Overcoming Complexity Catastrophe: An Algorithm for Beneficial Far-Reaching Adaptation under High Complexity

05/10/2021
by   Sasanka Sekhar Chanda, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro