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99
A genetic algorithm discovers particlebased computation in cellular automata
 PARALLEL PROBLEM SOLVING FROM NATURE
, 1994
"... How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globallycoordinated information proc ..."
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Cited by 66 (15 self)
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How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globallycoordinated information processing. On most runs a class of relatively unsophisticated strategies was evolved, but on a subset of runs a number of quite sophisticated strategies was discovered. We analyze the emergent logic underlying these strategies in terms of information processing performed by “particles” in spacetime, and we describe in detail the generational progression of the GA evolution of these strategies. Our analysis is a preliminary step in understanding the general mechanisms by which sophisticated emergent computational capabilities can be automatically produced in decentralized multiprocessor systems.
Statistical dynamics of the Royal Road genetic algorithm
 Theoretical Computer Science
, 1999
"... Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutationonly genetic algorithm (GA) is introduced that iden ..."
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Cited by 66 (5 self)
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Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutationonly genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability in evolutionary dynamics. The GA’s population dynamics is described in terms of flows in the space of fitness distributions. The trajectories through fitness distribution space are derived in closed form in the limit of infinite populations. We then show how finite populations induce metastability, even in regions where fitness does not exhibit a local optimum. In particular, the model predicts the occurrence of “fitness epochs”—periods of stasis in population fitness distributions—at finite population size and identifies the locations of these fitness epochs with the flow’s hyperbolic fixed points. This enables exact predictions of the metastable fitness distributions during the fitness epochs, as well as giving insight into the nature of the periods of stasis and the innovations between them. All these results are obtained as closedform expressions in terms of the GA’s parameters.
Is Anything Ever New? Considering Emergence
 IN
, 1994
"... This brief essay reviews an approach to defining and then detecting the emergence of complexity in nonlinear processes. It is, in fact, a synopsis of Reference [1] that leaves out the technical details in an attempt to clarify the motivations behind the approach. The central puzzle ..."
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Cited by 48 (6 self)
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This brief essay reviews an approach to defining and then detecting the emergence of complexity in nonlinear processes. It is, in fact, a synopsis of Reference [1] that leaves out the technical details in an attempt to clarify the motivations behind the approach. The central puzzle
Computational Mechanics of Cellular Automata: An Example
, 1995
"... We illustrate and extend the techniques of computational mechanics in explicating the structures that emerge in the spacetime behavior of elementary onedimensional cellular automaton rule 54. The CA's dominant regular domain is identified and a domain filter is constructed to locate and class ..."
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Cited by 47 (4 self)
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We illustrate and extend the techniques of computational mechanics in explicating the structures that emerge in the spacetime behavior of elementary onedimensional cellular automaton rule 54. The CA's dominant regular domain is identified and a domain filter is constructed to locate and classify defects in the domain. The primary particles are identified and a range of interparticle interactions is studied. The deterministic equation of motion of the filtered spacetime behavior is derived. Filters of increasing sophistication are constructed for the efficient gathering of particle statistics and for the identification of higherlevel defects, particle interactions, and secondary domains. We define the emergence time at which the spacetime behavior condenses into configurations consisting only of domains, particles, and particle interactions. Taken together, these techniques serve as the basis for the investigation of pattern evolution and selforganization in this representative sys...
Quantum automata and quantum grammars
 Theoretical Computer Science
"... Abstract. To study quantum computation, it might be helpful to generalize structures from language and automata theory to the quantum case. To that end, we propose quantum versions of finitestate and pushdown automata, and regular and contextfree grammars. We find analogs of several classical the ..."
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Cited by 37 (2 self)
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Abstract. To study quantum computation, it might be helpful to generalize structures from language and automata theory to the quantum case. To that end, we propose quantum versions of finitestate and pushdown automata, and regular and contextfree grammars. We find analogs of several classical theorems, including pumping lemmas, closure properties, rational and algebraic generating functions, and Greibach normal form. We also show that there are quantum contextfree languages that are not contextfree. 1
Emergence of Netgrammar in Communicating Agents
 BioSystems
, 1996
"... Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. Agents communicate with each other by deriving and accepting words in terms of their own grammar. T ..."
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Cited by 36 (4 self)
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Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. Agents communicate with each other by deriving and accepting words in terms of their own grammar. They are ranked according to their communicative effectiveness: an agent which can derive less frequent and less acceptable words and accept words in less computational time will have higher scores. They can evolve by mutational processes, which change rewriting rules in their symbolic grammars. Complexity and diversity of words increase in the course of time. The emergence of modules and loop structure enhances the evolution. On the other hand, ensemble structure lead to a netgrammar, restricting individual grammars and their evolution. Key words: Netgrammar; Algorithmic evolution; Moduletype evolution; Evolution of language; Symbolic grammar systems 1 Introduction Linguistic expressions...
Computation in cellular automata: A selected review
 Nonstandard Computation
, 1996
"... Cellular automata (CAs) are decentralized spatially extended systems consisting of large numbers of simple identical components with local connectivity. Such systems have the potential to perform complex computations with a high degree of efficiency and robustness, as well as to model the behavior o ..."
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Cited by 36 (2 self)
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Cellular automata (CAs) are decentralized spatially extended systems consisting of large numbers of simple identical components with local connectivity. Such systems have the potential to perform complex computations with a high degree of efficiency and robustness, as well as to model the behavior of complex systems in nature. For these reasons CAs and related architectures have
Syntactic Measures of Complexity
, 1999
"... page 14 Declaration  page 15 Notes of copyright and the ownership of intellectual property rights  page 15 The Author  page 16 Acknowledgements  page 16 1  Introduction  page 17 1.1  Background  page 17 1.2  The Style of Approach  page 18 1.3  Motivation  page 19 1.4  Style of ..."
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Cited by 33 (2 self)
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page 14 Declaration  page 15 Notes of copyright and the ownership of intellectual property rights  page 15 The Author  page 16 Acknowledgements  page 16 1  Introduction  page 17 1.1  Background  page 17 1.2  The Style of Approach  page 18 1.3  Motivation  page 19 1.4  Style of Presentation  page 20 1.5  Outline of the Thesis  page 21 2  Models and Modelling  page 23 2.1  Some Types of Models  page 25 2.2  Combinations of Models  page 28 2.3  Parts of the Modelling Apparatus  page 33 2.4  Models in Machine Learning  page 38 2.5  The Philosophical Background to the Rest of this Thesis  page 41 Syntactic Measures of Complexity  page 3  3  Problems and Properties  page 44 3.1  Examples of Common Usage  page 44 3.1.1  A case of nails  page 44 3.1.2  Writing a thesis  page 44 3.1.3  Mathematics  page 44 3.1.4  A gas  page 44 3.1.5  An ant hill  page 45 3.1.6  A car engine  page 45 3.1.7  A cell as part of an organism ...
Mechanisms of Emergent Computation in Cellular Automata
 Parallel Problem Solving from Nature Proceedings Vth Workshop PPSN V
, 1998
"... . We introduce a class of embeddedparticle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of ..."
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Cited by 28 (7 self)
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. We introduce a class of embeddedparticle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of the CAs they model. The results show, via a close quantitative agreement, that the embeddedparticle framework captures the main information processing mechanisms of the emergent computation that arise in these evolved CAs. 1 Introduction In previous work we have used genetic algorithms (GAs) to evolve cellular automata (CAs) to perform computational tasks that require global coordination. The evolving cellular automata framework has provided a direct approach to studying how evolution (natural or artificial) can create dynamical systems that perform emergent computation; that is, how it can find dynamical systems in which the interaction of simple components with local information storage...
Engineering Emergence
"... We explore various definitions and characteristics of emergence, how we might recognise and measure emergence, and how we might engineer emergent systems. We discuss the TUNA (“Theory Underpinning Nanotech Assemblers”) project, which is investigating emergent engineering in the context of molecular ..."
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Cited by 20 (8 self)
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We explore various definitions and characteristics of emergence, how we might recognise and measure emergence, and how we might engineer emergent systems. We discuss the TUNA (“Theory Underpinning Nanotech Assemblers”) project, which is investigating emergent engineering in the context of molecular nanotechnology, and use the TUNA case study to explore an architecture suitable for emergent complex systems. 1.