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13,602 | Performance Enhancement of Distributed Quasi Steady-State Genetic
Algorithm | cs.NE | This paper proposes a new scheme for performance enhancement of distributed
genetic algorithm (DGA). Initial population is divided in two classes i.e.
female and male. Simple distance based clustering is used for cluster formation
around females. For reclustering self-adaptive K-means is used, which produces
well distr... | computer science |
13,603 | Improving NSGA-II with an Adaptive Mutation Operator | cs.NE | The performance of a Multiobjective Evolutionary Algorithm (MOEA) is
crucially dependent on the parameter setting of the operators. The most desired
control of such parameters presents the characteristic of adaptiveness, i.e.,
the capacity of changing the value of the parameter, in distinct stages of the
evolutionary p... | computer science |
13,604 | Dienstplanerstellung in Krankenhaeusern mittels genetischer Algorithmen | cs.NE | This thesis investigates the use of problem-specific knowledge to enhance a
genetic algorithm approach to multiple-choice optimisation problems. It shows
that such information can significantly enhance performance, but that the
choice of information and the way it is included are important factors for
success. | computer science |
13,605 | Motif Detection Inspired by Immune Memory (JORS) | cs.NE | The search for patterns or motifs in data represents an area of key interest
to many researchers. In this paper we present the Motif Tracking Algorithm, a
novel immune inspired pattern identification tool that is able to identify
variable length unknown motifs which repeat within time series data. The
algorithm searche... | computer science |
13,606 | Solving Poisson Equation by Genetic Algorithms | cs.NE | This paper deals with a method for solving Poisson Equation (PE) based on
genetic algorithms and grammatical evolution. The method forms generations of
solutions expressed in an analytical form. Several examples of PE are tested
and in most cases the exact solution is recovered. But, when the solution
cannot be express... | computer science |
13,607 | Low-Complexity Particle Swarm Optimization for Time-Critical
Applications | cs.NE | 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 ... | computer science |
13,608 | Multimodal Optimization by Sparkling Squid Populations | cs.NE | 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 optimizat... | computer science |
13,609 | Spectrum Hole Prediction Based On Historical Data: A Neural Network
Approach | cs.NE | The concept of cognitive radio pioneered by Mitola promises to change the
future of wireless communication especially in the area of spectrum management.
Currently, the command and control strategy employed in spectrum assignment is
too rigid and needs to be reviewed. Recent studies have shown that assigned
spectrum is... | computer science |
13,610 | A binary differential evolution algorithm learning from explored
solutions | cs.NE | 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 diffe... | computer science |
13,611 | Time series forecasting using neural networks | cs.NE | Recent studies have shown the classification and prediction power of the
Neural Networks. It has been demonstrated that a NN can approximate any
continuous function. Neural networks have been successfully used for
forecasting of financial data series. The classical methods used for time
series prediction like Box-Jenki... | computer science |
13,612 | A Parameterized Complexity Analysis of Bi-level Optimisation with
Evolutionary Algorithms | cs.NE | 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), ... | computer science |
13,613 | N2Sky - Neural Networks as Services in the Clouds | cs.NE | 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 n... | computer science |
13,614 | An Overview of Schema Theory | cs.NE | 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 proj... | computer science |
13,615 | Across neighbourhood search for numerical optimization | cs.NE | Population-based search algorithms (PBSAs), including swarm intelligence
algorithms (SIAs) and evolutionary algorithms (EAs), are competitive
alternatives for solving complex optimization problems and they have been
widely applied to real-world optimization problems in different fields. In this
study, a novel populatio... | computer science |
13,616 | On the Resilience of an Ant-based System in Fuzzy Environments. An
Empirical Study | cs.NE | The current work describes an empirical study conducted in order to
investigate the behavior of an optimization method in a fuzzy environment.
MAX-MIN Ant System, an efficient implementation of a heuristic method is used
for solving an optimization problem derived from the Traveling Salesman Problem
(TSP). Several publ... | computer science |
13,617 | Evolving Accuracy: A Genetic Algorithm to Improve Election Night
Forecasts | cs.NE | 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
outperf... | computer science |
13,618 | Evolutionary Optimization for Decision Making under Uncertainty | cs.NE | 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-s... | computer science |
13,619 | Revolutionary Algorithms | cs.NE | 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 potent... | computer science |
13,620 | An Evolutionary Approach towards Clustering Airborne Laser Scanning Data | cs.NE | 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 th... | computer science |
13,621 | Genetic Algorithms and its use with back-propagation network | cs.NE | Genetic algorithms are considered as one of the most efficient search
techniques. Although they do not offer an optimal solution, their ability to
reach a suitable solution in considerably short time gives them their
respectable role in many AI techniques. This work introduces genetic algorithms
and describes their cha... | computer science |
13,622 | Reducing the Computational Cost in Multi-objective Evolutionary
Algorithms by Filtering Worthless Individuals | cs.NE | The large number of exact fitness function evaluations makes evolutionary
algorithms to have computational cost. In some real-world problems, reducing
number of these evaluations is much more valuable even by increasing
computational complexity and spending more time. To fulfill this target, we
introduce an effective f... | computer science |
13,623 | Microstrip Coupler Design Using Bat Algorithm | cs.NE | Evolutionary and swarm algorithms have found many applications in design
problems since todays computing power enables these algorithms to find
solutions to complicated design problems very fast. Newly proposed hybrid
algorithm, bat algorithm, has been applied for the design of microwave
microstrip couplers for the fir... | computer science |
13,624 | Associative Memories Based on Multiple-Valued Sparse Clustered Networks | cs.NE | 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 updat... | computer science |
13,625 | MCA Learning Algorithm for Incident Signals Estimation: A Review | cs.NE | Recently there has been many works on adaptive subspace filtering in the
signal processing literature. Most of them are concerned with tracking the
signal subspace spanned by the eigenvectors corresponding to the eigenvalues of
the covariance matrix of the signal plus noise data. Minor Component Analysis
(MCA) is impor... | computer science |
13,626 | An Evolutionary approach for solving Shrödinger Equation | cs.NE | The purpose of this paper is to present a method of solving the Shr\"odinger
Equation (SE) by Genetic Algorithms and Grammatical Evolution. The method forms
generations of trial solutions expressed in an analytical form. We illustrate
the effectiveness of this method providing, for example, the results of its
applicati... | computer science |
13,627 | Clustering Multidimensional Data with PSO based Algorithm | cs.NE | Data clustering is a recognized data analysis method in data mining whereas
K-Means is the well known partitional clustering method, possessing pleasant
features. We observed that, K-Means and other partitional clustering techniques
suffer from several limitations such as initial cluster centre selection,
preknowledge ... | computer science |
13,628 | CriPS: Critical Dynamics in Particle Swarm Optimization | cs.NE | 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 sc... | computer science |
13,629 | Artificial Neuron Modelling Based on Wave Shape | cs.NE | This paper describes a new model for an artificial neural network processing
unit or neuron. It is slightly different to a traditional feedforward network
by the fact that it favours a mechanism of trying to match the wave-like
'shape' of the input with the shape of the output against specific value error
corrections. ... | computer science |
13,630 | On the Sequence of State Configurations in the Garden of Eden | cs.NE | 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 log... | computer science |
13,631 | Application of Particle Swarm Optimization to Microwave Tapered
Microstrip Lines | cs.NE | Application of metaheuristic algorithms has been of continued interest in the
field of electrical engineering because of their powerful features. In this
work special design is done for a tapered transmission line used for matching
an arbitrary real load to a 50{\Omega} line. The problem at hand is to match
this arbitr... | computer science |
13,632 | Memory Capacity of Neural Networks using a Circulant Weight Matrix | cs.NE | This paper presents results on the memory capacity of a generalized feedback
neural network using a circulant matrix. Children are capable of learning soon
after birth which indicates that the neural networks of the brain have prior
learnt capacity that is a consequence of the regular structures in the brain's
organiza... | computer science |
13,633 | Noise Facilitation in Associative Memories of Exponential Capacity | cs.NE | 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 hig... | computer science |
13,634 | Controlling Recurrent Neural Networks by Conceptors | cs.NE | 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
organizati... | computer science |
13,635 | A Physarum-Inspired Approach to Optimal Supply Chain Network Design at
Minimum Total Cost with Demand Satisfaction | cs.NE | A supply chain is a system which moves products from a supplier to customers.
The supply chains are ubiquitous. They play a key role in all economic
activities. Inspired by biological principles of nutrients' distribution in
protoplasmic networks of slime mould Physarum polycephalum we propose a novel
algorithm for a s... | computer science |
13,636 | Offshore Wind Farm Layout Optimization Using Adapted Genetic Algorithm:
A different perspective | cs.NE | In this paper we study the problem of optimal layout of an offshore wind farm
to minimize the wake effect impacts. Considering the specific requirements of
concerned offshore wind farm, we propose an adaptive genetic algorithm (AGA)
which introduces location swaps to replace random crossovers in conventional
GAs. That ... | computer science |
13,637 | Unbiased Black-Box Complexities of Jump Functions | cs.NE | We analyze the unbiased black-box complexity of jump functions with small,
medium, and large sizes of the fitness plateau surrounding the optimal
solution.
Among other results, we show that when the jump size is $(1/2 -
\varepsilon)n$, that is, only a small constant fraction of the fitness values
is visible, then the... | computer science |
13,638 | Pulling back error to the hidden-node parameter technology:
Single-hidden-layer feedforward network without output weight | cs.NE | According to conventional neural network theories, the feature of
single-hidden-layer feedforward neural networks(SLFNs) resorts to parameters of
the weighted connections and hidden nodes. SLFNs are universal approximators
when at least the parameters of the networks including hidden-node parameter
and output weight ar... | computer science |
13,639 | A Memetic Algorithm for the Linear Ordering Problem with Cumulative
Costs | cs.NE | This paper introduces an effective memetic algorithm for the linear ordering
problem with cumulative costs. The proposed algorithm combines an order-based
recombination operator with an improved forward-backward local search procedure
and employs a solution quality based replacement criterion for pool updating.
Extensi... | computer science |
13,640 | Optimization of OFDM radar waveforms using genetic algorithms | cs.NE | In this paper, we present our investigations on the use of single objective
and multiobjective genetic algorithms based optimisation algorithms to improve
the design of OFDM pulses for radar. We discuss these optimization procedures
in the scope of a waveform design intended for two different radar processing
solutions... | computer science |
13,641 | A Genetic Algorithm for solving Quadratic Assignment Problem(QAP) | cs.NE | The Quadratic Assignment Problem (QAP) is one of the models used for the
multi-row layout problem with facilities of equal area. There are a set of n
facilities and a set of n locations. For each pair of locations, a distance is
specified and for each pair of facilities a weight or flow is specified (e.g.,
the amount o... | computer science |
13,642 | Radial basis function process neural network training based on
generalized frechet distance and GA-SA hybrid strategy | cs.NE | For learning problem of Radial Basis Function Process Neural Network
(RBF-PNN), an optimization training method based on GA combined with SA is
proposed in this paper. Through building generalized Fr\'echet distance to
measure similarity between time-varying function samples, the learning problem
of radial basis centre... | computer science |
13,643 | Online and Adaptive Pseudoinverse Solutions for ELM Weights | cs.NE | The ELM method has become widely used for classification and regressions
problems as a result of its accuracy, simplicity and ease of use. The solution
of the hidden layer weights by means of a matrix pseudoinverse operation is a
significant contributor to the utility of the method; however, the conventional
calculatio... | computer science |
13,644 | ELM Solutions for Event-Based Systems | cs.NE | Whilst most engineered systems use signals that are continuous in time, there
is a domain of systems in which signals consist of events. Events, like Dirac
delta functions, have no meaningful time duration. Many important real-world
systems are intrinsically event-based, including the mammalian brain, in which
the prim... | computer science |
13,645 | Memetic Search in Differential Evolution Algorithm | cs.NE | Differential Evolution (DE) is a renowned optimization stratagem that can
easily solve nonlinear and comprehensive problems. DE is a well known and
uncomplicated population based probabilistic approach for comprehensive
optimization. It has apparently outperformed a number of Evolutionary
Algorithms and further search ... | computer science |
13,646 | Randomized Memetic Artificial Bee Colony Algorithm | cs.NE | Artificial Bee Colony (ABC) optimization algorithm is one of the recent
population based probabilistic approach developed for global optimization. ABC
is simple and has been showed significant improvement over other Nature
Inspired Algorithms (NIAs) when tested over some standard benchmark functions
and for some comple... | computer science |
13,647 | Real-Time Traffic Signal Control for Modern Roundabouts by Using
Particle Swarm Optimization-Based Fuzzy Controller | cs.NE | Due to that the existing traffic facilities can hardly be extended,
developing traffic signal control methods is the most important way to improve
the traffic efficiency of modern roundabouts. This paper proposes a novel
traffic signal controller with two fuzzy layers for signalizing the roundabout.
The outer layer of ... | computer science |
13,648 | The Case for a Mixed-Initiative Collaborative Neuroevolution Approach | cs.NE | It is clear that the current attempts at using algorithms to create
artificial neural networks have had mixed success at best when it comes to
creating large networks and/or complex behavior. This should not be unexpected,
as creating an artificial brain is essentially a design problem. Human design
ingenuity still sur... | computer science |
13,649 | New crossover operators for multiple subset selection tasks | cs.NE | We have introduced two crossover operators, MMX-BLXexploit and
MMX-BLXexplore, for simultaneously solving multiple feature/subset selection
problems where the features may have numeric attributes and the subset sizes
are not predefined. These operators differ on the level of exploration and
exploitation they perform; o... | computer science |
13,650 | Structural bias in population-based algorithms | cs.NE | Challenging optimisation problems are abundant in all areas of science. Since
the 1950s, scientists have developed ever-diversifying families of black box
optimisation algorithms designed to address any optimisation problem, requiring
only that quality of a candidate solution is calculated via a fitness function
specif... | computer science |
13,651 | Evolutionary Artificial Neural Network Based on Chemical Reaction
Optimization | cs.NE | Evolutionary algorithms (EAs) are very popular tools to design and evolve
artificial neural networks (ANNs), especially to train them. These methods have
advantages over the conventional backpropagation (BP) method because of their
low computational requirement when searching in a large solution space. In this
paper, w... | computer science |
13,652 | Real-Coded Chemical Reaction Optimization with Different Perturbation
Functions | cs.NE | Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics
the interactions of molecules in chemical reactions to search for the global
optimum. The perturbation function greatly influences the performance of CRO on
solving different continuous problems. In this paper, we study four different
probabi... | computer science |
13,653 | Sensor Deployment for Air Pollution Monitoring Using Public
Transportation System | cs.NE | Air pollution monitoring is a very popular research topic and many monitoring
systems have been developed. In this paper, we formulate the Bus Sensor
Deployment Problem (BSDP) to select the bus routes on which sensors are
deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is
a recently propose... | computer science |
13,654 | Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using
Chemical Reaction Optimization | cs.NE | An electric vehicle (EV) may be used as energy storage which allows the
bi-directional electricity flow between the vehicle's battery and the electric
power grid. In order to flatten the load profile of the electricity system, EV
scheduling has become a hot research topic in recent years. In this paper, we
propose a ne... | computer science |
13,655 | An Inter-molecular Adaptive Collision Scheme for Chemical Reaction
Optimization | cs.NE | Optimization techniques are frequently applied in science and engineering
research and development. Evolutionary algorithms, as a kind of general-purpose
metaheuristic, have been shown to be very effective in solving a wide range of
optimization problems. A recently proposed chemical-reaction-inspired
metaheuristic, Ch... | computer science |
13,656 | Chemical Reaction Optimization for the Set Covering Problem | cs.NE | The set covering problem (SCP) is one of the representative combinatorial
optimization problems, having many practical applications. This paper
investigates the development of an algorithm to solve SCP by employing chemical
reaction optimization (CRO), a general-purpose metaheuristic. It is tested on a
wide range of be... | computer science |
13,657 | Product Reservoir Computing: Time-Series Computation with Multiplicative
Neurons | cs.NE | Echo state networks (ESN), a type of reservoir computing (RC) architecture,
are efficient and accurate artificial neural systems for time series processing
and learning. An ESN consists of a core of recurrent neural networks, called a
reservoir, with a small number of tunable parameters to generate a
high-dimensional r... | computer science |
13,658 | A Social Spider Algorithm for Global Optimization | cs.NE | The growing complexity of real-world problems has motivated computer
scientists to search for efficient problem-solving methods. Metaheuristics
based on evolutionary computation and swarm intelligence are outstanding
examples of nature-inspired solution techniques. Inspired by the social
spiders, we propose a novel Soc... | computer science |
13,659 | On the Dynamics of a Recurrent Hopfield Network | cs.NE | In this research paper novel real/complex valued recurrent Hopfield Neural
Network (RHNN) is proposed. The method of synthesizing the energy landscape of
such a network and the experimental investigation of dynamics of Recurrent
Hopfield Network is discussed. Parallel modes of operation (other than fully
parallel mode)... | computer science |
13,660 | The Benefit of Sex in Noisy Evolutionary Search | cs.NE | The benefit of sexual recombination is one of the most fundamental questions
both in population genetics and evolutionary computation. It is widely believed
that recombination helps solving difficult optimization problems. We present
the first result, which rigorously proves that it is beneficial to use sexual
recombin... | computer science |
13,661 | Analysis of Solution Quality of a Multiobjective Optimization-based
Evolutionary Algorithm for Knapsack Problem | cs.NE | Multi-objective optimisation is regarded as one of the most promising ways
for dealing with constrained optimisation problems in evolutionary
optimisation. This paper presents a theoretical investigation of a
multi-objective optimisation evolutionary algorithm for solving the 0-1
knapsack problem. Two initialisation me... | computer science |
13,662 | Exploring Transfer Function Nonlinearity in Echo State Networks | cs.NE | Supralinear and sublinear pre-synaptic and dendritic integration is
considered to be responsible for nonlinear computation power of biological
neurons, emphasizing the role of nonlinear integration as opposed to nonlinear
output thresholding. How, why, and to what degree the transfer function
nonlinearity helps biologi... | computer science |
13,663 | Positive Neural Networks in Discrete Time Implement Monotone-Regular
Behaviors | cs.NE | We study the expressive power of positive neural networks. The model uses
positive connection weights and multiple input neurons. Different behaviors can
be expressed by varying the connection weights. We show that in discrete time,
and in absence of noise, the class of positive neural networks captures the
so-called m... | computer science |
13,664 | A neuromorphic hardware framework based on population coding | cs.NE | In the biological nervous system, large neuronal populations work
collaboratively to encode sensory stimuli. These neuronal populations are
characterised by a diverse distribution of tuning curves, ensuring that the
entire range of input stimuli is encoded. Based on these principles, we have
designed a neuromorphic sys... | computer science |
13,665 | Genetic optimization of the Hyperloop route through the Grapevine | cs.NE | We demonstrate a genetic algorithm that employs a versatile fitness function
to optimize route selection for the Hyperloop, a proposed high speed passenger
transportation system. | computer science |
13,666 | Estimation of the parameters of an infectious disease model using neural
networks | cs.NE | In this paper, we propose a realistic mathematical model taking into account
the mutual interference among the interacting populations. This model attempts
to describe the control (vaccination) function as a function of the number of
infective individuals, which is an improvement over the existing susceptible
infective... | computer science |
13,667 | Denoising Autoencoders for fast Combinatorial Black Box Optimization | cs.NE | Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Autoencoders (AE) are
generative stochastic networks with these desired properties. We integrate a
special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate
the performance o... | computer science |
13,668 | Technical Analysis on Financial Forecasting | cs.NE | Financial forecasting is an estimation of future financial outcomes for a
company, industry, country using historical internal accounting and sales data.
We may predict the future outcome of BSE_SENSEX practically by some soft
computing techniques and can also optimized using PSO (Particle Swarm
Optimization), EA (Evol... | computer science |
13,669 | Benchmarking NLopt and state-of-art algorithms for Continuous Global
Optimization via Hybrid IACO$_\mathbb{R}$ | cs.NE | This paper presents a comparative analysis of the performance of the
Incremental Ant Colony algorithm for continuous optimization
($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library.
The key objective is to understand how the various algorithms in the NLopt
library perform in combination with t... | computer science |
13,670 | Simulation of Genetic Algorithm: Traffic Light Efficiency | cs.NE | Traffic is a problem in many urban areas worldwide. Traffic flow is dictated
by certain devices such as traffic lights. The traffic lights signal when each
lane is able to pass through the intersection. Often, static schedules
interfere with ideal traffic flow. The purpose of this project was to find a
way to make inte... | computer science |
13,671 | Improved Calibration of Near-Infrared Spectra by Using Ensembles of
Neural Network Models | cs.NE | IR or near-infrared (NIR) spectroscopy is a method used to identify a
compound or to analyze the composition of a material. Calibration of NIR
spectra refers to the use of the spectra as multivariate descriptors to predict
concentrations of the constituents. To build a calibration model,
state-of-the-art software predo... | computer science |
13,672 | Feeder Load Balancing using Neural Network | cs.NE | The distribution system problems, such as planning, loss minimization, and
energy restoration, usually involve the phase balancing or network
reconfiguration procedures. The determination of an optimal phase balance is,
in general, a combinatorial optimization problem. This paper proposes optimal
reconfiguration of the... | computer science |
13,673 | Study of all the periods of a Neuronal Recurrence Equation | cs.NE | We characterize the structure of the periods of a neuronal recurrence
equation. Firstly, we give a characterization of k-chains in 0-1 periodic
sequences. Secondly, we characterize the periods of all cycles of some neuronal
recurrence equation. Thirdly, we explain how these results can be used to
deduce the existence o... | computer science |
13,674 | Gibbs Sampling with Low-Power Spiking Digital Neurons | cs.NE | Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in a wide variety of applications including image classification and
speech recognition. Inference and learning in these algorithms uses a Markov
Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms
the kernel of... | computer science |
13,675 | Some Further Evidence about Magnification and Shape in Neural Gas | cs.NE | Neural gas (NG) is a robust vector quantization algorithm with a well-known
mathematical model. According to this, the neural gas samples the underlying
data distribution following a power law with a magnification exponent that
depends on data dimensionality only. The effects of shape in the input data
distribution, ho... | computer science |
13,676 | Encoding Spike Patterns in Multilayer Spiking Neural Networks | cs.NE | Information encoding in the nervous system is supported through the precise
spike-timings of neurons; however, an understanding of the underlying processes
by which such representations are formed in the first place remains unclear.
Here we examine how networks of spiking neurons can learn to encode for input
patterns ... | computer science |
13,677 | New Ideas for Brain Modelling 3 | cs.NE | This paper considers a process for the creation and subsequent firing of
sequences of neuronal patterns, as might be found in the human brain. The scale
is one of larger patterns emerging from an ensemble mass, possibly through some
type of energy equation and a reduction procedure. The links between the
patterns can b... | computer science |
13,678 | A simple and efficient SNN and its performance & robustness evaluation
method to enable hardware implementation | cs.NE | Spiking Neural Networks (SNN) are more closely related to brain-like
computation and inspire hardware implementation. This is enabled by small
networks that give high performance on standard classification problems. In
literature, typical SNNs are deep and complex in terms of network structure,
weight update rules and ... | computer science |
13,679 | Neural Turing Machines: Convergence of Copy Tasks | cs.NE | The architecture of neural Turing machines is differentiable end to end and
is trainable with gradient descent methods. Due to their large unfolded depth
Neural Turing Machines are hard to train and because of their linear access of
complete memory they do not scale. Other architectures have been studied to
overcome th... | computer science |
13,680 | Improved Quick Hypervolume Algorithm | cs.NE | In this paper, we present a significant improvement of Quick Hypervolume
algorithm, one of the state-of-the-art algorithms for calculating exact
hypervolume of the space dominated by a set of d-dimensional points. This value
is often used as a quality indicator in multiobjective evolutionary algorithms
and other multio... | computer science |
13,681 | Empirical Evaluation of A New Approach to Simplifying Long Short-term
Memory (LSTM) | cs.NE | The standard LSTM, although it succeeds in the modeling long-range
dependences, suffers from a highly complex structure that can be simplified
through modifications to its gate units. This paper was to perform an empirical
comparison between the standard LSTM and three new simplified variants that
were obtained by elim... | computer science |
13,682 | Understanding the Impact of Precision Quantization on the Accuracy and
Energy of Neural Networks | cs.NE | Deep neural networks are gaining in popularity as they are used to generate
state-of-the-art results for a variety of computer vision and machine learning
applications. At the same time, these networks have grown in depth and
complexity in order to solve harder problems. Given the limitations in power
budgets dedicated... | computer science |
13,683 | Delta Networks for Optimized Recurrent Network Computation | cs.NE | Many neural networks exhibit stability in their activation patterns over time
in response to inputs from sensors operating under real-world conditions. By
capitalizing on this property of natural signals, we propose a Recurrent Neural
Network (RNN) architecture called a delta network in which each neuron
transmits its ... | computer science |
13,684 | Transfer Learning based Dynamic Multiobjective Optimization Algorithms | cs.NE | One of the major distinguishing features of the dynamic multiobjective
optimization problems (DMOPs) is the optimization objectives will change over
time, thus tracking the varying Pareto-optimal front becomes a challenge. One
of the promising solutions is reusing the "experiences" to construct a
prediction model via s... | computer science |
13,685 | Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A
Case Study with the UCI Thyroid Disease Database | cs.NE | The random initialization of weights of a multilayer perceptron makes it
possible to model its training process as a Las Vegas algorithm, i.e. a
randomized algorithm which stops when some required training error is obtained,
and whose execution time is a random variable. This modeling is used to perform
a case study on... | computer science |
13,686 | Stochastic Optimization Algorithms | cs.NE | When looking for a solution, deterministic methods have the enormous
advantage that they do find global optima. Unfortunately, they are very
CPU-intensive, and are useless on untractable NP-hard problems that would
require thousands of years for cutting-edge computers to explore. In order to
get a result, one needs to ... | computer science |
13,687 | Estimation of fuzzy anomalies in Water Distribution Systems | cs.NE | State estimation is necessary in diagnosing anomalies in Water Demand Systems
(WDS). In this paper we present a neural network performing such a task. State
estimation is performed by using optimization, which tries to reconcile all the
available information. Quantification of the uncertainty of the input data
(telemet... | computer science |
13,688 | The Comparison of Methods Artificial Neural Network with Linear
Regression Using Specific Variables for Prediction Stock Price in Tehran
Stock Exchange | cs.NE | In this paper, researchers estimated the stock price of activated companies
in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial
Neural Network methods and compared these two methods. In Artificial Neural
Network, of General Regression Neural Network method (GRNN) for architecture is
used. In th... | computer science |
13,689 | Delta Learning Rule for the Active Sites Model | cs.NE | This paper reports the results on methods of comparing the memory retrieval
capacity of the Hebbian neural network which implements the B-Matrix approach,
by using the Widrow-Hoff rule of learning. We then, extend the recently
proposed Active Sites model by developing a delta rule to increase memory
capacity. Also, thi... | computer science |
13,690 | Building Blocks Propagation in Quantum-Inspired Genetic Algorithm | cs.NE | This paper presents an analysis of building blocks propagation in
Quantum-Inspired Genetic Algorithm, which belongs to a new class of
metaheuristics drawing their inspiration from both biological evolution and
unitary evolution of quantum systems. The expected number of quantum
chromosomes matching a schema has been an... | computer science |
13,691 | Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean
Functions | cs.NE | With this paper, we contribute to the understanding of ant colony
optimization (ACO) algorithms by formally analyzing their runtime behavior. We
study simple MAX-MIN ant systems on the class of linear pseudo-Boolean
functions defined on binary strings of length 'n'. Our investigations point out
how the progress accordi... | computer science |
13,692 | Evolutionary Approach to Test Generation for Functional BIST | cs.NE | In the paper, an evolutionary approach to test generation for functional BIST
is considered. The aim of the proposed scheme is to minimize the test data
volume by allowing the device's microprogram to test its logic, providing an
observation structure to the system, and generating appropriate test data for
the given ar... | computer science |
13,693 | Bottleneck of using single memristor as a synapse and its solution | cs.NE | It is now widely accepted that memristive devices are perfect candidates for
the emulation of biological synapses in neuromorphic systems. This is mainly
because of the fact that like the strength of synapse, memristance of the
memristive device can be tuned actively (e.g., by the application of volt- age
or current). ... | computer science |
13,694 | Discriminating between Nasal and Mouth Breathing | cs.NE | The recommendation to change breathing patterns from the mouth to the nose
can have a significantly positive impact upon the general well being of the
individual. We classify nasal and mouth breathing by using an acoustic sensor
and intelligent signal processing techniques. The overall purpose is to
investigate the pos... | computer science |
13,695 | Pure Strategy or Mixed Strategy? | cs.NE | Mixed strategy EAs aim to integrate several mutation operators into a single
algorithm. However few theoretical analysis has been made to answer the
question whether and when the performance of mixed strategy EAs is better than
that of pure strategy EAs. In theory, the performance of EAs can be measured by
asymptotic c... | computer science |
13,696 | The Expert System Designed to Improve Customer Satisfaction | cs.NE | Customer Relationship Management becomes a leading business strategy in
highly competitive business environment. It aims to enhance the performance of
the businesses by improving the customer satisfaction and loyalty. The
objective of this paper is to improve customer satisfaction on product's colors
and design with th... | computer science |
13,697 | Between theory and practice: guidelines for an optimization scheme with
genetic algorithms - Part I: single-objective continuous global optimization | cs.NE | The rapid advances in the field of optimization methods in many pure and
applied science pose the difficulty of keeping track of the developments as
well as selecting an appropriate technique that best suits the problem in-hand.
From a practitioner point of view is rightful to wander "which optimization
method is the b... | computer science |
13,698 | Search space analysis with Wang-Landau sampling and slow adaptive walks | cs.NE | Two complementary techniques for analyzing search spaces are proposed: (i) an
algorithm to detect search points with potential to be local optima; and (ii) a
slightly adjusted Wang-Landau sampling algorithm to explore larger search
spaces. The detection algorithm assumes that local optima are points which are
easier to... | computer science |
13,699 | Generation of Two-Layer Monotonic Functions | cs.NE | The problem of implementing a class of functions with particular conditions
by using monotonic multilayer functions is considered. A genetic algorithm is
used to create monotonic functions of a certain class, and these are
implemented with two-layer monotonic functions. The existence of a solution to
the given problem ... | computer science |
13,700 | Intelligent Algorithm for Optimum Solutions Based on the Principles of
Bat Sonar | cs.NE | This paper presents a new intelligent algorithm that can solve the problems
of finding the optimum solution in the state space among which the desired
solution resides. The algorithm mimics the principles of bat sonar in finding
its targets. The algorithm introduces three search approaches. The first search
approach co... | computer science |
13,701 | A Survey on Techniques of Improving Generalization Ability of Genetic
Programming Solutions | cs.NE | In the field of empirical modeling using Genetic Programming (GP), it is
important to evolve solution with good generalization ability. Generalization
ability of GP solutions get affected by two important issues: bloat and
over-fitting. We surveyed and classified existing literature related to
different techniques used... | computer science |
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