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A molecular approach to complex adaptive systems

Decraene, James and Mitchell, George G. and McMullin, Barry (2007) A molecular approach to complex adaptive systems. In: CS2007 - IEEE SMC UK and RI 6th Conference on Cybernetic Systems, 6-7 Sept 2007 , Dublin, Ireland.

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Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Holland’s broadcast language. In the MCS.b, no explicit fitness function or rule discovery mechanism is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS: Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this paper we present a series of experiments focusing on the self-replication ability of these CAS. Results indicate counter intuitive outcomes as opposed to those inferred from the literature. This work highlights the current deficit of a theoretical framework for the study of Artificial Chemistries.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:ISSN 1744-9170 (print), ISSN 1744-9189 (CD-ROM).Pages 140-145
Subjects:Computer Science > Artificial intelligence
Engineering > Artificial life
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Published in:Proceedings of the IEEE Systems, Man and Cybernetics Society, CS2007. . IEEE Systems, Man and Cybernetics Society.
Publisher:IEEE Systems, Man and Cybernetics Society
Official URL:
Copyright Information:©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:4594
Deposited On:08 Jun 2009 13:59 by James Decraene. Last Modified 08 Jun 2009 14:00

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