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The Advantages of Evolutionary Computation
, 1997
"... Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific ..."
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Cited by 536 (6 self)
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and optimization mechanism. Evolved biota demonstrate optimized complex behavior at every level: the cell, the organ, the individual, and the population. The problems that biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivities. These are also characteristics
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 352 (12 self)
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. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a
From Hopfield networks to Boltzmann machines
, 1997
"... X m6=n x (m) i x (m) j x (n) j (17.3) = (I \Gamma 1)x (n) i + X j 6=i X m6=n x (m) i x (m) j x (n) j : (17.4) The first term is (I \Gamma 1) times the desired state x (n) i . If this were the only term, it would keep the neuron firmly clamped in the desired state. The sec ..."
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X m6=n x (m) i x (m) j x (n) j (17.3) = (I \Gamma 1)x (n) i + X j 6=i X m6=n x (m) i x (m) j x (n) j : (17.4) The first term is (I \Gamma 1) times the desired state x (n) i . If this were the only term, it would keep the neuron firmly clamped in the desired state. The second term is a sum of (I \Gamma 1)(N \Gamma 1) random quantities x (m) i x<
3D Sound for Virtual Reality and Multimedia
, 2000
"... This paper gives HRTF magnitude data in numerical form for 43 frequencies between 0.212 kHz, the average of 12 studies representing 100 different subjects. However, no phase data is included in the tables; group delay simulation would need to be included in order to account for ITD. In 3D sound ..."
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Cited by 282 (5 self)
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This paper gives HRTF magnitude data in numerical form for 43 frequencies between 0.212 kHz, the average of 12 studies representing 100 different subjects. However, no phase data is included in the tables; group delay simulation would need to be included in order to account for ITD. In 3D sound applications intended for many users, we want might want to use HRTFs that represent the common features of a number of individuals. But another approach might be to use the features of a person who has desirable HRTFs, based on some criteria. (One can sense a future 3D sound system where the pinnae of various famous musicians are simulated.) A set of HRTFs from a good localizer (discussed in Chapter 2) could be used if the criterion were localization performance. If the localization ability of the person is relatively accurate or more accurate than average, it might be reasonable to use these HRTF measurements for other individuals. The Convolvotron 3D audio system (Wenzel, Wightman, and Foster, 1988) has used such sets particularly because elevation accuracy is affected negatively when listening through a bad localizers ears (see Wenzel, et al., 1988). It is best when any single nonindividualized HRTF set is psychoacoustically validated using a 113 statistical sample of the intended user population, as shown in Chapter 2. Otherwise, the use of one HRTF set over another is a purely subjective judgment based on criteria other than localization performance. The technique used by Wightman and Kistler (1989a) exemplifies a laboratorybased HRTF measurement procedure where accuracy and replicability of results were deemed crucial. A comparison of their techniques with those described in Blauert (1983), Shaw (1974), Mehrgardt and Mellert (1977), Middlebrooks, Makous, and Gree...
Gradient calculation for dynamic recurrent neural networks: a survey
 IEEE Transactions on Neural Networks
, 1995
"... Abstract  We survey learning algorithms for recurrent neural networks with hidden units, and put the various techniques into a common framework. We discuss xedpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non xedpoint algorithms, namely backp ..."
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Cited by 182 (3 self)
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Abstract  We survey learning algorithms for recurrent neural networks with hidden units, and put the various techniques into a common framework. We discuss xedpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non xedpoint algorithms, namely
Complexity Issues in Discrete Hopfield Networks
, 1994
"... We survey some aspects of the computational complexity theory of discretetime and discretestate Hopfield networks. The emphasis is on topics that are not adequately covered by the existing survey literature, most significantly: 1. the known upper and lower bounds for the convergence times of Hopfi ..."
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Cited by 19 (4 self)
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We survey some aspects of the computational complexity theory of discretetime and discretestate Hopfield networks. The emphasis is on topics that are not adequately covered by the existing survey literature, most significantly: 1. the known upper and lower bounds for the convergence times
Abstract A Hopfield neural network based task mapping method
"... With a prior knowledge of a program, static mapping aims to identify an optimal clustering strategy that can produce the best performance. In this paper we present a static method that uses Hopfield neural network to cluster the tasks of a parallel program for a given system. This method takes into ..."
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With a prior knowledge of a program, static mapping aims to identify an optimal clustering strategy that can produce the best performance. In this paper we present a static method that uses Hopfield neural network to cluster the tasks of a parallel program for a given system. This method takes
Results 1  10
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