@inproceedings{kotancheck:2002:gecco,
	title = {Evolutionary Computing in {Dow Chemical}},
	address = {New York, New York},
	author = {Mark Kotanchek and Arthur Kordon and Guido Smits and Flor Castillo and R. Pell and M. B. Seasholtz and L. Chiang and P. Margl and P. K. Mercure and A. Kalos},
	booktitle = {GECCO-2002 Presentations in the Evolutionary
                 Computation in Industry Track},
	editor = {Lawrence ``Dave'' Davis and Rajkumar Roy},
	month = {11-13 July},
	pages = {101--110},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2fb2e0aa9145e2450a958922e12d9bb61/brazovayeye},
	organisation = {ISGEC}, notes = {powerpoint slides? Diverse subsets from chemical
                 libraries. Soft sensors in intelligent alarm
                 processing. Polypropylene structure-property
                 relationships. non-linear DOE (design of experiments)
                 using GP/GENPRO See also \cite{kordon:2002:gecco}},
	keywords = {PSO, SVM algorithms, genetic machines, networks, neural particle programming, support swarm, vector }
}

@incollection{kordon:2005:GPTP,
	title = {Application Issues of Genetic Programming in
                 Industry},
	address = {Ann Arbor},
	author = {Arthur Kordon and Flor Castillo and Guido Smits and Mark Kotanchek},
	booktitle = {Genetic Programming Theory and Practice {III}},
	chapter = {16},
	editor = {Tina Yu and Rick L. Riolo and Bill Worzel},
	month = {12-14 May},
	pages = {241--258},
	publisher = {Springer},
	series = {Genetic Programming},
	volume = {9},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/2d5bb90b233d2c509e6ffa7c5f525aa9d/brazovayeye},
	abstract = {The chapter gives a systematic view, based on the
                 experience from The Dow Chemical Company, of the key
                 issues for applying symbolic regression with Genetic
                 Programming (GP) in industrial problems. The
                 competitive advantages of GP are defined and several
                 industrial problems appropriate for GP are recommended
                 and referenced with specific applications in the
                 chemical industry. A systematic method for selecting
                 the key GP parameters, based on statistical design of
                 experiments, is proposed. The most significant
                 technical and non-technical issues for delivering a
                 successful GP industrial application are discussed
                 briefly.},
	size = {18 pages}, isbn = {0-387-28110-X}, notes = {part of \cite{yu:2005:GPTP} Published Jan 2006 after
                 the workshop},
	keywords = {algorithms, applications, design experiments, genetic industrial of parameter problems, programming, real regression, selection symbolic world }
}

@inproceedings{1144264,
	title = {Pareto front genetic programming parameter selection
                 based on design of experiments and industrial data},
	address = {Seattle, Washington, USA},
	author = {Flor Castillo and Arthur Kordon and Guido Smits and Ben Christenson and Dee Dickerson},
	booktitle = {{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation},
	editor = {Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens},
	month = {8-12 July},
	pages = {1613--1620},
	publisher = {ACM Press},
	url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1613.pdf},
	volume = {2},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2fe8d2a2b3519e75c9d284be5b574a2c8/brazovayeye},
	organisation = {ACM SIGEVO (formerly ISGEC)}, publisher_address = {New York, NY, 10286-1405, USA}, isbn = {1-59593-186-4}, notes = {GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060}, doi = {doi:10.1145/1143997.1144264},
	keywords = {Applications, Pareto Real-World algorithms, applications, design experiments, front, genetic industrial of programming, regression statistical symbolic }
}

@incollection{Castillo:2006:GPTP,
	title = {Robust Pareto Front Genetic Programming Parameter
                 Selection Based on Design of Experiments and Industrial
                 Data},
	address = {Ann Arbor},
	author = {Flor Castillo and Arthur Kordon and Guido Smits},
	booktitle = {Genetic Programming Theory and Practice {IV}},
	chapter = {2},
	editor = {Rick L. Riolo and Terence Soule and Bill Worzel},
	month = {11-13 May},
	pages = {-},
	publisher = {Springer},
	series = {Genetic and Evolutionary Computation},
	volume = {5},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/22bd2d3ffb96c7472097d2f3fd13e9fc4/brazovayeye},
	abstract = {Symbolic regression based on Pareto front GP is a very
                 effective approach for generating high-performance
                 parsimonious empirical models acceptable for industrial
                 applications. The chapter addresses the issue of
                 finding the optimal parameter settings of Pareto front
                 GP which direct the simulated evolution toward simple
                 models with acceptable prediction error. A generic
                 methodology based on statistical design of experiments
                 is proposed. It includes determination of the number of
                 replicates by half-width confidence intervals,
                 determination of the significant factors by fractional
                 factorial design of experiments, approaching the
                 optimum by steepest ascent/descent, and local
                 exploration around the optimum by Box Behnken design of
                 experiments. The results from implementing the proposed
                 methodology to different types of industrial data sets
                 show that the statistically significant factors are the
                 number of cascades, the number of generations, and the
                 population size. The optimal values for the three
                 parameters have been defined based on second order
                 regression models with R2 higher than 0.97 for small,
                 medium, and large-sized data sets. The robustness of
                 the optimal parameters toward the types of data sets
                 was explored and a robust setting for the three
                 significant parameters was obtained. It reduces the
                 calculation time by 30per cent to 50per cent without
                 statistically significant reduction in the mean
                 response.},
	size = {18 pages}, isbn = {0-387-33375-4}, notes = {part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop},
	keywords = {algorithms, applications, design experiments, genetic industrial of parameter programming, regression, selection symbolic }
}

@inproceedings{castillo:2004:ueatsvtilmls,
	title = {Using Evolutionary Algorithms to Suggest Variable
                 Transformations in Linear Model Lack-of-Fit
                 Situations},
	address = {Portland, Oregon},
	author = {Flor Castillo and Jeff Sweeney and Wayne Zirk},
	booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
                 Computation},
	month = {20-23 June},
	pages = {556--560},
	publisher = {IEEE Press},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/298c3809b474342fd35b6a4cb71a45f1f/brazovayeye},
	abstract = {When significant model lack of fit (LOF) is present in
                 a second-order linear regression model, it is often
                 difficult to propose the appropriate parameter
                 transformation that will make model LOF insignificant.
                 This paper presents the potential of genetic
                 programming (GP) symbolic regression for reducing or
                 eliminating significant second-order linear model LOF.
                 A case study in an industrial setting at The Dow
                 Chemical Company is presented to illustrate this
                 methodology.},
	isbn = {0-7803-8515-2}, notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.},
	keywords = {Computing Evolutionary Industry Process algorithms, genetic in programming, the }
}

@incollection{castillo:2004:GPTP,
	title = {Using Genetic Programming in Industrial Statistical
                 Model Building},
	address = {Ann Arbor},
	author = {Flor Castillo and Arthur Kordon and Jeff Sweeney and Wayne Zirk},
	booktitle = {Genetic Programming Theory and Practice {II}},
	chapter = {3},
	editor = {Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel},
	month = {13-15 May},
	pages = {31--48},
	publisher = {Springer},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/2c35a2882bf7bda1234b4e495259640ca/brazovayeye},
	isbn = {0-387-23253-2}, notes = {part of \cite{oreilly:2004:GPTP2}},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Castillo:2003:gecco,
	title = {A Methodology for Combining Symbolic Regression and
                 Design of Experiments to Improve Empirical Model
                 Building},
	address = {Chicago},
	author = {Flor Castillo and Kenric Marshall and James Green and Arthur Kordon},
	booktitle = {Genetic and Evolutionary Computation -- GECCO-2003},
	editor = {E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller},
	month = {12-16 July},
	pages = {1975--1985},
	publisher = {Springer-Verlag},
	series = {LNCS},
	volume = {2724},
	year = {2003},
	biburl = {http://www.bibsonomy.org/bibtex/2e88dea4497718e64b262ecfd45197448/brazovayeye},
	abstract = {A novel methodology for empirical model building using
                 GP-generated symbolic regression in combination with
                 statistical design of experiments as well as undesigned
                 data is proposed. The main advantage of this
                 methodology is the maximum data usage when
                 extrapolation is necessary. The methodology offers
                 alternative non-linear models that can either linearize
                 the response in the presence of Lack or Fit or
                 challenge and confirm the results from the linear
                 regression in a cost effective and time efficient
                 fashion. The economic benefit is the reduced number of
                 additional experiments in the presence of Lack of
                 Fit.},
	publisher_address = {Berlin}, isbn = {3-540-40603-4}, notes = {GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eights Annual Genetic Programming
                 Conference (GP-2003)},
	keywords = {Applications Real World algorithms, design experiments, genetic of programming, regression, symbolic }
}

@inproceedings{castillo:2002:gecco,
	title = {Symbolic Regression In Design Of Experiments: {A} Case
                 Study With Linearizing Transformations},
	address = {New York},
	author = {Flor A. Castillo and Ken A. Marshall and James L. Green and Arthur K. Kordon},
	booktitle = {GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference},
	editor = {W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska},
	month = {9-13 July},
	pages = {1043--1047},
	publisher = {Morgan Kaufmann Publishers},
	url = {http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2de9f908f3d93d77f6c2da7562248fa63/brazovayeye},
	publisher_address = {San Francisco, CA 94104, USA}, isbn = {1-55860-878-8}, notes = {GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)},
	keywords = {(DoE), applications, design experiment fit, genetic lack linearizing of programming, real regression symbolic transformations, world }
}

@book{schröder*1957-*1992,
	title = {Jenseits des Marktes : Ansätze öko-sozialen Wirtschaftens aus neo-libertärer Sicht},
	address = {Haag + Herchen},
	annote = {158, XXXIII S},
	author = {{Rolf} Schröder*1957-* and {S.} Flor},
	howpublished = {Frankfurt am Main},
	url = {http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+127934901&sourceid=fbw_bibsonomy},
	year = {1992},
	biburl = {http://www.bibsonomy.org/bibtex/28d58d4c33bfb03f30ac1dcd824ec366a/fbw},
	description = {imported},
	isbn = {3-89228-759-7},
	keywords = {imported }
}

@article{Arribas2006,
	title = {Oscillators in resonance p:q:r},
	author = {M. Arribas and A. Elipe and L. Flor{\'i}a and A. Riaguas},
	journal = {Chaos, Solitons \& Fractals},
	month = {Mar},
	number = {5},
	pages = {1220--1228},
	url = {http://www.sciencedirect.com/science/article/B6TJ4-4GP1VBY-1/1/0facc141fb41235f020c5408115f0b63},
	volume = {27},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/20cd20a6a8d0de6f79fe5e2130af6b58e/smicha},
	description = {Chaos, Solitons & Fractals},
	keywords = {imported }
}