Recently we have discovered an error in the implementation of the mutation operator in our earlier work on robust evolutionary algorithm design for socio-economic simulation (Alkemade et al. 2006, 2007). The original ...
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Recently we have discovered an error in the implementation of the mutation operator in our earlier work on robust evolutionary algorithm design for socio-economic simulation (Alkemade et al. 2006, 2007). The original paper compared two commonly used approaches to socio-economic simulation. In the first approach parameter settings for the evolutionary algorithm are directly derived from the underlying economic model while in the second approach to social learning parameter settings are chosen so as to optimise evolutionary algorithm performance. Main conclusions of the original paper are that the first approach may hinder the performance of the evolutionary algorithm and thereby hinder agent learning, that is, that social learning evolutionary algorithms are able to overcome the so-called spite-effect and obtain high profit outcomes. These main conclusions are still confirmed when the error in the mutation operator is corrected. However, the convergence behaviour of some of the individual runs differs significantly from the (incorrect) results presented in the earlier papers. More specifically, in the corrected experiments we do not observe the same type of premature convergence in approach I. In this paper we present the corrected results.
This paper considers a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program. We assume that each agent has limited information about the problem data and...
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ISBN:
(纸本)9781467360890
This paper considers a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program. We assume that each agent has limited information about the problem data and communicates with other agents at discrete times of its choice. Our main contribution is the development of a distributed continuous-time dynamics and a set of state-based rules, termed triggers, that an individual agent can use to determine when to broadcast its state to neighboring agents to ensure convergence. Our technical approach to the algorithm design and analysis overcomes a number of challenges, including establishing convergence in the absence of a common smooth Lyapunov function, ensuring that the triggers are detectable by agents using only local information, and accounting for the asynchronism in the state broadcasts of the agents. Simulations illustrate our results.
Background,Motivation and Objective For large amounts of data are transported to the ground in the process of logging and limitations of cable bandwidth,so ultrasonic log data need to be compressed in the downhole whe...
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Background,Motivation and Objective For large amounts of data are transported to the ground in the process of logging and limitations of cable bandwidth,so ultrasonic log data need to be compressed in the downhole when *** acoustic data compression algorithm is
After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the ...
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ISBN:
(纸本)9783037855744
After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the classification boundaries are not clear, convergence low efficiency and accuracy, there may even be the state does not converge, using rough set theory, the right value to modify the function to be modified, and joined the contradictions sample test module, after the use of EEG to verify reached the deletion of number of features and the purpose to improve the classification accuracy.
Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the netw...
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Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the network lifetime, most existing research focuses on constructing a load-balanced routing tree rooted at the base station for data gathering. However, this may result in a long routing path from some sensors to the base station. Motivated by the need of some mission-critical applications that require all sensed data to be received by the base station with minimal delay, this paper aims to construct a routing tree such that the network lifetime is maximized while keeping the routing path from each sensor to the base station minimized. This paper shows that finding such a tree is NP-hard. Thus a novel heuristic called top-down algorithm is presented, which constructs the routing tree layer by layer such that each layer is optimally extended, using a network flow model. A distributed refinement algorithm is then devised that dramatically improves on the load balance for the routing tree produced by the top-down algorithm. Finally, extensive simulations are conducted. The experimental results show that the top-down algorithm with balance-refinement delivers a shortest routing tree whose network lifetime achieves around 85% of the optimum. (C) 2012 Elsevier B.V. All rights reserved.
A key challenge in defense and security systems is to implement functionality within a power budget. We show how data bandwidth redundancy and the need to change performance is exploited to achieve power efficient, fi...
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A key challenge in defense and security systems is to implement functionality within a power budget. We show how data bandwidth redundancy and the need to change performance is exploited to achieve power efficient, field programmable gate array realizations with improved sampling rates. A unified methodology is given for the implementation of a key function, the fast Fourier transform, for a Radar-based digital receiver. Locality of data, temporal and spatial resource usage are examined from first principles, leading to an algorithmic approach that demonstrates substantial industrial benefits in terms of power, performance and resource usage. A power saving of 18% is achieved over a Cooley Tukey design with a 100% speed improvement;the work is extended to other cyclical fast algorithms.
With the proliferation of multiprocessor computers, data structures capable of supporting several processes are a growing need. Concurrent data structures seek to provide similar performance to sequential data structu...
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With the proliferation of multiprocessor computers, data structures capable of supporting several processes are a growing need. Concurrent data structures seek to provide similar performance to sequential data structures while being accessible concurrently by several processes and providing synchronization mechanisms transparently to those processes. Red-black trees are an important data structure used in many systems. Unfortunately, it is difficult to implement an efficient concurrent red-black tree for shared memory processes;so most research efforts have been directed towards other dictionary data structures, mainly skip-lists and AVL trees. In this paper we present a new type of concurrent red-black tree that uses optimistic concurrency techniques and new balancing operations to scale well and support contention. Our tree performs favorably compared to other similar dictionaries;in particular, in high contention scenarios it performs up to 14% better than the best-known concurrent dictionary solutions. (C) 2013 Elsevier Inc. All rights reserved.
We study the problem of searching for a vertex with a desired property in the arrangement of a set of lines in the plane. We show that this problem can be solved efficiently by modifying (and simplifying) two slope se...
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We study the problem of searching for a vertex with a desired property in the arrangement of a set of lines in the plane. We show that this problem can be solved efficiently by modifying (and simplifying) two slope selection algorithms without using parametric search. We apply this result to a points approximation problem and obtain an optimal solution for it without using parametric search. Since this line arrangement searching problem is quite natural, our result may find other applications as well. (C) 2013 Elsevier B.V. All rights reserved.
Given a set of n points in the plane, each point having a positive weight, and an integer k > 0, we present an optimal O(n log n)-time deterministic algorithm to compute a step function with k steps that minimizes ...
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Given a set of n points in the plane, each point having a positive weight, and an integer k > 0, we present an optimal O(n log n)-time deterministic algorithm to compute a step function with k steps that minimizes the maximum weighted vertical distance to the input points. It matches the expected time bound of the best known randomized algorithm for this problem. Our approach relies on Cole's improved parametric searching technique. As a direct application, our result yields the first O(n log n)-time algorithm for computing a k-center of a set of n weighted points on the real line. (C) 2012 Elsevier B.V. All rights reserved.
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