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17

Jan 5, 2013
01/13

Jan 5, 2013
by
Iztok Fister; Marjan Mernik; Janez Brest

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Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function,...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1301.0929

16
16

Jan 5, 2013
01/13

Jan 5, 2013
by
Iztok Fister; Marjan Mernik; Bogdan Filipič

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This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The performance of these algorithms were tested...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1301.0939

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5.0

Jan 2, 2014
01/14

Jan 2, 2014
by
Muhammad Saqib Sohail; Muhammad Omer Bin Saeed; Syed Zeeshan Rizvi; Mobien Shoaib; Asrar Ul Haq Sheikh

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Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.0546

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3.0

Jan 4, 2014
01/14

Jan 4, 2014
by
Videh Seksaria

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The swarm intelligence of animals is a natural paradigm to apply to optimization problems. Ant colony, bee colony, firefly and bat algorithms are amongst those that have been demonstrated to efficiently to optimize complex constraints. This paper proposes the new Sparkling Squid Algorithm (SSA) for multimodal optimization, inspired by the intelligent swarm behavior of its namesake. After an introduction, formulation and discussion of its implementation, it will be compared to other popular...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.0858

3
3.0

Jan 6, 2014
01/14

Jan 6, 2014
by
Yu Chen; Weicheng Xie; Xiufen Zou

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Although real-coded differential evolution (DE) algorithms can perform well on continuous optimization problems (CoOPs), it is still a challenging task to design an efficient binary-coded DE algorithm. Inspired by the learning mechanism of particle swarm optimization (PSO) algorithms, we propose a binary learning differential evolution (BLDE) algorithm that can efficiently locate the global optimal solutions by learning from the last population. Then, we theoretically prove the global...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.1124

2
2.0

Jan 8, 2014
01/14

Jan 8, 2014
by
Laurent Moalic; Alexandre Gondran

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Graph vertex coloring with a given number of colors is a well-known and much-studied NP-complete problem.The most effective methods to solve this problem are proved to be hybrid algorithms such as memetic algorithms or quantum annealing. Those hybrid algorithms use a powerful local search inside a population-based algorithm.This paper presents a new memetic algorithm based on one of the most effective algorithms: the Hybrid Evolutionary Algorithm HEA from Galinier and Hao (1999).The proposed...

Topics: Neural and Evolutionary Computing, Mathematics, Computing Research Repository, Artificial...

Source: http://arxiv.org/abs/1401.2184

2
2.0

Jan 9, 2014
01/14

Jan 9, 2014
by
Alireza Goudarzi; Peter Banda; Matthew R. Lakin; Christof Teuscher; Darko Stefanovic

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Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir's state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who study the task-solving performance and computational power of RC. In addition, in contrast to...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Learning

Source: http://arxiv.org/abs/1401.2224

2
2.0

Jan 9, 2014
01/14

Jan 9, 2014
by
Dogan Corus; Per Kristian Lehre; Frank Neumann; Mojgan Pourhassan

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Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. With this paper, we start the runtime analysis of evolutionary algorithms for bi-level optimisation problems. We examine two NP-hard problems, the generalised minimum spanning tree problem (GMST), and the generalised travelling salesman problem (GTSP) in the context of parameterised complexity. For the generalised minimum spanning tree problem, we analyse the two approaches...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.1905

5
5.0

Jan 10, 2014
01/14

Jan 10, 2014
by
Erich Schikuta; Erwin Mann

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We present the N2Sky system, which provides a framework for the exchange of neural network specific knowledge, as neural network paradigms and objects, by a virtual organization environment. It follows the sky computing paradigm delivering ample resources by the usage of federated Clouds. N2Sky is a novel Cloud-based neural network simulation environment, which follows a pure service oriented approach. The system implements a transparent environment aiming to enable both novice and experienced...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.2468

13
13

Jan 12, 2014
01/14

Jan 12, 2014
by
David White

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The purpose of this paper is to give an introduction to the field of Schema Theory written by a mathematician and for mathematicians. In particular, we endeavor to to highlight areas of the field which might be of interest to a mathematician, to point out some related open problems, and to suggest some large-scale projects. Schema theory seeks to give a theoretical justification for the efficacy of the field of genetic algorithms, so readers who have studied genetic algorithms stand to gain the...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.2651

4
4.0

Jan 19, 2014
01/14

Jan 19, 2014
by
Ronald Hochreiter; Christoph Waldhauser

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In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provide an argument to tighten the intersection between computer science and social science, especially political science, further....

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.4674

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10.0

Jan 19, 2014
01/14

Jan 19, 2014
by
Ronald Hochreiter

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Optimizing decision problems under uncertainty can be done using a variety of solution methods. Soft computing and heuristic approaches tend to be powerful for solving such problems. In this overview article, we survey Evolutionary Optimization techniques to solve Stochastic Programming problems - both for the single-stage and multi-stage case.

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.4696

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14

Jan 19, 2014
01/14

Jan 19, 2014
by
Ronald Hochreiter; Christoph Waldhauser

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The optimization of dynamic problems is both widespread and difficult. When conducting dynamic optimization, a balance between reinitialization and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimize a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.4714

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2.0

Jan 20, 2014
01/14

Jan 20, 2014
by
Ronald Hochreiter; Christoph Waldhauser

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Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize...

Topics: Physics, Neural and Evolutionary Computing, Computers and Society, Computing Research Repository,...

Source: http://arxiv.org/abs/1401.4857

4
4.0

Jan 20, 2014
01/14

Jan 20, 2014
by
Ronald Hochreiter; Christoph Waldhauser

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In land surveying, the generation of maps was greatly simplified with the introduction of orthophotos and at a later stage with airborne LiDAR laser scanning systems. While the original purpose of LiDAR systems was to determine the altitude of ground elevations, newer full wave systems provide additional information that can be used on classifying the type of ground cover and the generation of maps. The LiDAR resulting point clouds are huge, multidimensional data sets that need to be grouped in...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.4848

2
2.0

Jan 21, 2014
01/14

Jan 21, 2014
by
Michał Karpiński; Maciej Pacut

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The goal of this paper is to propose and test a new memetic algorithm for the capacitated vehicle routing problem in parallel computing environment. In this paper we consider simple variation of vehicle routing problem in which the only parameter is the capacity of the vehicle and each client only needs one package. We present simple reduction to prove the existence of polynomial-time algorithm for capacity 2. We analyze the efficiency of the algorithm using hierarchical Parallel Random Access...

Topics: Distributed, Parallel, and Cluster Computing, Neural and Evolutionary Computing, Computing Research...

Source: http://arxiv.org/abs/1401.5216

2
2.0

Jan 21, 2014
01/14

Jan 21, 2014
by
Shu Kong; Zhuolin Jiang; Qiang Yang

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We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question. In this paper, we present a very efficient mid-level feature learning approach (MidFea), which only involves simple operations such as $k$-means clustering, convolution, pooling, vector quantization and random projection. We explain why this simple method generates the desired features,...

Topics: Neural and Evolutionary Computing, Robotics, Computing Research Repository, Computer Vision and...

Source: http://arxiv.org/abs/1401.5535

5
5.0

Jan 23, 2014
01/14

Jan 23, 2014
by
Nan Wang; Jan Melchior; Laurenz Wiskott

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We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of Gaussians, which gives a much better insight into the model's capabilities and limitations. We show that GRBMs are capable of learning meaningful features both in a two-dimensional blind source separation task and in modeling natural images. Further, we show that...

Topics: Neural and Evolutionary Computing, Machine Learning, Computing Research Repository, Statistics,...

Source: http://arxiv.org/abs/1401.5900

4
4.0

Jan 24, 2014
01/14

Jan 24, 2014
by
Yang Yu; Hong Qian

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Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their general power from a statistical view of EAs. By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA)...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Learning

Source: http://arxiv.org/abs/1401.6333

3
3.0

Jan 28, 2014
01/14

Jan 28, 2014
by
Jeffrey Tsang

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Fingerprinting operators generate functional signatures of game players and are useful for their automated analysis independent of representation or encoding. The theory for a fingerprinting operator which returns the length-weighted probability of a given move pair occurring from playing the investigated agent against a general parametrized probabilistic finite-state transducer (PFT) is developed, applicable to arbitrary iterated games. Results for the distinguishing power of the 1-state...

Topics: Computer Science and Game Theory, Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1401.7406

3
3.0

Feb 3, 2014
02/14

Feb 3, 2014
by
Hooman Jarollahi; Naoya Onizawa; Takahiro Hanyu; Warren J. Gross

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Associative memories are structures that store data patterns and retrieve them given partial inputs. Sparse Clustered Networks (SCNs) are recently-introduced binary-weighted associative memories that significantly improve the storage and retrieval capabilities over the prior state-of-the art. However, deleting or updating the data patterns result in a significant increase in the data retrieval error probability. In this paper, we propose an algorithm to address this problem by incorporating...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1402.0808

16
16

Feb 4, 2014
02/14

Feb 4, 2014
by
Andrea Soltoggio

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Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes helps distinguish true cause-effect relationships from coincidental occurrences. In the model proposed here, a novel plasticity rule employs short and long-term changes to evaluate hypotheses on cause-effect relationships. Transient weights represent hypotheses that are consolidated in long-term memory only when they...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Quantitative Biology, Neurons and...

Source: http://arxiv.org/abs/1402.0710

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7.0

Feb 5, 2014
02/14

Feb 5, 2014
by
Carlos Pedro Gonçalves

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Quantum cybernetics and its connections to complex quantum systems science is addressed from the perspective of complex quantum computing systems. In this way, the notion of an autonomous quantum computing system is introduced in regards to quantum artificial intelligence, and applied to quantum artificial neural networks, considered as autonomous quantum computing systems, which leads to a quantum connectionist framework within quantum cybernetics for complex quantum computing systems. Several...

Topics: Quantum Physics, Neural and Evolutionary Computing, Computing Research Repository, Disordered...

Source: http://arxiv.org/abs/1402.1141

3
3.0

Feb 5, 2014
02/14

Feb 5, 2014
by
Haşim Sak; Andrew Senior; Françoise Beaufays

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Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep...

Topics: Computation and Language, Machine Learning, Learning, Neural and Evolutionary Computing, Computing...

Source: http://arxiv.org/abs/1402.1128

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2.0

Feb 6, 2014
02/14

Feb 6, 2014
by
Zhang-Hua Fu; Jin-Kao Hao

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Given an undirected graph with costs associated with each edge as well as each pair of edges, the quadratic minimum spanning tree problem (QMSTP) consists of determining a spanning tree of minimum total cost. This problem can be used to model many real-life network design applications, in which both routing and interference costs should be considered. For this problem, we propose a three-phase search approach named TPS, which integrates 1) a descent-based neighborhood search phase using two...

Topics: Neural and Evolutionary Computing, Data Structures and Algorithms, Computing Research Repository

Source: http://arxiv.org/abs/1402.1379

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12

Feb 8, 2014
02/14

Feb 8, 2014
by
Guido Montúfar; Razvan Pascanu; Kyunghyun Cho; Yoshua Bengio

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We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation...

Topics: Machine Learning, Neural and Evolutionary Computing, Computing Research Repository, Statistics,...

Source: http://arxiv.org/abs/1402.1869

2
2.0

Feb 9, 2014
02/14

Feb 9, 2014
by
Wen Wang; Zhen Cui; Hong Chang; Shiguang Shan; Xilin Chen

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The comparison of heterogeneous samples extensively exists in many applications, especially in the task of image classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers. In DCAN, each deep structure is developed via stacking multiple discriminative coupled auto-encoders, a denoising auto-encoder trained with...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Computer Vision and Pattern...

Source: http://arxiv.org/abs/1402.2031

2
2.0

Feb 11, 2014
02/14

Feb 11, 2014
by
Chris Watkins; Yvonne Buttkewitz

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We show that evolutionary computation can be implemented as standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms' can be constructed that are reversible Markov chains that satisfy detailed balance; it follows that the stationary distribution of populations is a Gibbs distribution in a simple factorised form. For some standard and popular nonparametric probability models, we exhibit Gibbs-sampling procedures that are plausible genetic algorithms. At...

Topics: Populations and Evolution, Neural and Evolutionary Computing, Quantitative Biology, Computing...

Source: http://arxiv.org/abs/1402.2704

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3.0

Feb 12, 2014
02/14

Feb 12, 2014
by
Gabriela Ochoa; Sébastien Verel; Fabio Daolio; Marco Tomassini

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This chapter overviews a recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is a graph having as vertices the local optima and as edges the possible weighted transitions between them. Two definitions of edges have been proposed: basin-transition and escape-edges, which capture relevant topological features of the underlying search spaces....

Topics: Neural and Evolutionary Computing, Computing Research Repository, Artificial Intelligence

Source: http://arxiv.org/abs/1402.2959

2
2.0

Feb 13, 2014
02/14

Feb 13, 2014
by
Kishore Konda; Roland Memisevic; David Krueger

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Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation. We then show that negative biases impede the learning of data distributions whose intrinsic dimensionality is high. We also propose a new activation function that decouples...

Topics: Neural and Evolutionary Computing, Statistics, Computing Research Repository, Computer Vision and...

Source: http://arxiv.org/abs/1402.3337

3
3.0

Feb 13, 2014
02/14

Feb 13, 2014
by
Guido Montufar; Nihat Ay; Keyan Ghazi-Zahedi

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Conditional restricted Boltzmann machines are undirected stochastic neural networks with a layer of input and output units connected bipartitely to a layer of hidden units. These networks define models of conditional probability distributions on the states of the output units given the states of the input units, parametrized by interaction weights and biases. We address the representational power of these models, proving results their ability to represent conditional Markov random fields and...

Topics: Neural and Evolutionary Computing, Machine Learning, Computing Research Repository, Statistics,...

Source: http://arxiv.org/abs/1402.3346

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8.0

Feb 14, 2014
02/14

Feb 14, 2014
by
Jan Koutník; Klaus Greff; Faustino Gomez; Jürgen Schmidhuber

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Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when the long-term memory is required. This paper introduces...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Learning

Source: http://arxiv.org/abs/1402.3511

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6.0

Feb 16, 2014
02/14

Feb 16, 2014
by
Wei Gao; Zhi-Hua Zhou

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Great successes of deep neural networks have been witnessed in various real applications. Many algorithmic and implementation techniques have been developed, however, theoretical understanding of many aspects of deep neural networks is far from clear. A particular interesting issue is the usefulness of dropout, which was motivated from the intuition of preventing complex co-adaptation of feature detectors. In this paper, we study the Rademacher complexity of different types of dropout, and our...

Topics: Neural and Evolutionary Computing, Machine Learning, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1402.3811

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2.0

Feb 17, 2014
02/14

Feb 17, 2014
by
Ella Gale; Ben de Lacy Costello; Andrew Adamatzky

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Time perception is essential for task switching, and in the mammalian brain appears alongside other processes. Memristors are electronic components used as synapses and as models for neurons. The d.c. response of memristors can be considered as a type of short-term memory. Interactions of the memristor d.c. response within networks of memristors leads to the emergence of oscillatory dynamics and intermittent spike trains, which are similar to neural dynamics. Based on this data, the structure...

Topics: Neural and Evolutionary Computing, Robotics, Computing Research Repository, Emerging Technologies

Source: http://arxiv.org/abs/1402.4007

3
3.0

Feb 17, 2014
02/14

Feb 17, 2014
by
Ella Gale; Ben de Lacy Costello; Andrew Adamatzky

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Memristors have been suggested as a novel route to neuromorphic computing based on the similarity between neurons (synapses and ion pumps) and memristors. The D.C. action of the memristor is a current spike, which we think will be fruitful for building memristor computers. In this paper, we introduce 4 different logical assignations to implement sequential logic in the memristor and introduce the physical rules, summation, `bounce-back', directionality and `diminishing returns', elucidated from...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Emerging Technologies, Hardware...

Source: http://arxiv.org/abs/1402.4036

3
3.0

Feb 17, 2014
02/14

Feb 17, 2014
by
Deborah Gater; Attya Iqbal; Jeffrey Davey; Ella Gale

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Memristors have been suggested as neuromorphic computing elements. Spike-time dependent plasticity and the Hodgkin-Huxley model of the neuron have both been modelled effectively by memristor theory. The d.c. response of the memristor is a current spike. Based on these three facts we suggest that memristors are well-placed to interface directly with neurons. In this paper we show that connecting a spiking memristor network to spiking neuronal cells causes a change in the memristor network...

Topics: Biological Physics, Neural and Evolutionary Computing, Physics, Emerging Technologies, Computing...

Source: http://arxiv.org/abs/1402.4029

3
3.0

Feb 18, 2014
02/14

Feb 18, 2014
by
Donia El Kateb; François Fouquet; Johann Bourcier; Yves Le Traon

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In the last years, multi-objective evolutionary algorithms (MOEA) have been applied to different software engineering problems where many conflicting objectives have to be optimized simultaneously. In theory, evolutionary algorithms feature a nice property for runtime optimization as they can provide a solution in any execution time. In practice, based on a Darwinian inspired natural selection, these evolutionary algorithms produce many deadborn solutions whose computation results in a...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Software Engineering

Source: http://arxiv.org/abs/1402.4442

5
5.0

Feb 19, 2014
02/14

Feb 19, 2014
by
Shujia Liu

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This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP). To construct a powerful GA, I use edge swapping(ES) with a local search procedure to determine good combinations of building blocks of parent solutions for generating even better offspring solutions. Experimental results on well studied TSP benchmarks demonstrate that the proposed GA is competitive in finding very high quality solutions on instances with up to 16,862 cities.

Topics: Neural and Evolutionary Computing, Computing Research Repository, Artificial Intelligence

Source: http://arxiv.org/abs/1402.4699

3
3.0

Feb 24, 2014
02/14

Feb 24, 2014
by
Enrico Ampellio; Luca Vassio

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In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation...

Topics: Distributed, Parallel, and Cluster Computing, Neural and Evolutionary Computing, Mathematics,...

Source: http://arxiv.org/abs/1402.5830

3
3.0

Feb 26, 2014
02/14

Feb 26, 2014
by
Csaba Patcas; Attila Bartha

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The debts' clearing problem is about clearing all the debts in a group of n entities (persons, companies etc.) using a minimal number of money transaction operations. The problem is known to be NP-hard in the strong sense. As for many intractable problems, techniques from the field of artificial intelligence are useful in finding solutions close to optimum for large inputs. An evolutionary algorithm for solving the debts' clearing problem is proposed.

Topics: Neural and Evolutionary Computing, Computing Research Repository, Artificial Intelligence

Source: http://arxiv.org/abs/1402.6556

3
3.0

Feb 26, 2014
02/14

Feb 26, 2014
by
Gal Katz; Doron Peled

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Formal methods apply algorithms based on mathematical principles to enhance the reliability of systems. It would only be natural to try to progress from verification, model checking or testing a system against its formal specification into constructing it automatically. Classical algorithmic synthesis theory provides interesting algorithms but also alarming high complexity and undecidability results. The use of genetic programming, in combination with model checking and testing, provides a...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Software Engineering, Artificial...

Source: http://arxiv.org/abs/1402.6785

2
2.0

Feb 27, 2014
02/14

Feb 27, 2014
by
Jakub Nalepa; Zbigniew J. Czech

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This paper presents a parallel memetic algorithm for solving the vehicle routing problem with time windows (VRPTW). The VRPTW is a well-known NP-hard discrete optimization problem with two objectives. The main objective is to minimize the number of vehicles serving customers scattered on the map, and the second one is to minimize the total distance traveled by the vehicles. Here, the fleet size is minimized in the first phase of the proposed method using the parallel heuristic algorithm (PHA),...

Topics: Distributed, Parallel, and Cluster Computing, Neural and Evolutionary Computing, Computing Research...

Source: http://arxiv.org/abs/1402.6942

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7.0

Feb 27, 2014
02/14

Feb 27, 2014
by
Adam Erskine; J Michael Herrmann

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Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving a search task. Instead of adding search biases in order to improve performance in certain problems, we aim to remove algorithm-induced scales by controlling the swarm with a mechanism that is scale-free except possibly for a suppression of scales beyond the system size. In this way a very promising performance is achieved due to the balance of large-scale exploration and local search. The resulting algorithm shows...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1402.6888

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2.0

Mar 7, 2014
03/14

Mar 7, 2014
by
Yukihiro Kamada; Kiyonori Miyasaki

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Autonomous threshold element circuit networks are used to investigate the structure of neural networks. With these circuits, as the transition functions are threshold functions, it is necessary to consider the existence of sequences of state configurations that cannot be transitioned. In this study, we focus on all logical functions of four or fewer variables, and we discuss the periodic sequences and transient series that transition from all sequences of state configurations. Furthermore, by...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1403.1727

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Mar 7, 2014
03/14

Mar 7, 2014
by
Ahmed. H. Asad; Ahmad Taher Azar; Nashwa El-Bendary; Aboul Ella Hassaanien

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Features selection is an essential step for successful data classification, since it reduces the data dimensionality by removing redundant features. Consequently, that minimizes the classification complexity and time in addition to maximizing its accuracy. In this article, a comparative study considering six features selection heuristics is conducted in order to select the best relevant features subset. The tested features vector consists of fourteen features that are computed for each pixel in...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Computer Vision and Pattern...

Source: http://arxiv.org/abs/1403.1735

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2.0

Mar 7, 2014
03/14

Mar 7, 2014
by
Amira Sayed A. Aziz; Ahmad Taher Azar; Aboul Ella Hassanien; Sanaa Al-Ola Hanafy

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Network security is a growing issue, with the evolution of computer systems and expansion of attacks. Biological systems have been inspiring scientists and designs for new adaptive solutions, such as genetic algorithms. In this paper, we present an approach that uses the genetic algorithm to generate anomaly net- work intrusion detectors. In this paper, an algorithm propose use a discretization method for the continuous features selected for the intrusion detection, to create some homogeneity...

Topics: Cryptography and Security, Networking and Internet Architecture, Neural and Evolutionary Computing,...

Source: http://arxiv.org/abs/1403.1729

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7.0

Mar 10, 2014
03/14

Mar 10, 2014
by
Robert John Freeman

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This paper was was first drafted in 2001 as a formalization of the system described in U.S. patent U.S. 7,392,174. It describes a system for implementing a parser based on a kind of cross-product over vectors of contextually similar words. It is being published now in response to nascent interest in vector combination models of syntax and semantics. The method used aggressive substitution of contextually similar words and word groups to enable product vectors to stay in the same space as their...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Computation and Language

Source: http://arxiv.org/abs/1403.2152

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2.0

Mar 12, 2014
03/14

Mar 12, 2014
by
Igor Brigadir; Derek Greene; Pádraig Cunningham

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Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Information Retrieval

Source: http://arxiv.org/abs/1403.2923

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Mar 13, 2014
03/14

Mar 13, 2014
by
Amin Karbasi; Amir Hesam Salavati; Amin Shokrollahi; Lav R. Varshney

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Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number of patterns. Although these designs correct external errors in recall, they assume neurons that compute noiselessly, in contrast to the highly variable neurons in brain regions thought to operate associatively such as hippocampus and olfactory cortex. Here we consider associative memories with noisy internal...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1403.3305

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Mar 13, 2014
03/14

Mar 13, 2014
by
Herbert Jaeger

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The human brain is a dynamical system whose extremely complex sensor-driven neural processes give rise to conceptual, logical cognition. Understanding the interplay between nonlinear neural dynamics and concept-level cognition remains a major scientific challenge. Here I propose a mechanism of neurodynamical organization, called conceptors, which unites nonlinear dynamics with basic principles of conceptual abstraction and logic. It becomes possible to learn, store, abstract, focus, morph,...

Topics: Neural and Evolutionary Computing, Computing Research Repository

Source: http://arxiv.org/abs/1403.3369