[Tccc] CFP: Special Track on Network-based Computation (BIONETICS 2010)

Special Track on Network-based Computation
hosted at The 5th International ICST Conference on Bio-Inspired Models
of Network, Information, and Computing Systems (BIONETICS 2010)
Boston, USA
December 1 - 3, 2010
http://www.bionetics.org/
CALL FOR PAPERS
For a long time, network and computation have been in a close
relationship with each other in several disciplines of information
sciences. Back in the 1970s to 1980s, the data-flow computer [1] was
studied in many institutes in the world in the hope that parallel
algorithms represented by data-flow networks might remedy the
'bottle-neck' problem which a von Neumann computer suffers from. In
the 1980s, a new research area of so-called neural computers (NCs) [2]
had emerged. Researchers on the NC implemented artificial neural
networks to represent connectivity between artificial neurons. Such
models as a layered-network, Hopfield-network, etc. were proposed and
their computing and learning capabilities have been examined. More
recently, Genetic Programming (GP) [3] used networks to represent
algorithms. The original model of GP used only a program graph with
tree topology, but such research as PADO [4], Cartesian GP [5], and
GNP [6] adopted networks with free topology and extended the GP's
domain. Also, in the 1990s, active networking paradigm [7] was
proposed by networking researchers. The active networking enables
program encapsulation in every packet and its execution at routers.
In 2007, a model of artificial chemistry named Modified Network
Artificial Chemistry (MNAC) [8] was proposed. The MNAC is also
referred to as 'program-flow computing' because in the MNAC, molecular
agents with functional programs are not attached to nodes (CPUs) but
move from node to node, bringing a variety of different functions to
CPUs.
The special track, Network-based Computation, is dedicated to present
a forum for researchers interested in the extension of the
above-mentioned works which use networks for computation. We seek
highly original and unpublished papers that extend the boundary of the
former studies and point to new research directions. The interested
topics include but are not limited to:
• New models for network computation
• Algorithmically transitive network
• Von Neumann programs and network computation
• Parallel computation with networks
• Implementation scheme of network computation
• Active networking
• Network-based optimization
• Network computation and P2P
• Brain-like systems
Paper Submission Guidelines:
Paper submission instructions will be announced at
http://www.bionetics.org/. Submitted papers will be reviewed by the
TPC members of BIONETICS 2010. All accepted papers will be published
by Springer.
A selected number of best papers will be considered for publication in
leading journals such as ACM Transactions on Autonomous and Adaptive
Systems (TAAS; http://taas.acm.org/) and Int'l Journal of Autonomous
and Adaptive Communications Systems (IJAACS;
http://www.inderscience.com/browse/index.php?journalCODE=ijaacs).
Important Dates:
Paper submission due: July 16
Notification due: September 12
Camera ready due: October 10
Conference date: December 1 to 3

Track Chairs
Hideaki Suzuki, NICT, Japan
Hiroyuki Ohsaki, Osaka University, Japan
References
[1] Sharp, J.A. (ed.): Data flow computing: Theory and practice. Ablex
Publishing Corp.: Norwood, NJ (1992)
[2] Haykin, S.: Neural networks and learning machines. Prentice-Hall,
Inc. (2009)
[3] Koza, J.R.: Genetic Programming: on the Programming of Computers
by Means of Natural Selection. MIT Press, Boston (1992); Koza, J.R.:
Genetic Programming II: Automatic Discovery of Reusable Programs. MIT
Press, Boston (1994)
[4] Teller, A., Veloso, M.: PADO: Learning tree-structured algorithm
for orchestration into an object recognition system. Carnegie Mellon
University Technical Report, CMU-CS-95-101 (1995)
[5] Miller, J.F., Smith, S.L.: Redundancy and computational efficiency
in cartesian genetic programming. IEEE Transactions on Evolutionary
Computation, 10(2) (2006) 167-174
[6] Mabu, S., Hirasawa, K., Hu, J.: A graph-based evolutionary
algorithm: genetic network programming (GNP) and its extension using
reinforcement learning. Evolutionary Computation 15(3) (2007) 369-398
[7] Tennenhouse, D.L., Wetherall, D.J.: Towards an active network
architecture. ACM Computer Communication Review 26(2) (1996) 5-18
[8] Suzuki, H.: A network cell with molecular agents that divides from
centrosome signals. BioSystems 94 (2008) 118-125

--
Pruet Boonma
pruet@eng.cmu.ac.th
Department of Computer Engineering
Chiang Mai University

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