Deducing local rules for solving global tasks with random Boolean networks

@article{Mesot2005DeducingLR,
  title={Deducing local rules for solving global tasks with random Boolean networks},
  author={Bertrand Mesot and Christof Teuscher},
  journal={Physica D: Nonlinear Phenomena},
  year={2005},
  volume={211},
  pages={88-106}
}
  • B. Mesot, C. Teuscher
  • Published 2005
  • Mathematics, Physics, Computer Science
  • Physica D: Nonlinear Phenomena
Abstract It has been shown that uniform as well as non-uniform cellular automata (CA) can be evolved to perform certain computational tasks. Random Boolean networks are a generalization of two-state cellular automata, where the interconnection topology and the cell’s rules are specified at random. Here we present a novel analytical approach to find the local rules of random Boolean networks (RBNs) to solve the global density classification and the synchronization task from any initial… Expand
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References

SHOWING 1-10 OF 65 REFERENCES
Co-evolving architectures for cellular machines
TLDR
This paper generalizes on a second aspect of CAs, namely their standard, homogeneous connectivity, and studies non-standard architectures, where each cell has a small, identical number of connections, yet not necessarily from its most immediate neighboring cells. Expand
Co-evolving non-uniform cellular automata to perform computations
A major impediment of cellular automata (CA) stems from the difficulty of utilizing their complex behavior to perform useful computations. Recent studies by Packard and Mitchell et al. have shownExpand
Quasi-Uniform Computation-Universal Cellular Automata
TLDR
The computation-universal system presented here is simpler than previous ones, and is embedded in the minimal possible two-dimensional cellular space, namely 2-state, 5-neighbor (which is insufficient for universal computation in the uniform model). Expand
Emergent properties in random complex automata
Abstract Studies of large, randomly assembled binary (Boolean) node automata have demonstrated that such systems can spontaneously exhibit enormously ordered dynamical behavior. An important approachExpand
Evolving cellular automata to perform computations: mechanisms and impediments
Abstract We present results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CAs) to perform a particular computational task - one-dimensional densityExpand
Random Boolean network model exhibiting deterministic chaos.
  • M. T. Matache, J. Heidel
  • Mathematics, Medicine
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
This paper considers a simple Boolean network with N nodes, each node's state at time t being determined by a certain number of parent nodes, which may vary from one node to another, and provides a generalization of the formula for the probability of finding a node in state 1 at a time t. Expand
Asynchronous, irregular automata nets: the path not taken.
TLDR
A primary objective is to demonstrate that irregular asynchronous automata nets, as opposed to cellular automata, are a realistic approach to modeling biological information processing. Expand
Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work
TLDR
The work described here is the first step in employing GAs to engineer useful emergent computation in decentralized multi-processor systems and is also a step in understanding how an evolutionary process can produce complex systems with sophisticated collective computational abilities. Expand
Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations
TLDR
An experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata to perform a particular computational task, demonstrates how symmetry breaking can impede the evolution toward higher computational capability. Expand
Computation in Artificially Evolved, Non-Uniform Cellular Automata
TLDR
The algorithm is presented and it is demonstrated that high-performance systems can be evolved to perform two non-trivial computational tasks, density and random number generation. Expand
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