@conference { koza:1998:pmGPcp,
	title = {Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Extracellular and Membrane Cellular Location},
	editor = {V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben},
	volume = {1447},
	year = {1998},
	month = {"25-27 " # mar},
	publisher = {Springer-Verlag},
	type = {inproceedings},
	address = {Mission Valley Marriott, San Diego, California, USA},
	abstract = {As newly sequenced proteins are deposited into the
                 world's ever-growing archive of protein sequences, they
                 are typically immediately tested by various algorithms
                 for clues as to their biological structure and
                 function. One question about a new protein involves its
                 cellular location &shyp; that is, where the protein
                 resides in a living organism (extracellular, membrane,
                 etc.). A human-created five-way algorithm for cellular
                 location using statistical techniques with 76% accuracy
                 was recently reported. This paper describes a two-way
                 algorithm that was evolved using genetic programming
                 with 83% accuracy for determining whether a protein is
                 extracellular and with 89% accuracy for membrane
                 proteins. Unlike the statistical calculation, the
                 genetically evolved algorithm employs a large and
                 varied arsenal of computational capabilities, including
                 arithmetic functions, conditional operations,
                 subroutines, iterations, memory, data structures,
                 set-creating operations, macro definitions, recursion,
                 etc. The genetically evolved classification algorithm
                 can be viewed as an extension (which we call a
                 programmatic motif) of the conventional notion of a
                 protein motif.},
	keywords = {genetic algorithms, genetic programming},
	ISBN = {3-540-64891-7},
	URL = {http://www.genetic-programming.com/jkpdf/ep1998.pdf},
	author = { Koza, John  and  Bennett, Forrest  and  Andre, David}
}