3 Koza, John Bennett, Forrest Andre, David 1998 V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben LNCS Mission Valley Marriott, San Diego, California, USA Springer-Verlag 1447 "25-27 " # mar 3-540-64891-7 genetic algorithms, genetic programming 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. http://www.genetic-programming.com/jkpdf/ep1998.pdf