Not found

Plase contact us for more information at support@eai.eu

The page you requested cannot be found. If you entered the address manually, please check if it is correct. If the address is correct we apologise for the inconvenience and assure you that we are working to fix the problem.

Call for papers

NBC 2010

Special Track on Network-based Computation

http://www.bionetics.org/sp/nbc.shtml

Call for papers (pdf)

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 We seek highly original and unpublished papers that extend the boundary of the former studies and point to new research directions.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:

Paper Submission:

Authors are invited to submit papers in the following categories:

Papers must follow the Springer LNICST format. Please visit http://www.bionetics.org/ submission.shtml for detailed submission instructions.

Important Dates:

Publication:

All accepted paper will be published by Springer. A selected number of best papers will be considered for publication in leading journals such as:

Track Chairs:

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
Not found

Not found

Plase contact us for more information at support@eai.eu

The page you requested cannot be found. If you entered the address manually, please check if it is correct. If the address is correct we apologise for the inconvenience and assure you that we are working to fix the problem.