  In his seminal work with NK algorithms, Kauffman noted that fitness outcomes
from algorithms navigating an NK landscape show a sharp decline at high
complexity arising from pervasive interdependence among problem dimensions.
This phenomenon - where complexity effects dominate (Darwinian) adaptation
efforts - is called complexity catastrophe. We present an algorithm -
incremental change taking turns (ICTT) - that finds distant configurations
having fitness superior to that reported in extant research, under high
complexity. Thus, complexity catastrophe is not inevitable: a series of
incremental changes can lead to excellent outcomes.
