@inproceedings{oai:CiteSeerPSU:503706,
	title = {Evolving Protein Motifs Using a Stochastic Regular
                 Language with Codon-Level Probabilities},
	address = {The Banff Centre for Conferences, Box 1020, 107 Tunnel
                 Mountain Drive, Banff, Alberta, T0L 0C0, Canada},
	annote = {The Pennsylvania State University CiteSeer Archives},
	author = {Brian J. Ross},
	booktitle = {6th IASTED International Conference, Artificial
                 Intelligence and Soft Computing, ASC 2002},
	month = {17-19 July},
	url = {http://citeseer.ist.psu.edu/503706.html},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2e803f67fc5b9cec78f15931414c574ad/brazovayeye},
	abstract = {Experiments involving the evolution of protein motifs
                 using genetic programming are presented. The motifs use
                 a stochastic regular expression language that uses
                 codon-level probabilities within conserved sets
                 (masks). Experiments compared basic genetic programming
                 with Lamarckian evolution, as well as the use of
                 {"}natural{"} probability distributions for masks
                 obtained from the sequence database. It was found that
                 Lamarckian evolution was detrimental to the probability
                 performance of motifs. A comparison of evolved and
                 natural mask probability schemes is inconclusive, since
                 these strategies produce incompatible characterisations
                 of motif fitness as used by the genetic programming
                 system.},
	citeseer-references = {oai:CiteSeerPSU:42914; oai:CiteSeerPSU:212791;
                 oai:CiteSeerPSU:215947; oai:CiteSeerPSU:331862;
                 oai:CiteSeerPSU:503937; oai:CiteSeerPSU:506252}, citeseer-isreferencedby = {oai:CiteSeerPSU:79088}, organisation = {The International Association of Science and
                 Technology for Development (IASTED)}, rights = {unrestricted}, oai = {oai:CiteSeerPSU:503706}, language = {en},
	keywords = {algorithms, expressions, genetic motif programming, protein regular stochastic }
}