Computational Complexity as an Ultimate Constraint on Evolution

Complexity Digest http://comdig.unam.mx/2019/03/16/computational-complexity-as-an-ultimate-constraint-on-evolution/

Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima — thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semi-smooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics — even ones that do not follow adaptive paths — can find a…

source: http://comdig.unam.mx/2019/03/16/computational-complexity-as-an-ultimate-constraint-on-evolution/