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Using an artificial neural network trained with a genetic algorithm to model brand share

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  • Fish, Kelly E.
  • Johnson, John D.
  • Dorsey, Robert E.
  • Blodgett, Jeffery G.

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  • Fish, Kelly E. & Johnson, John D. & Dorsey, Robert E. & Blodgett, Jeffery G., 2004. "Using an artificial neural network trained with a genetic algorithm to model brand share," Journal of Business Research, Elsevier, vol. 57(1), pages 79-85, January.
    • Handle: RePEc:eee:jbrese:v:57:y:2004:i:1:p:79-85
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    References listed on IDEAS

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    1. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    2. P. (Sundar) Balakrishnan & Martha Cooper & Varghese Jacob & Phillip Lewis, 1994. "A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 509-525, December.
    3. Rajeev Kohli & Ramesh Krishnamurti, 1987. "A Heuristic Approach to Product Design," Management Science, INFORMS, vol. 33(12), pages 1523-1533, December.
    4. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    5. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
    6. P. V. (Sundar) Balakrishnan & Varghese S. Jacob, 1996. "Genetic Algorithms for Product Design," Management Science, INFORMS, vol. 42(8), pages 1105-1117, August.
    7. Kohli, Rajeev & Krishnamurti, Ramesh, 1989. "Optimal product design using conjoint analysis: Computational complexity and algorithms," European Journal of Operational Research, Elsevier, vol. 40(2), pages 186-195, May.
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    Cited by:

    1. Y Hayashi & M-H Hsieh & R Setiono, 2009. "Predicting consumer preference for fast-food franchises: a data mining approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1221-1229, September.
    2. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
    3. Ringle, Christian M. & Sarstedt, Marko & Schlittgen, Rainer & Taylor, Charles R., 2013. "PLS path modeling and evolutionary segmentation," Journal of Business Research, Elsevier, vol. 66(9), pages 1318-1324.
    4. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    5. Kaiser, Carolin & Ahuvia, Aaron & Rauschnabel, Philipp A. & Wimble, Matt, 2020. "Social media monitoring: What can marketers learn from Facebook brand photos?," Journal of Business Research, Elsevier, vol. 117(C), pages 707-717.
    6. Saeed Rasekhi, 2011. "Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries," Journal of Economics and Behavioral Studies, AMH International, vol. 3(6), pages 352-359.
    7. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2015. "Improving productivity using a multi-objective optimization of robotic trajectory planning," Journal of Business Research, Elsevier, vol. 68(7), pages 1429-1431.

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