Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/93823
Title: The evolution of complexity in self-maintaining cellular information processing networks
Authors: Decraene, James
McMullin, Barry
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2011
Source: Decraene, J., & Mcmullin, B. (2011). The Evolution of Complexity in Self-maintaining Cellular Information Processing Networks. Advances in Complex Systems, 14(1), 55-75.
Series/Report no.: Advances in complex systems
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.
URI: https://hdl.handle.net/10356/93823
http://hdl.handle.net/10220/6873
DOI: 10.1142/S0219525911002913
Rights: © 2011 World Scientific Publishing. This is the author created version of a work that has been peer reviewed and accepted for publication by Advances in Complex Systems, World Scientific Publishing. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: http://dx.doi.org/10.1142/S0219525911002913.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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