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Evolutionary Computing in Dow Chemical

, , , , , , , , , and . GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, page 101--110. New York, New York, (11-13 July 2002)

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Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data, , and . Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 2, Springer, Ann Arbor, (11-13 May 2006)Application Issues of Genetic Programming in Industry, , , and . Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 16, Springer, Ann Arbor, (12-14 May 2005)Evolutionary Computing in Dow Chemical, , , , , , , , , and . GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, page 101--110. New York, New York, (11-13 July 2002)Using Genetic Programming in Industrial Statistical Model Building, , , and . Genetic Programming Theory and Practice II, chapter 3, Springer, Ann Arbor, (13-15 May 2004)Competitive advantages of evolutionary computation for industrial applications., , , , , and . Congress on Evolutionary Computation, page 166-173. IEEE, (2005)Symbolic regression in multicollinearity problems., and . GECCO, page 2207-2208. ACM, (2005)Pareto front genetic programming parameter selection based on design of experiments and industrial data., , , , and . GECCO, page 1613-1620. ACM, (2006)A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building., , , and . GECCO, volume 2724 of Lecture Notes in Computer Science, page 1975-1985. Springer, (2003)Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations., , , and . GECCO, page 1043-1047. Morgan Kaufmann, (2002)Evaluating and ranking patents with multiple criteria: How many criteria are required to find the most promising patents?, , , , and . Comput. Chem. Eng., (2019)