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Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem
 Stanford University
, 1996
"... It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various humanwritten algorithms have appeared in the past two decades for the vexatiou ..."
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Cited by 48 (11 self)
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It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various humanwritten algorithms have appeared in the past two decades for the vexatious majority classification task for onedimensional twostate cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 GacsKurdyumovLevin (GKL) rule, all other known humanwritten rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space. 1.
HormoneInspired SelfOrganization and Distributed Control of Robotic Swarms
 Autonomous Robots
, 2004
"... Abstract. The control of robot swarming in a distributed manner is a difficult problem because global behaviors must emerge as a result of many local actions. This paper uses a bioinspired control method called the Digital Hormone Model (DHM) to control the tasking and executing of robot swarms bas ..."
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Cited by 35 (2 self)
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Abstract. The control of robot swarming in a distributed manner is a difficult problem because global behaviors must emerge as a result of many local actions. This paper uses a bioinspired control method called the Digital Hormone Model (DHM) to control the tasking and executing of robot swarms based on local communication, signal propagation, and stochastic reactions. The DHM model is probabilistic, dynamic, faulttolerant, computationally efficient, and can be easily tasked to change global behavior. Different from most existing distributed control and learning mechanisms, DHM considers the topological structure of the organization, supports dynamic reconfiguration and selforganization, and requires no globally unique identifiers for individual robots. The paper describes the DHM and presents the experimental results on simulating biological observations in the forming of feathers, and simulating wireless communicated swarm behavior at a large scale for attacking target, forming sensor networks, selfrepairing, and avoiding pitfalls in mission execution.
Cam8: a computer architecture based on cellular automata
 Proceedings of the Pattern Formation and LatticeGas Automata Conference. Fields Institute, American Mathematical Society
, 1993
"... The maximum computational density allowed by the laws of physics is available only in a format that mimics the basic spatial locality of physical law. Finegrained uniform computations with this kind of local interconnectivity (Cellular Automata) are particularly good candidates for efficient and ma ..."
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Cited by 34 (8 self)
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The maximum computational density allowed by the laws of physics is available only in a format that mimics the basic spatial locality of physical law. Finegrained uniform computations with this kind of local interconnectivity (Cellular Automata) are particularly good candidates for efficient and massive microphysical implementation. Conventional computers are ill suited to run CA models, and so discourage their development. Nevertheless, we have recently seen examples of interesting physical systems for which the best computational models are cellular automata running on ordinary computers. By simply rearranging the same quantity and quality of hardware as one might find in a lowend workstation today, we have made a lowcost CA multiprocessor that is about as good at large CA calculations as any existing supercomputer. This machine’s architecture is scalable in size (and performance) by orders of magnitude, since its 3D spatial mesh organization is indefinitely extendable. Using a relatively small degree of parallelism, such machines make possible a level ∗ This research was supported by the Advanced Research Projects Agency, grant N001489J1988. of performance at CA calculations much superior to that of existing supercomputers, but vastly inferior to what a fully parallel CA machine could achieve. By creating an intermediate hardware platform that makes a broad range of new CA algorithms practical for real applications, we hope to whet the appetite of researchers for the astronomical computing power that can be harnessed in microphysics in a CA format.
CRYSTALLINE COMPUTATION
 CHAPTER 18 OF FEYNMAN AND COMPUTATION (A. HEY, ED.)
, 1999
"... Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which o ..."
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Cited by 29 (8 self)
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Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which obey an invertible dynamics: from any state of the dynamical system you can infer the previous state. This kind of microscopic reversibility is an important property of all microscopic physical dynamics. Invertible lattice systems become even more physically realistic if we impose locality of interaction and exact conservation laws. In fact, some invertible and momentum conserving lattice dynamics—in which discrete particles hop between neighboring lattice sites at discrete times—accurately reproduce hydrodynamics in the macroscopic limit. These kinds of discrete systems not only provide an intriguing informationdynamics approach to modeling macroscopic physics, but they may also be supremely practical. Exactly the same properties that make these models physically realistic also make them efficiently realizable. Algorithms that incorporate constraints such as locality of interaction and invertibility can be run on microscopic physical hardware that shares these constraints. Such hardware can, in principle, achieve a higher density and rate of computation than any other kind of computer. Thus it is interesting to construct discrete lattice dynamics which are more physicslike both in order to capture more of the richness of physical dynamics in informational models, and in order to improve our ability to harness physics for computation. In this chapter, we discuss techniques for bringing discrete lattice dynamics closer to physics, and some of the interesting consequences of doing so.
Evolution of intricate longdistance communication signals in cellular automata using genetic programming
 In Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems
, 1996
"... A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326%. This level of accuracy exceeds that of the GacsKurdyumovLevin (GKL) rule, all other known humanwritten ..."
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Cited by 23 (0 self)
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A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326%. This level of accuracy exceeds that of the GacsKurdyumovLevin (GKL) rule, all other known humanwritten rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it utilizes a finegrained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space. 1.
Information characteristics and the structure of landscapes
 Evolutionary Computation
, 2000
"... Various techniques for statistical analysis of the structure of tness landscapes have been proposed. An important feature of these techniques is that they study the ruggedness of landscapes by measuring their correlation characteristics. This paper proposes a new information analysis of tness lands ..."
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Cited by 21 (2 self)
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Various techniques for statistical analysis of the structure of tness landscapes have been proposed. An important feature of these techniques is that they study the ruggedness of landscapes by measuring their correlation characteristics. This paper proposes a new information analysis of tness landscapes. The underlying idea is to consider a tness landscape as an ensemble of objects that are related to the tness of neighboring points. Three information characteristics of the ensemble are dened and studied. They are termed: information content, partial information content, and information stability. The information characteristics of a range of landscapes with known correlation features are analyzed in an attempt to reveal the advantages of the information analysis. We show that the proposed analysis is an appropriate tool for investigating the structure of tness landscapes.
A Boolean delay equation model of colliding cascades. Part II: Prediction of critical transitions
, 2003
"... We consider a prominent feature of hierarchical nonlinear (‘‘complex’’) systems: persistent recurrence of abrupt overall changes, called here ‘‘critical transitions.’’ Motivated by the earthquake prediction problem, we formulate a model that uses heuristic constraints taken from the dynamics of seis ..."
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Cited by 17 (7 self)
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We consider a prominent feature of hierarchical nonlinear (‘‘complex’’) systems: persistent recurrence of abrupt overall changes, called here ‘‘critical transitions.’’ Motivated by the earthquake prediction problem, we formulate a model that uses heuristic constraints taken from the dynamics of seismicity. Our conclusions, though, may apply to hierarchical systems that arise in other areas.We use the Boolean delay equation (BDE) framework to model the dynamics of colliding cascades, in which a direct cascade of loading interacts with an inverse cascade of failures. The elementary interactions of elements in the system are replaced by their integral effect, represented by the delayed switching of an element’s state.The present paper is the first of two on the BDE approach to modeling seismicity. Its major results are the following: (i) A model that implements the approach. (ii) Simulating three basic types of seismic regime. (iii) A study of regime switching in a parameter space of the loading and healing rates. The second paper focuses on the earthquake prediction problem.
Cryptography with Dynamical Systems
 In: Cellular Automata and Cooperative Phenomena, Eds: E. Goles and N. Boccara
, 1993
"... Dynamical systems are often described as "unpredictable" or "complex " as aspects of their behavior may bear a cryptic relationship with the simple evolution laws which define them. Some theorists work to quantify this complexity in various ways. Others try to turn the cryptic na ..."
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Cited by 14 (1 self)
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Dynamical systems are often described as "unpredictable" or "complex " as aspects of their behavior may bear a cryptic relationship with the simple evolution laws which define them. Some theorists work to quantify this complexity in various ways. Others try to turn the cryptic nature of dynamical systems to a practical end: encryption of messages to preserve their secrecy. Here some previous efforts to engineer cryptosystems based on dynamical systems are reviewed, leading up to a detailed proposal for a cellular automaton cryptosystem. Cryptosystems constructed from cellular automaton primitives can be implemented in simply constructed massively parallel hardware. They can be counted on to deliver high encryption/decryption rates at low cost. In addition to these practical features, cellular automaton cryptosystems may help illuminate some foundational issues in both dynamical systems theory and cryptology, since each of these disciplines rests heavily on the meanings given to the int...
Discovery of rewrite rules in Lindenmayer systems and state transition rules in cellular automata via genetic programming
, 1993
"... Abstract: It is difficult to write programs for both Lindenmayer systems and cellular automata. This paper demonstrates the possibility of discovering the rewrite rule for Lindenmayer systems and the state transition rules for cellular automata by means of genetic programming. Genetic programming is ..."
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Cited by 13 (5 self)
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Abstract: It is difficult to write programs for both Lindenmayer systems and cellular automata. This paper demonstrates the possibility of discovering the rewrite rule for Lindenmayer systems and the state transition rules for cellular automata by means of genetic programming. Genetic programming is an extension of the genetic algorithm in which computer programs are genetically bred to solve problems. We demonstrate the use of genetic programming to discover the rewrite rules for a Lindenmayer system for the quadratic Koch island using a pattern matching measure as the driving force for the evolutionary process. We also demonstrate the use of genetic programming to discover the state transition rules for a onedimensional and twodimensional cellular automata using entropy as the driving force for the evolutionary process. 1