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Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations

, , , and . GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, page 1043--1047. New York, Morgan Kaufmann Publishers, (9-13 July 2002)

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Soft Sensor Development Using Genetic Programming, and . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), page 1346--1351. San Francisco, California, USA, Morgan Kaufmann, (7-11 July 2001)Industrial evolutionary computing., , and . GECCO (Companion), page 3297-3322. ACM, (2007)Biomass Inferential Sensor Based on Ensemble of Models Generated by Genetic Programming., , , , , , and . GECCO (2), volume 3103 of Lecture Notes in Computer Science, page 1078-1089. Springer, (2004)Symbolic regression modeling of blown film process effects., and . IEEE Congress on Evolutionary Computation, page 561-568. IEEE, (2004)Competitive advantages of evolutionary computation for industrial applications., , , , , and . Congress on Evolutionary Computation, page 166-173. IEEE, (2005)Marketing computational intelligence in industry.. IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2010)Adaptive Inferential Sensors Based on Evolving Fuzzy Models., and . IEEE Trans. Syst. Man Cybern. Part B, 40 (2): 529-539 (2010)Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations., , , and . GECCO, page 1043-1047. Morgan Kaufmann, (2002)How Competitive Is Genetic Programming in Business Data Science Applications?, , and . GPTP, page 145-163. Springer, (2019)Applied Artificial Intelligence-Based Systems as Competitive Advantage.. IEEE Conf. on Intelligent Systems, page 6-18. IEEE, (2020)