., Zhou, G.: A genetic algorithm for the mini-max spanning forest problem. In: Whitley, D., Goldberg, D.E., Cantu-Paz, E., Spector, L., Parmee, L., Beyer, H.-G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference 2000, p. 387. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  33. 33.
    Tsujimura, Y., Gen, M., Syarif, A.: Solving a Nonlinear Side Constrained Transportation Problem by Using Spanning Tree-based Genetic Algorithm with Fuzzy Logic Controller. In: Proceedings of the Congress on Evolutionary Computation 2002, vol. 1, pp. 546–551 (2002)Google Scholar
  34. 34.
    Kim, K., Murray, A.T., Xiao, N.: A multiobjective evolutionary algorithm for surveillance sensor placement. Environment and Planning B: Planning and Design, Pion Ltd., London 35(5), 935–948 (2008)CrossRefGoogle Scholar
  35. 35.
    Kajisha, H.: Synthesis of Self-Replication Cellular Automata Using Genetic Algorithms. In: Proceedings of the IEEE-INNS-ENNS international Joint Conference on Neural Networks (IJCNN 2000), July 24 - 27, vol. 5, p. 5173. IEEE Computer Society, Washington (2000)Google Scholar
  36. 36.
    Scholand, A.J.: GA Parameter (1998),
  37. 37.
    Back, T.: Optimal Mutation Rates in Genetic Search. In: Proceedings of the 5th International Conferences of Genetic Algorithms, pp. 2–8. Morgan Kaufmann, San Francisco (1993)Google Scholar
  38. 38.
    Bingul, Z., Sekmen, A.S., Palaniappan, S., Zein-Sabatto, S.: Genetic algorithms applied to real time multiobjective optimization problems. In: Proceedings of the 2000 {IEEE} SouteastCon Conference (SoutheastCON 2000), pp. 95–103 (2000)Google Scholar
  39. 39.
    Qin, L., Yang, S.X., Pollari, F., Dore, K., Fazil, A., Ahmed, R., Buxton, J., Grimsrud, K.: Genetic algorithm based neural classifier for factor subset extraction. Soft Computing - A Fusion of Foundations, Methodologies and Applications 12(7), 623–632 (2008)Google Scholar
  40. 40.
    Yamamoto, M., Zaier, R., Chen, P., Toyota, T.: Decision-making method of optimum inspection interval for plant maintenance by genetic algorithms. In: Proceedings of EcoDesign 2001: Second International Symposium on Environmentally Conscious Design and Inverse Manufacturing, pp. 466–469 (2001)Google Scholar
  41. 41.
    Shieh, H.M., May, M.D.: Solving the capacitated clustering problem with genetic algorithms. Journal of the Chinese Institute of Industrial Engineers 18(3), 1–12 (2001)Google Scholar
  42. 42.
    Dagli, C.H., Schierholt, K.: Evaluating the performance of the genetic neuro scheduler using constant as well as changing crossover and mutation rates. In: Proceedings of the 21st International Conference on Computers and Industrial Engineering, pp. 253–256 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kim Soon Gan
    • 1
  • Patricia Anthony
    • 1
  • Jason Teo
    • 1
  • Kim On Chin
    • 1
  1. 1.Universiti Malaysia SabahKota KinabaluMalaysia

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