2008/10/19 by Keki M. Burjorjee, Burjorjee, Keki M.
Computer Science · #F.2.m #FOS: Computer and information sciences #I.2.8 #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.0810.3357
Sharpened motivation, improved notation
arxiv created 2009/03/31 · arxiv updated 2009/12/01
Since the inception of genetic algorithmics the identification of computational efficiencies of the simple genetic algorithm (SGA) has been an important goal. In this paper we distinguish between a computational competency of the SGA--an efficient, but narrow computational ability--and a computational proficiency of the SGA--a computational ability that is both efficient and broad. Till date, attempts to deduce a computational proficiency of the SGA have been unsuccessful. It may, however, be possible to inductively infer a computational proficiency of the SGA from a set of related computational competencies that have been deduced. With this in mind we deduce two computational competencies of the SGA. These competencies, when considered together, point toward a remarkable computational proficiency of the SGA. This proficiency is pertinent to a general problem that is closely related to a well-known statistical problem at the cutting edge of computational genetics.