Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{Koza:1994:rppsGP,

• author = "J. R. Koza",
• title = "Recognizing patterns in protein sequences using {iteration-performing} calculations in genetic programming",
• booktitle = "1994 IEEE World Congress on Computational Intelligence",
• year = "1994",
• volume = "1",
• pages = "244--249",
• address = "Orlando, Florida, USA",
• month = "27-29 " # jun,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, memory",
• URL = "http://www.genetic-programming.com/jkpdf/icec1994.pdf",
• DOI = "10.1109/ICEC.1994.350008",
• size = "7 pages",
• abstract = "This paper uses genetic programming with automatically defined functions (ADFs) for the dynamic creation of a pattern-recognising computer program consisting of initially-unknown detectors, an initially-unknown iterative calculation incorporating the as-yet-undiscovered detectors, and an initially-unspecified final calculation incorporating the results of the as-yet-unspecified iteration. The program's goal is to recognise a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. Genetic programming with automatic function definition is given a training set of differently-sized mouse protein segments and their correct classification. Correlation is used as the fitness measure. Automatic function definition enables genetic programming to dynamically create subroutines (detectors). A restricted form of iteration is introduced to enable genetic programming to perform calculations on the values returned by the detectors. When cross-validated, the best genetically-evolved recogniser for transmembrane domains achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6percent. This error rate is better than that recently reported for five other methods.",

}

Genetic Programming entries for John Koza

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