ESTIMATES FROM EVOLUTIONARY ALGORITHMS THEORY APPLIED
 TO DIRECTED EVOLUTION

Abstract

The field of evolutionary computation emerged in the area of computer science due to transfer of ideas from biology and developed independently for several decades, enriched with techniques from probability theory, complexity theory and optimization methods. Our aim is to consider how some recent results form the theory of evolutionary computation may be transferred back into biology. It has been noted that
 the non-elitist evolutionary algorithms optimizing Royal Road fitness functions may be considered as models of evolutionary search for the synthetic enhancer sequences “from scratch”. This problem asks for a tight cluster of supposedly unknown motifs
 from the initial random (or partially random) set of DNA sequences using SELEX approaches. We apply the upper bounds on the expected hitting time of a target area of genotypic space in order to upper-bound the expected time to finding a sufficiently fit series of motifs in a SELEX procedure. On the other hand, using the theory of evolutionary computation, we propose an upper bound on the expected proportion of the DNA sequences with sufficiently high fitness at a given round of a SELEX procedure. Both approaches are evaluated in computational experiment, using a Royal Road fitness function as a model of the SELEX procedure for regulatory FIS factor
 binding site.