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Induction over Strategic Agents: a genetic algorithm solution

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Abstract

We study the problem called Induction over Strategic Agents. This problem has proven hard to solve, even for small problems. We start by reducing the problem to an unconstrained search over w∈ℜ n. Once we accomplish this, we develop a Genetic Algorithm to perform this search. We compare our results to those obtained on a Mixed Integer formulation.

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References

  • Boylu, F. (2006). Startegic learning dissertation. Decision and Information Sciences, University of Florida, Gainesville.

  • Boylu, F., Aytug, H., & Koehler, G. J. (2007). Induction over constrained Strategic Agents. Decision and Information Sciences. Gainesville: University of Florida.

    Google Scholar 

  • Boylu, F., Aytug, H., & Koehler, G. J. (2008, forthcoming). Induction over Strategic Agents. Information Systems Research.

  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based methods. Cambridge: Cambridge University Press.

    Google Scholar 

  • Dalvi, N., Domingos, P., Sanghai, M.S., & Verma, D., (2004). Adversarial classification. In Tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD) (pp. 99–108). Seattle

  • Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In J. D. Schaefer (Ed.), Proceedings of the third international conference on genetic algorithms (pp. 61–69). Los Altos: Morgan Kaufman.

    Google Scholar 

  • DeJong, K. A., & Spears, W. M. (1990). An analysis of the interacting roles of population size and crossover in genetic algorithms. In Proc. first workshop parallel problem solving from nature (pp. 38–47). Berlin: Springer.

    Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization & machine learning. Reading: Addison-Wesley.

    Google Scholar 

  • ILOG (2005). ILOG CPLEX. Reference manual and user manual. V9.5. Gentilly: ILOG.

    Google Scholar 

  • Vapnik, V. (1998). Statistical learning theory. New York: Wiley.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Operations and Information Management, School of Business, University of Connecticut, Stamford, CT, 06901, USA

    Fidan Boylu

  2. Information Systems and Operations Management Department, 351 BUS, The Warrington College of Business Administration, University of Florida, Gainesville, FL, 32611, USA

    Haldun Aytug & Gary J. Koehler

Authors
  1. Fidan Boylu
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  2. Haldun Aytug
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  3. Gary J. Koehler
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Correspondence to Fidan Boylu.

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Boylu, F., Aytug, H. & Koehler, G.J. Induction over Strategic Agents: a genetic algorithm solution. Ann Oper Res 174, 135–146 (2010). https://doi.org/10.1007/s10479-008-0485-0

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  • DOI: https://doi.org/10.1007/s10479-008-0485-0

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