@inproceedings{LeyuanShi:1998:hGA,
	title = {A New Hybrid Genetic Algorithm},
	address = {University of Wisconsin, Madison, Wisconsin, USA},
	author = {Leyuan Shi and Sigurdur Olafsson},
	booktitle = {Late Breaking Papers at the Genetic Programming 1998
                 Conference},
	editor = {John R. Koza},
	month = {22-25 July},
	publisher = {Stanford University Bookstore},
	url = {http://citeseer.ist.psu.edu/100318.html},
	year = {1998},
	biburl = {http://www.bibsonomy.org/bibtex/2fdcf175d26835e4ae6665a3cf9582b23/brazovayeye},
	notes = {GP-98LB}, publisher_address = {Stanford, CA, USA},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Ruihua:2003:PADM,
	title = {Genetic programming for partial discharge feature
                 construction in large generator diagnosis},
	author = {Li Ruihua and Xie Hengkun and Gao Naikui and Shi Weixiang},
	booktitle = {Properties and Applications of Dielectric Materials,
                 2003. Proceedings of the 7th International Conference
                 on},
	month = {1-5 June},
	pages = {258--261},
	volume = {1},
	year = {2003},
	biburl = {http://www.bibsonomy.org/bibtex/2fb3cb8a7e40e20a7149c6d3698332d77/brazovayeye},
	organisation = {IEEE}, notes = {real data on artificial defects. TE571},
	keywords = {algorithms, extraction feature genetic programming, stator, }
}

@article{Jin_GP_Wavelet,
	title = {Genetic Programming with Wavelet-Based Indicators for
                 Financial Forecasting},
	author = {Jin Li and Zhu Shi and Xiaoli Li},
	journal = {Transactions of the Institute of Measurement and
                 Control},
	month = {August},
	number = {3},
	pages = {285--297},
	url = {http://tim.sagepub.com/content/vol28/issue3/},
	volume = {28},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/21479875c7618d52df36fa285c9166ca6/brazovayeye},
	abstract = {Wavelet analysis, as a promising technique, has been
                 used to approach numerous problems in science and
                 engineering. Recent years have witnessed its novel
                 application in economic and finance. This paper is to
                 investigate whether features (or indicators) extracted
                 using the wavelet analysis technique could improve
                 financial forecasting by means of Financial Genetic
                 Programming (FGP), a genetic programming based
                 forecasting tool (i.e., Li, 2001). More specifically,
                 to predict whether Down Jones Industrial Average (DJIA)
                 Index will rise by 2.2per cent or more within the next
                 21 trading days, we first extract some indicators based
                 on wavelet coefficients of the DJIA time series using a
                 discrete wavelet transform; we then feed FGP with those
                 wavelet-based indicators to generate decision trees and
                 make predictions. By comparison with the prediction
                 performance of our previous study (i.e., Li and Tsang,
                 2000), it is suggested that wavelet analysis be capable
                 of bringing in promising indicators, and improving the
                 forecasting performance of FGP.},
	keywords = {algorithms, analysis, financial forecasting genetic programming, wavelet }
}

@article{Hung:2006:NA,
	title = {Alignment using genetic programming with causal trees
                 for identification of protein functions},
	author = {Chun-Min Hung and Yueh-Min Huang and Ming-Shi Chang},
	journal = {Nonlinear Analysis},
	month = {1 September},
	number = {5},
	pages = {1070--1093},
	volume = {65},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2b974d1297a79901a58fff6dbc30f1150/brazovayeye},
	abstract = {A hybrid evolutionary model is used to propose a
                 hierarchical homology of protein sequences to identify
                 protein functions systematically. The proposed model
                 offers considerable potentials, considering the
                 inconsistency of existing methods for predicting novel
                 proteins. Because some novel proteins might align
                 without meaningful conserved domains, maximising the
                 score of sequence alignment is not the best criterion
                 for predicting protein functions. This work presents a
                 decision model that can minimise the cost of making a
                 decision for predicting protein functions using the
                 hierarchical homologies. Particularly, the model has
                 three characteristics: (i) it is a hybrid evolutionary
                 model with multiple fitness functions that uses genetic
                 programming to predict protein functions on a distantly
                 related protein family, (ii) it incorporates modified
                 robust point matching to accurately compare all feature
                 points using the moment invariant and thin-plate spline
                 theorems, and (iii) the hierarchical homologies holding
                 up a novel protein sequence in the form of a causal
                 tree can effectively demonstrate the relationship
                 between proteins. This work describes the comparisons
                 of nucleocapsid proteins from the putative polyprotein
                 SARS virus and other coronaviruses in other hosts using
                 the model.},
	notes = {Hybrid Systems and Applications}, doi = {doi:10.1016/j.na.2005.09.048},
	keywords = {algorithms, genetic programming }
}