title: Complexity measurement based on information theory and kolmogorov complexity.
abstract: In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.
authors.forename: Leong Ting
authors.initials: LT
authors.lastname: Lui
authors.affiliation: University of Nottingham.
authors.forename: Germán
authors.initials: G
authors.lastname: Terrazas
authors.affiliation: University of Sheffield.
authors.forename: Hector
authors.initials: H
authors.lastname: Zenil
authors.affiliation: University of Nottingham.
authors.forename: Cameron
authors.initials: C
authors.lastname: Alexander
authors.affiliation: Newcastle University.
authors.forename: Natalio
authors.initials: N
authors.lastname: Krasnogor
keywords: Complex systems; cellular automata; measures of complexity; molecular computation; open-ended evolution