Abstract
We introduce hedonic, modeling benchmarks for the field of sensory science evaluation. Our benchmark framework provides a general means of defining a response surface which we call a “sensory map”. A sensory map is described by a mathematical expression which rationalizes domain specific knowledge of the explanatory variables and their individual or higher order contribution to hedonic, response. The benchmark framework supports the sensory map’s socalled ground truth to be controllably distorted to mimic the human and protocol factors that obscure it. To provide a baseline for future algorithm comparison, we evaluate a public research release of genetic programming symbolic regression algorithm on a sampling of the framework’s benchmarks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Becker, Ying, Fei, Peng, and Lester, AnnaM. (2006). Stock selection : An innovative application of genetic programming methodology. In Riolo, Rick L., Soule, Terence, andWorzel, Bill, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic andEvolutionary Computation, chapter 12, pages 315–334. Springer, Ann Arbor.
Becker,Ying L., Fox,Harold, and Fei, Peng (2007).An empirical study ofmultiobjective algorithms for stock ranking. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 14, pages 239–259. Springer, Ann Arbor.
Becker, Ying L. andO’Reilly, Una-May (2009). Genetic programming for quantitative stock selection. In Xu, Lihong et al., editors, GEC ’09: Proceedings of the first ACM/SIGEVOSummit on Genetic and Evolutionary Computation, pages 9–16, Shanghai, China. ACM. Costello, E., McGinty, L., Burland, M., and Smyth, B. (2007). The role of recommendation for flavor innovation and discovery. In IC-AI, pages 463– 469.
Keijzer, Maarten (2003). Improving symbolic regression with interval arithmetic and linear scaling. In Ryan, Conor et al., editors, Genetic Programming, Proceedings of EuroGP’2003, volume 2610 of LNCS, pages 70–82, Essex. Springer-Verlag.
Korns, Michael (2011). Accuracy in symbolic regression. Genetic and Evolutionary Computation, chapter 8. Springer, Ann Arbor.
Kotanchek, Mark E., Vladislavleva, Ekaterina Y., and Smits, Guido F. (2009). Symbolic regression via GP as a discovery engine: Insights on outliers and prototypes. In Riolo, Rick L., O’Reilly, Una-May, and McConaghy, Trent, editors, Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation, chapter 4, pages 55–72. Springer, Ann Arbor.
Leibowitz, HW and Post, RB (1982). Capabilities and limitations of the human being as a sensor. Selected sensory methods: problems and approaches tomeasuringhedonics, page 2.
Moskowitz, HR (1982). Utilitarian benefits of magnitude estimation scaling for testing product acceptability. In Selected sensory methods: problems and approaches to measuring hedonics: a symposium, page 11. ASTM International.
Nikolaev, Nikolay and Iba, Hitoshi (2001). Genetic programming using chebishev polynomials. In Spector, Lee et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 89–96, San Francisco, California, USA. Morgan Kaufmann. Riskey, DR (1982). Effects of context and interstimulus procedures in judgments of saltiness and pleasantness. In Selected sensory methods: problems and approaches to measuring hedonics: a symposium, page 71. ASTM International.
Schmidt, Michael D. and Lipson, Hod (2006). Actively probing and modeling users in interactive coevolution. In Proceedings of the 8th annual conference onGenetic and evolutionary computation,GECCO’06, pages 385–386,New York, NY, USA. ACM.
Silva, S. (2011). http://gplab.sourceforge.net/index.html. GPLab v.3 April 2007.
Silva, S. and Almeida, J. (2003). GPLAB-a genetic programming toolbox for MATLAB. In Proceedings of the Nordic MATLAB Conference, pages 273– 278.
Veeramachaneni, Kalyan, Vladislavleva, Katya, Burland, Matt, Parcon, Jason, and O’Reilly, Una-May (2010). Evolutionary optimization of flavors. In Branke, Juergen et al., editors, GECCO ’10: Proceedings of the 12th annual conference onGenetic and evolutionary computation, pages 1291–1298, Portland, Oregon, USA. ACM.
Vladislavleva, Ekaterina J., Smits,Guido F., and denHertog,Dick (2009).Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Transactions on Evolutionary Computation, 13(2):333–349.
Vladislavleva, Katya, Veeramachaneni, Kalyan, Burland, Matt, Parcon, Jason, and O’Reilly, Una-May (2010a). Knowledge mining with genetic programming methods for variable selection in flavor design. In Branke, Juergen et al., editors, GECCO ’10: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 941–948, Portland, Oregon, USA. ACM.
Vladislavleva, Katya, Veeramachaneni, Kalyan, and O’Reilly, Una-May (2010b). Learning a lot from only a little:Genetic programming for panel segmentation on sparse sensory evaluation data. In Esparcia-Alcazar, Anna Isabel et al., editors, Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, volume 6021 of LNCS, pages 244–255, Istanbul. Springer.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Noel, PL., Veeramachaneni, K., O’Reilly, UM. (2011). Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling. In: Riolo, R., Vladislavleva, E., Moore, J. (eds) Genetic Programming Theory and Practice IX. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1770-5_10
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1770-5_10
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1769-9
Online ISBN: 978-1-4614-1770-5
eBook Packages: Computer ScienceComputer Science (R0)