2021/05/10 by Sasanka Sekhar Chanda, Chanda, Sasanka Sekhar, Sai Yayavaram +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Evolution and Genetic Dynamics #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #cs.NE #nlin.AO
paper · pdf · doi:10.48550/arxiv.2105.04311
10 pages, 5 Figures
arxiv created 2021/05/10 · openalex publication_date 2021/05/10 · arxiv updated 2021/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.