Paper Info
Title
The Evolution of Complexity in Self-Maintaining Cellular Information Processing Networks
Abstract
We examine the role of self-maintenance (collective autocatalysis) in the evolution of computational biochemical networks. In primitive proto-cells (lacking separate genetic machinery) self-maintenance is a necessary condition for the direct reproduction and inheritance of what we here term Cellular Information Processing Networks (CIPNs). Indeed, partially reproduced or defective CIPNs may generally lead to malfunctioning or premature death of affected cells. We explore the interaction of this self-maintenance property with the evolution and adaptation of CIPNs capable of distinct information processing abilities. We present an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is an agent-based multi-level selectional Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism. Using this system, we successfully evolve an artificial CIPN to improve performance on a simple pre-specified information processing task whilst subject to the constraint of continuous self-maintenance. We also describe the evolution of self-maintaining, cross-talking and multi-tasking, CIPNs exhibiting a higher level of topological and functional complexity. This proof of concept aims at contributing to the understanding of the open-ended evolutionary growth of complexity in artificial systems.
Year
DOI
Venue
2011
10.1142/S0219525911002913
ADVANCES IN COMPLEX SYSTEMS
Keywords
Field
DocType
Self-maintaining chemistry,collective autocatalysis,evolutionary growth of complexity,agent-based artificial chemistry
Artificial life,Information processing,Artificial chemistry,Computer science,Proof of concept,Artificial intelligence,Rewriting,Artificial systems,Classifier (linguistics),Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
14
1
0219-5259
Citations 
PageRank 
References 
1
0.63
1
Authors
2
Name
Order
Citations
PageRank
James Decraene15010.17
Barry McMullin210421.39