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Complexity and robustness
- Proceedings of the National Academy of Sciences 99(Suppl
, 2002
"... Highly Optimized Tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity. HOT is motivated primarily by systems from biology and engineering and emphasizes 1) highly structured, nongeneric, selfdissimilar internal configurations and 2) robust, yet ..."
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Cited by 156 (10 self)
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Highly Optimized Tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity. HOT is motivated primarily by systems from biology and engineering and emphasizes 1) highly structured, nongeneric, selfdissimilar internal configurations and 2) robust, yet fragile external behavior. HOT claims these are the most important features of complexity and are not accidents of evolution or artifices of engineering design, but are inevitably intertwined and mutually reinforcing. In the spirit of this collection, our paper contrasts HOT with alternative perspectives on complexity, drawing on both real world examples and also model systems, particularly those from Self-Organized Criticality (SOC).
TOPOLOGICAL ANALYSIS OF SPECIFIC SPATIAL COMPLEX NETWORKS
, 2009
"... Based on analyses of specific spatial networks, we compare the accuracy of three models in capturing topologies of two types of spatial networks: electronic circuits and brain networks. The models analyzed are an optimization model trading off multiple-objective constraints, an extended preferential ..."
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Cited by 5 (2 self)
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Based on analyses of specific spatial networks, we compare the accuracy of three models in capturing topologies of two types of spatial networks: electronic circuits and brain networks. The models analyzed are an optimization model trading off multiple-objective constraints, an extended preferential attachment model with spatial constraints, and the generalized random graph model. First, we find that the optimization model and the spatial preferential attachment model can generate similar topological structures under appropriate parameters. Second, our experiments surprisingly show that the simple generalized random graph model outperforms the two proposed models. Third, we find that a series of spatial networks under global optimization of wire length, including the electronic circuits, brain networks, neuronal networks and transportation networks, have high s-metric values close to those of the corresponding generalized random graph models. These s-metric observations explain why the generalized random graph model can match the electronic circuits and the brain networks well from a probabilistic viewpoint, and distinguish their structures from self-organized spatial networks, such as the Internet.
The Transfer Function of
- PZT Phase Modulators in Optical Fiber Sensors, In: Proceedings of National Science Council
, 1994
"... test data modelling by evolutionary radial basis ..."
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Discovering adaptive heuristics for ad-hoc sensor networks by mining evolved optimal configurations,” in Proc
- Oak Ridge National Laboratory, Los Alamos National Laboratory, Naval
, 1968
"... Abstract — Ad-hoc sensor networks comprising large numbers of randomly deployed wireless sensors have recently been an active focus of investigation. These networks require selforganized configuration after deployment, and ad-hoc heuristic methods for such configuration have been proposed with regar ..."
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Cited by 3 (2 self)
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Abstract — Ad-hoc sensor networks comprising large numbers of randomly deployed wireless sensors have recently been an active focus of investigation. These networks require selforganized configuration after deployment, and ad-hoc heuristic methods for such configuration have been proposed with regard to many aspects of the networks ’ performance. However, systematic approaches for such configuration remain elusive. In this paper, we present a preliminary attempt towards such a systematic approach using evolutionary algorithms and reverse engineering. In particular, we focus on the problem of obtaining heterogeneous networks that optimize global functional properties through local adaptive rules. Almost all work on adhoc sensor network has so far involved homogeneous networks where all nodes transmit with the same power level, creating a symmetric connectivity. It is possible to construct heterogeneous networks by allowing nodes to transmit at different power levels, and such networks are known to provide improvements in network lifetime, power efficiency, routing, etc. However, such networks are difficult to build mainly because the optimal power level for each node depends on the node location and spatial context, which are not known before deployment. A few heuristic schemes focused on improving power consumption have been proposed in the literature, but the issue has not been investigated sufficiently at a general level. In this paper, we present a new and improved heuristic developed using a reverse engineered approach. A genetic algorithm is used to generate a set of heterogeneous sensor networks that are characterized by low short paths and minimal congestion. Analysis of this optimal network set yields rules that form the basis for a local heuristic. We show that networks adapted using this heuristic produce significant improvement over the homogeneous case. More importantly, the results validate the utility of the proposed approach that can be used in other self-organizing systems. I.
Journal of Experimental Botany, Page 1 of 11
, 2008
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Engineering Adaptable Systems: State of The Art
, 2010
"... reviewed selected recent research in complex adaptive systems that could be used to increase speed, flexibility and adaptability. Based on our review and analysis, engineering adaptable systems was identified as a key element in providing capability on demand—the ability to adapt fielded systems to ..."
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reviewed selected recent research in complex adaptive systems that could be used to increase speed, flexibility and adaptability. Based on our review and analysis, engineering adaptable systems was identified as a key element in providing capability on demand—the ability to adapt fielded systems to unforeseen internal and external contingencies.
Bow-tie Decomposition in Directed Graphs
"... Abstract—The bow-tie structure is frequently cited in the literature of the World Wide Web and in many other areas, such as metabolic networks, but it has never been precisely defined, so that to some extent the concept being discussed remains vague. This paper first provides a formal definition of ..."
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Abstract—The bow-tie structure is frequently cited in the literature of the World Wide Web and in many other areas, such as metabolic networks, but it has never been precisely defined, so that to some extent the concept being discussed remains vague. This paper first provides a formal definition of a bow-tie structure relative to a given strongly connected components. That definition details distinctions which are not usually made, such as the difference between intendrils and outtendrils. Theorems and algorithms are then provided to justify and support the definition. Finally a bow-tie decomposition algorithm is developed and illustrated. The algorithms have also been implemented and tested on a university domain. be present in core metabolism networks [12]. Thus the bowtie decomposition has demonstrated its usefulness both in theoretical studies and eminently practical applications. While the bow-tie structure is frequently cited in the literature, it is generally described in words or by an illustration and has never been precisely defined. It has also never been noted that this structure is really relative to a given strongly connected component. This paper provides the needed formal definition of a bow-tie decomposition relative to a component and an algorithm for computing it.
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