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

Autor:	Chanda, Sasanka Sekhar, Yayavaram, Sai
Rok vydání:	2021
Předmět:	Computer Science - Neural and Evolutionary Computing Nonlinear Sciences - Adaptation and Self-Organizing Systems
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 5 Figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.04311 Zobrazit plný text záznamu View this record from Arxiv