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

TL;DR

This paper introduces ICTT, an algorithm that effectively navigates high-complexity NK landscapes, overcoming the complexity catastrophe by using incremental changes to find superior configurations.

Contribution

The paper presents ICTT, a novel algorithm that achieves beneficial adaptation in highly complex NK landscapes, challenging the notion that complexity catastrophe is unavoidable.

Findings

ICTT finds distant, high-fitness configurations in complex landscapes.

Incremental changes can prevent the effects of complexity catastrophe.

ICTT outperforms existing methods in high-complexity scenarios.

Abstract

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.