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13,502 | Self-Delimiting Neural Networks | cs.NE | Self-delimiting (SLIM) programs are a central concept of theoretical computer
science, particularly algorithmic information & probability theory, and
asymptotically optimal program search (AOPS). To apply AOPS to (possibly
recurrent) neural networks (NNs), I introduce SLIM NNs. Neurons of a typical
SLIM NN have thresho... | computer science |
13,503 | Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms | cs.NE | A significant challenge in nature-inspired algorithmics is the identification
of specific characteristics of problems that make them harder (or easier) to
solve using specific methods. The hope is that, by identifying these
characteristics, we may more easily predict which algorithms are best-suited to
problems sharing... | computer science |
13,504 | Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics | cs.NE | We introduce a dynamic neural algorithm called Dynamic Neural (DN)
SARSA(\lambda) for learning a behavioral sequence from delayed reward.
DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence
representation, classical reinforcement learning, and a computational
neuroscience model of working memo... | computer science |
13,505 | Comparing several heuristics for a packing problem | cs.NE | Packing problems are in general NP-hard, even for simple cases. Since now
there are no highly efficient algorithms available for solving packing
problems. The two-dimensional bin packing problem is about packing all given
rectangular items, into a minimum size rectangular bin, without overlapping.
The restriction is th... | computer science |
13,506 | Towards a Better Understanding of the Local Attractor in Particle Swarm
Optimization: Speed and Solution Quality | cs.NE | Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic
for solving continuous optimization problems. Although this technique is widely
used, the understanding of the mechanisms that make swarms so successful is
still limited. We present the first substantial experimental investigation of
the infl... | computer science |
13,507 | Maximizing Diversity for Multimodal Optimization | cs.NE | Most multimodal optimization algorithms use the so called \textit{niching
methods}~\cite{mahfoud1995niching} in order to promote diversity during
optimization, while others, like \textit{Artificial Immune
Systems}~\cite{de2010conceptual} try to find multiple solutions as its main
objective. One of such algorithms, call... | computer science |
13,508 | Simulation based Hardness Evaluation of a Multi-Objective Genetic
Algorithm | cs.NE | Studies have shown that multi-objective optimization problems are hard
problems. Such problems either require longer time to converge to an optimum
solution, or may not converge at all. Recently some researchers have claimed
that real culprit for increasing the hardness of multi-objective problems are
not the number of... | computer science |
13,509 | Application and Verification of Algorithm Learning Based Neural Network | cs.NE | This paper has been withdrawn by the author due to a crucial accuracy error
in Fig. 5. For precise performance of ALBNN please refer to Yoon et al.'s work
in the following article. Yoon, H., Park, C. S., Kim, J. S., & Baek, J. G.
(2013). Algorithm learning based neural network integrating feature selection
and classifi... | computer science |
13,510 | Conceptors: an easy introduction | cs.NE | Conceptors provide an elementary neuro-computational mechanism which sheds a
fresh and unifying light on a diversity of cognitive phenomena. A number of
demanding learning and processing tasks can be solved with unprecedented ease,
robustness and accuracy. Some of these tasks were impossible to solve before.
This entir... | computer science |
13,511 | Explicit Computation of Input Weights in Extreme Learning Machines | cs.NE | We present a closed form expression for initializing the input weights in a
multi-layer perceptron, which can be used as the first step in synthesis of an
Extreme Learning Ma-chine. The expression is based on the standard function for
a separating hyperplane as computed in multilayer perceptrons and linear
Support Vect... | computer science |
13,512 | A swarm optimization algorithm inspired in the behavior of the
social-spider | cs.NE | Swarm intelligence is a research field that models the collective behavior in
swarms of insects or animals. Several algorithms arising from such models have
been proposed to solve a wide range of complex optimization problems. In this
paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is
propose... | computer science |
13,513 | An Evolutionary Approach for Optimal Citing and Sizing of Micro-Grid in
Radial Distribution Systems | cs.NE | This Paper presents the methodology of penetration of Micro-Grids (MG) in the
radial distribution system (RDS). The aim of this paper is to minimize a total
real power loss that descends the performance of the radial distribution system
by integrating various renewable resources as Distributed Generation (DG). The
comb... | computer science |
13,514 | A Heuristic Method to Generate Better Initial Population for
Evolutionary Methods | cs.NE | Initial population plays an important role in heuristic algorithms such as GA
as it help to decrease the time those algorithms need to achieve an acceptable
result. Furthermore, it may influence the quality of the final answer given by
evolutionary algorithms. In this paper, we shall introduce a heuristic method
to gen... | computer science |
13,515 | Thermodynamic-RAM Technology Stack | cs.NE | We introduce a technology stack or specification describing the multiple
levels of abstraction and specialization needed to implement a neuromorphic
processor (NPU) based on the previously-described concept of AHaH Computing and
integrate it into today's digital computing systems. The general purpose NPU
implementation... | computer science |
13,516 | An optimization algorithm for multimodal functions inspired by
collective animal behavior | cs.NE | Interest in multimodal function optimization is expanding rapidly since real
world optimization problems often demand locating multiple optima within a
search space. This article presents a new multimodal optimization algorithm
named as the Collective Animal Behavior (CAB). Animal groups, such as schools
of fish, flock... | computer science |
13,517 | Self-Adaptation Mechanism to Control the Diversity of the Population in
Genetic Algorithm | cs.NE | One of the problems in applying Genetic Algorithm is that there is some
situation where the evolutionary process converges too fast to a solution which
causes it to be trapped in local optima. To overcome this problem, a proper
diversity in the candidate solutions must be determined. Most existing
diversity-maintenance... | computer science |
13,518 | A Framework for Predicting Phishing Websites using Neural Networks | cs.NE | In India many people are now dependent on online banking. This raises
security concerns as the banking websites are forged and fraud can be committed
by identity theft. These forged websites are called as Phishing websites and
created by malicious people to mimic web pages of real websites and it attempts
to defraud pe... | computer science |
13,519 | An Efficient Preprocessing Methodology for Discovering Patterns and
Clustering of Web Users using a Dynamic ART1 Neural Network | cs.NE | In this paper, a complete preprocessing methodology for discovering patterns
in web usage mining process to improve the quality of data by reducing the
quantity of data has been proposed. A dynamic ART1 neural network clustering
algorithm to group users according to their Web access patterns with its neat
architecture ... | computer science |
13,520 | A New Method for Lower Bounds on the Running Time of Evolutionary
Algorithms | cs.NE | We present a new method for proving lower bounds on the expected running time
of evolutionary algorithms. It is based on fitness-level partitions and an
additional condition on transition probabilities between fitness levels. The
method is versatile, intuitive, elegant, and very powerful. It yields exact or
near-exact ... | computer science |
13,521 | Analysis of Speedups in Parallel Evolutionary Algorithms for
Combinatorial Optimization | cs.NE | Evolutionary algorithms are popular heuristics for solving various
combinatorial problems as they are easy to apply and often produce good
results. Island models parallelize evolution by using different populations,
called islands, which are connected by a graph structure as communication
topology. Each island periodic... | computer science |
13,522 | CIXL2: A Crossover Operator for Evolutionary Algorithms Based on
Population Features | cs.NE | In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into acc... | computer science |
13,523 | Developing a supervised training algorithm for limited precision
feed-forward spiking neural networks | cs.NE | Spiking neural networks have been referred to as the third generation of
artificial neural networks where the information is coded as time of the
spikes. There are a number of different spiking neuron models available and
they are categorized based on their level of abstraction. In addition, there
are two known learnin... | computer science |
13,524 | Memetic Algorithms: Parametrization and Balancing Local and Global
Search | cs.NE | This is a preprint of a book chapter from the Handbook of Memetic Algorithms,
Studies in Computational Intelligence, Vol. 379, ISBN 978-3-642-23246-6,
Springer, edited by F. Neri, C. Cotta, and P. Moscato. It is devoted to the
parametrization of memetic algorithms and how to find a good balance between
global and local... | computer science |
13,525 | Discretization of a matrix in the problem of quadratic functional binary
minimization | cs.NE | The capability of discretization of matrix elements in the problem of
quadratic functional minimization with linear member built on matrix in
N-dimensional configuration space with discrete coordinates is researched. It
is shown, that optimal procedure of replacement matrix elements by the integer
quantities with the l... | computer science |
13,526 | Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange
Rate Using Artificial Neural Network | cs.NE | A large part of the workforce, and growing every day, is originally from
India. India one of the second largest populations in the world, they have a
lot to offer in terms of jobs. The sheer number of IT workers makes them a
formidable travelling force as well, easily picking up employment in English
speaking countries... | computer science |
13,527 | More Effective Crossover Operators for the All-Pairs Shortest Path
Problem | cs.NE | The all-pairs shortest path problem is the first non-artificial problem for
which it was shown that adding crossover can significantly speed up a
mutation-only evolutionary algorithm. Recently, the analysis of this algorithm
was refined and it was shown to have an expected optimization time (w.r.t. the
number of fitnes... | computer science |
13,528 | Meme as Building Block for Evolutionary Optimization of Problem
Instances | cs.NE | A significantly under-explored area of evolutionary optimization in the
literature is the study of optimization methodologies that can evolve along
with the problems solved. Particularly, present evolutionary optimization
approaches generally start their search from scratch or the ground-zero state
of knowledge, indepe... | computer science |
13,529 | Nugget Discovery with a Multi-objective Cultural Algorithm | cs.NE | Partial classification popularly known as nugget discovery comes under
descriptive knowledge discovery. It involves mining rules for a target class of
interest. Classification "If-Then" rules are the most sought out by decision
makers since they are the most comprehensible form of knowledge mined by data
mining techniq... | computer science |
13,530 | Learning the Pseudoinverse Solution to Network Weights | cs.NE | The last decade has seen the parallel emergence in computational neuroscience
and machine learning of neural network structures which spread the input signal
randomly to a higher dimensional space; perform a nonlinear activation; and
then solve for a regression or classification output by means of a mathematical
pseudo... | computer science |
13,531 | Empirical review of standard benchmark functions using evolutionary
global optimization | cs.NE | We have employed a recent implementation of genetic algorithms to study a
range of standard benchmark functions for global optimization. It turns out
that some of them are not very useful as challenging test functions, since they
neither allow for a discrimination between different variants of genetic
operators nor exh... | computer science |
13,532 | The Road to VEGAS: Guiding the Search over Neutral Networks | cs.NE | VEGAS (Varying Evolvability-Guided Adaptive Search) is a new methodology
proposed to deal with the neutrality property of some optimization problems. ts
main feature is to consider the whole neutral network rather than an arbitrary
solution. Moreover, VEGAS is designed to escape from plateaus based on the
evolvability ... | computer science |
13,533 | Exploring Promising Stepping Stones by Combining Novelty Search with
Interactive Evolution | cs.NE | The field of evolutionary computation is inspired by the achievements of
natural evolution, in which there is no final objective. Yet the pursuit of
objectives is ubiquitous in simulated evolution. A significant problem is that
objective approaches assume that intermediate stepping stones will increasingly
resemble the... | computer science |
13,534 | Clubs-based Particle Swarm Optimization | cs.NE | This paper introduces a new dynamic neighborhood network for particle swarm
optimization. In the proposed Clubs-based Particle Swarm Optimization (C-PSO)
algorithm, each particle initially joins a default number of what we call
'clubs'. Each particle is affected by its own experience and the experience of
the best perf... | computer science |
13,535 | Distributed Evolutionary Computation: A New Technique for Solving Large
Number of Equations | cs.NE | Evolutionary computation techniques have mostly been used to solve various
optimization and learning problems successfully. Evolutionary algorithm is more
effective to gain optimal solution(s) to solve complex problems than
traditional methods. In case of problems with large set of parameters,
evolutionary computation ... | computer science |
13,536 | A Genetic algorithm to solve the container storage space allocation
problem | cs.NE | This paper presented a genetic algorithm (GA) to solve the container storage
problem in the port. This problem is studied with different container types
such as regular, open side, open top, tank, empty and refrigerated containers.
The objective of this problem is to determine an optimal containers
arrangement, which r... | computer science |
13,537 | A Generalized Hybrid Real-Coded Quantum Evolutionary Algorithm Based on
Particle Swarm Theory with Arithmetic Crossover | cs.NE | This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary
Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial
optimization. The main idea of HRCQEA is to devise a new technique for mutation
and crossover operators. Using the evolutionary equation of PSO a
Single-Multiple gene Mut... | computer science |
13,538 | Design and Development of Artificial Neural Networking (ANN) system
using sigmoid activation function to predict annual rice production in
Tamilnadu | cs.NE | Prediction of annual rice production in all the 31 districts of Tamilnadu is
an important decision for the Government of Tamilnadu. Rice production is a
complex process and non linear problem involving soil, crop, weather, pest,
disease, capital, labour and management parameters. ANN software was designed
and developed... | computer science |
13,539 | Expensive Optimisation: A Metaheuristics Perspective | cs.NE | Stochastic, iterative search methods such as Evolutionary Algorithms (EAs)
are proven to be efficient optimizers. However, they require evaluation of the
candidate solutions which may be prohibitively expensive in many real world
optimization problems. Use of approximate models or surrogates is being
explored as a way ... | computer science |
13,540 | Evolutionary Approaches to Expensive Optimisation | cs.NE | Surrogate assisted evolutionary algorithms (EA) are rapidly gaining
popularity where applications of EA in complex real world problem domains are
concerned. Although EAs are powerful global optimizers, finding optimal
solution to complex high dimensional, multimodal problems often require very
expensive fitness functio... | computer science |
13,541 | Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC
Performance | cs.NE | ROC is usually used to analyze the performance of classifiers in data mining.
ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical
ROC curve, and covers potential optima for the given set of classifiers.
Generally, ROC performance maximization could be considered to maximize the
ROCCH, which als... | computer science |
13,542 | Hybrid Evolutionary Computation for Continuous Optimization | cs.NE | Hybrid optimization algorithms have gained popularity as it has become
apparent there cannot be a universal optimization strategy which is globally
more beneficial than any other. Despite their popularity, hybridization
frameworks require more detailed categorization regarding: the nature of the
problem domain, the con... | computer science |
13,543 | Efficient Evolutionary Algorithm for Single-Objective Bilevel
Optimization | cs.NE | Bilevel optimization problems are a class of challenging optimization
problems, which contain two levels of optimization tasks. In these problems,
the optimal solutions to the lower level problem become possible feasible
candidates to the upper level problem. Such a requirement makes the
optimization problem difficult ... | computer science |
13,544 | A hybrid bat algorithm | cs.NE | Swarm intelligence is a very powerful technique to be used for optimization
purposes. In this paper we present a new swarm intelligence algorithm, based on
the bat algorithm. The Bat algorithm is hybridized with differential evolution
strategies. Besides showing very promising results of the standard benchmark
function... | computer science |
13,545 | Understanding Evolutionary Potential in Virtual CPU Instruction Set
Architectures | cs.NE | We investigate fundamental decisions in the design of instruction set
architectures for linear genetic programs that are used as both model systems
in evolutionary biology and underlying solution representations in evolutionary
computation. We subjected digital organisms with each tested architecture to
seven different... | computer science |
13,546 | Modeling Based on Elman Wavelet Neural Network for Class-D Power
Amplifiers | cs.NE | In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate
with the input signal, manifesting into power-supply induced intermodulation
distortion (PS-IMD) and due to the memory effects of the system, there exist
asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on
the Elman ... | computer science |
13,547 | Multiplicative Approximations, Optimal Hypervolume Distributions, and
the Choice of the Reference Point | cs.NE | Many optimization problems arising in applications have to consider several
objective functions at the same time. Evolutionary algorithms seem to be a very
natural choice for dealing with multi-objective problems as the population of
such an algorithm can be used to represent the trade-offs with respect to the
given ob... | computer science |
13,548 | Implementation of a language driven Backpropagation algorithm | cs.NE | Inspired by the importance of both communication and feedback on errors in
human learning, our main goal was to implement a similar mechanism in
supervised learning of artificial neural networks. The starting point in our
study was the observation that words should accompany the input vectors
included in the training s... | computer science |
13,549 | On the Success Rate of Crossover Operators for Genetic Programming with
Offspring Selection | cs.NE | Genetic programming is a powerful heuristic search technique that is used for
a number of real world applications to solve among others regression,
classification, and time-series forecasting problems. A lot of progress towards
a theoretic description of genetic programming in form of schema theorems has
been made, but... | computer science |
13,550 | Data Mining using Unguided Symbolic Regression on a Blast Furnace
Dataset | cs.NE | In this paper a data mining approach for variable selection and knowledge
extraction from datasets is presented. The approach is based on unguided
symbolic regression (every variable present in the dataset is treated as the
target variable in multiple regression runs) and a novel variable relevance
metric for genetic p... | computer science |
13,551 | A Cooperative Framework for Fireworks Algorithm | cs.NE | This paper presents a cooperative framework for fireworks algorithm (CoFFWA).
A detailed analysis of existing fireworks algorithm (FWA) and its recently
developed variants has revealed that (i) the selection strategy lead to the
contribution of the firework with the best fitness (core firework) for the
optimization ove... | computer science |
13,552 | Some Theoretical Properties of a Network of Discretely Firing Neurons | cs.NE | The problem of optimising a network of discretely firing neurons is
addressed. An objective function is introduced which measures the average
number of bits that are needed for the network to encode its state. When this
is minimised, it is shown that this leads to a number of results, such as
topographic mappings, piec... | computer science |
13,553 | Retaining Experience and Growing Solutions | cs.NE | Generally, when genetic programming (GP) is used for function synthesis any
valuable experience gained by the system is lost from one problem to the next,
even when the problems are closely related. With the aim of developing a system
which retains beneficial experience from problem to problem, this paper
introduces th... | computer science |
13,554 | Optimal Neuron Selection: NK Echo State Networks for Reinforcement
Learning | cs.NE | This paper introduces the NK Echo State Network. The problem of learning in
the NK Echo State Network is reduced to the problem of optimizing a special
form of a Spin Glass Problem known as an NK Landscape. No weight adjustment is
used; all learning is accomplished by spinning up (turning on) or spinning down
(turning ... | computer science |
13,555 | An Online Learning Algorithm for Neuromorphic Hardware Implementation | cs.NE | We propose a sign-based online learning (SOL) algorithm for a neuromorphic
hardware framework called Trainable Analogue Block (TAB). The TAB framework
utilises the principles of neural population coding, implying that it encodes
the input stimulus using a large pool of nonlinear neurons. The SOL algorithm
is a simple w... | computer science |
13,556 | General Riemannian SOM | cs.NE | Kohonen's Self-Organizing Maps (SOMs) have proven to be a successful
data-reduction method to identify the intrinsic lower-dimensional sub-manifold
of a data set that is scattered in the higher-dimensional feature space.
Motivated by the possibly non-Euclidian nature of the feature space and of the
intrinsic geometry o... | computer science |
13,557 | Discontinuous Piecewise Polynomial Neural Networks | cs.NE | An artificial neural network is presented based on the idea of connections
between units that are only active for a specific range of input values and
zero outside that range (and so are not evaluated outside the active range).
The connection function is represented by a polynomial with compact support.
The finite rang... | computer science |
13,558 | Evolving Spiking Networks with Variable Resistive Memories | cs.NE | Neuromorphic computing is a brainlike information processing paradigm that
requires adaptive learning mechanisms. A spiking neuro-evolutionary system is
used for this purpose; plastic resistive memories are implemented as synapses
in spiking neural networks. The evolutionary design process exploits parameter
self-adapt... | computer science |
13,559 | Fractally-organized Connectionist Networks: Conjectures and Preliminary
Results | cs.NE | A strict interpretation of connectionism mandates complex networks of simple
components. The question here is, is this simplicity to be interpreted in
absolute terms? I conjecture that absolute simplicity might not be an essential
attribute of connectionism, and that it may be effectively exchanged with a
requirement f... | computer science |
13,560 | The evolutionary origins of hierarchy | cs.NE | Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limit... | computer science |
13,561 | A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive
Synapses and In-Situ Learning | cs.NE | Nanoscale resistive memories are expected to fuel dense integration of
electronic synapses for large-scale neuromorphic system. To realize such a
brain-inspired computing chip, a compact CMOS spiking neuron that performs
in-situ learning and computing while driving a large number of resistive
synapses is desired. This ... | computer science |
13,562 | pH Prediction by Artificial Neural Networks for the Drinking Water of
the Distribution System of Hyderabad City | cs.NE | In this research, feedforward ANN (Artificial Neural Network) model is
developed and validated for predicting the pH at 10 different locations of the
distribution system of drinking water of Hyderabad city. The developed model is
MLP (Multilayer Perceptron) with back propagation algorithm.The data for the
training and ... | computer science |
13,563 | Channel Equalization Using Multilayer Perceptron Networks | cs.NE | In most digital communication systems, bandwidth limited channel along with
multipath propagation causes ISI (Inter Symbol Interference) to occur. This
phenomenon causes distortion of the given transmitted symbol due to other
transmitted symbols. With the help of equalization ISI can be reduced. This
paper presents a s... | computer science |
13,564 | Information Utilization Ratio in Heuristic Optimization Algorithms | cs.NE | Heuristic algorithms are able to optimize objective functions efficiently
because they use intelligently the information about the objective functions.
Thus, information utilization is critical to the performance of heuristics.
However, the concept of information utilization has remained vague and abstract
because ther... | computer science |
13,565 | Norm-preserving Orthogonal Permutation Linear Unit Activation Functions
(OPLU) | cs.NE | We propose a novel activation function that implements piece-wise orthogonal
non-linear mappings based on permutations. It is straightforward to implement,
and very computationally efficient, also it has little memory requirements. We
tested it on two toy problems for feedforward and recurrent networks, it shows
simila... | computer science |
13,566 | Deep Gate Recurrent Neural Network | cs.NE | This paper introduces two recurrent neural network structures called Simple
Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general
structures for learning long term dependencies. Compared to traditional Long
Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures
require fewer parameters... | computer science |
13,567 | Learning to Generate Genotypes with Neural Networks | cs.NE | Neural networks and evolutionary computation have a rich intertwined history.
They most commonly appear together when an evolutionary algorithm optimises the
parameters and topology of a neural network for reinforcement learning
problems, or when a neural network is applied as a surrogate fitness function
to aid the ev... | computer science |
13,568 | Forecasting Volatility in Indian Stock Market using Artificial Neural
Network with Multiple Inputs and Outputs | cs.NE | Volatility in stock markets has been extensively studied in the applied
finance literature. In this paper, Artificial Neural Network models based on
various back propagation algorithms have been constructed to predict volatility
in the Indian stock market through volatility of NIFTY returns and volatility
of gold retur... | computer science |
13,569 | An Online Structural Plasticity Rule for Generating Better Reservoirs | cs.NE | In this article, a novel neuro-inspired low-resolution online unsupervised
learning rule is proposed to train the reservoir or liquid of Liquid State
Machine. The liquid is a sparsely interconnected huge recurrent network of
spiking neurons. The proposed learning rule is inspired from structural
plasticity and trains t... | computer science |
13,570 | Multi-agent evolutionary systems for the generation of complex virtual
worlds | cs.NE | Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, m... | computer science |
13,571 | Evolutionary Image Transition Based on Theoretical Insights of Random
Processes | cs.NE | Evolutionary algorithms have been widely studied from a theoretical
perspective. In particular, the area of runtime analysis has contributed
significantly to a theoretical understanding and provided insights into the
working behaviour of these algorithms. We study how these insights into
evolutionary processes can be u... | computer science |
13,572 | K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms | cs.NE | There has been a variety of crossover operators proposed for Real-Coded
Genetic Algorithms (RCGAs), which recombine values from the same location in
pairs of strings. In this article we present a recombination operator for RC-
GAs that selects the locations randomly in both parents, and compare it to
mainstream crossov... | computer science |
13,573 | evt_MNIST: A spike based version of traditional MNIST | cs.NE | Benchmarks and datasets have important role in evaluation of machine learning
algorithms and neural network implementations. Traditional dataset for images
such as MNIST is applied to evaluate efficiency of different training
algorithms in neural networks. This demand is different in Spiking Neural
Networks (SNN) as th... | computer science |
13,574 | Memory and Information Processing in Recurrent Neural Networks | cs.NE | Recurrent neural networks (RNN) are simple dynamical systems whose
computational power has been attributed to their short-term memory. Short-term
memory of RNNs has been previously studied analytically only for the case of
orthogonal networks, and only under annealed approximation, and uncorrelated
input. Here for the ... | computer science |
13,575 | Influence of Topological Features on Spatially-Structured Evolutionary
Algorithms Dynamics | cs.NE | In the last decades, complex networks theory significantly influenced other
disciplines on the modeling of both static and dynamic aspects of systems
observed in nature. This work aims to investigate the effects of networks'
topological features on the dynamics of an evolutionary algorithm, considering
in particular th... | computer science |
13,576 | A Polynomial Time Approximation Scheme for a Single Machine Scheduling
Problem Using a Hybrid Evolutionary Algorithm | cs.NE | Nowadays hybrid evolutionary algorithms, i.e, heuristic search algorithms
combining several mutation operators some of which are meant to implement
stochastically a well known technique designed for the specific problem in
question while some others playing the role of random search, have become
rather popular for tack... | computer science |
13,577 | Theoretical foundation for CMA-ES from information geometric perspective | cs.NE | This paper explores the theoretical basis of the covariance matrix adaptation
evolution strategy (CMA-ES) from the information geometry viewpoint.
To establish a theoretical foundation for the CMA-ES, we focus on a geometric
structure of a Riemannian manifold of probability distributions equipped with
the Fisher metr... | computer science |
13,578 | Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012
noisy testbed | cs.NE | In this paper, we study the performance of IPOP-saACM-ES, recently proposed
self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution
Strategy. The algorithm was tested using restarts till a total number of
function evaluations of $10^6D$ was reached, where $D$ is the dimension of the
function search spac... | computer science |
13,579 | A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring | cs.NE | The Artificial Bee Colony (ABC) is the name of an optimization algorithm that
was inspired by the intelligent behavior of a honey bee swarm. It is widely
recognized as a quick, reliable, and efficient methods for solving optimization
problems. This paper proposes a hybrid ABC (HABC) algorithm for graph
3-coloring, whic... | computer science |
13,580 | On applying Neuro - Computing in E-com Domain | cs.NE | Prior studies have generally suggested that Artificial Neural Networks (ANNs)
are superior to conventional statistical models in predicting consumer buying
behavior. There are, however, contradicting findings which raise question over
usefulness of ANNs. This paper discusses development of three neural networks
for mod... | computer science |
13,581 | A Connectionist Network Approach to Find Numerical Solutions of
Diophantine Equations | cs.NE | The paper introduces a connectionist network approach to find numerical
solutions of Diophantine equations as an attempt to address the famous
Hilbert's tenth problem. The proposed methodology uses a three layer feed
forward neural network with back propagation as sequential learning procedure
to find numerical solutio... | computer science |
13,582 | General Upper Bounds on the Running Time of Parallel Evolutionary
Algorithms | cs.NE | We present a new method for analyzing the running time of parallel
evolutionary algorithms with spatially structured populations. Based on the
fitness-level method, it yields upper bounds on the expected parallel running
time. This allows to rigorously estimate the speedup gained by parallelization.
Tailored results ar... | computer science |
13,583 | Speeding up the construction of slow adaptive walks | cs.NE | An algorithm (bliss) is proposed to speed up the construction of slow
adaptive walks. Slow adaptive walks are adaptive walks biased towards closer
points or smaller move steps. They were previously introduced to explore a
search space, e.g. to detect potential local optima or to assess the ruggedness
of a fitness lands... | computer science |
13,584 | Optimization of Real, Hermitian Quadratic Forms: Real, Complex
Hopfield-Amari Neural Network | cs.NE | In this research paper, the problem of optimization of quadratic forms
associated with the dynamics of Hopfield-Amari neural network is considered. An
elegant (and short) proof of the states at which local/global minima of
quadratic form are attained is provided. A theorem associated with local/global
minimization of q... | computer science |
13,585 | Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES
on the BBOB-2012 noiseless testbed | cs.NE | In this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM-ES,
recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation
Evolution Strategies. Both algorithms were tested using restarts till a total
number of function evaluations of $10^6D$ was reached, where $D$ is the
dimension of ... | computer science |
13,586 | Artificial Neural Network for Performance Modeling and Optimization of
CMOS Analog Circuits | cs.NE | This paper presents an implementation of multilayer feed forward neural
networks (NN) to optimize CMOS analog circuits. For modeling and design
recently neural network computational modules have got acceptance as an
unorthodox and useful tool. To achieve high performance of active or passive
circuit component neural ne... | computer science |
13,587 | Evaluation of Particle Swarm Optimization Algorithms for Weighted
Max-Sat Problem: Technical Report | cs.NE | An experimental evaluation is conducted to asses the performance of 4
different Particle Swarm Optimization neighborhood structures in solving
Max-Sat problem. The experiment has shown that none of the algorithms achieves
statistically significant performance over the others under confidence level of
0.05. | computer science |
13,588 | Hybrid Optimized Back propagation Learning Algorithm For Multi-layer
Perceptron | cs.NE | Standard neural network based on general back propagation learning using
delta method or gradient descent method has some great faults like poor
optimization of error-weight objective function, low learning rate, instability
.This paper introduces a hybrid supervised back propagation learning algorithm
which uses trust... | computer science |
13,589 | A genetic algorithm applied to the validation of building thermal models | cs.NE | This paper presents the coupling of a building thermal simulation code with
genetic algorithms (GAs). GAs are randomized search algorithms that are based
on the mechanisms of natural selection and genetics. We show that this coupling
allows the location of defective sub-models of a building thermal model i.e.
parts of ... | computer science |
13,590 | A New Constructive Method to Optimize Neural Network Architecture and
Generalization | cs.NE | In this paper, after analyzing the reasons of poor generalization and
overfitting in neural networks, we consider some noise data as a singular value
of a continuous function - jump discontinuity point. The continuous part can be
approximated with the simplest neural networks, which have good generalization
performance... | computer science |
13,591 | Comparison of Ant-Inspired Gatherer Allocation Approaches using
Memristor-Based Environmental Models | cs.NE | Memristors are used to compare three gathering techniques in an
already-mapped environment where resource locations are known. The All Site
model, which apportions gatherers based on the modeled memristance of that
path, proves to be good at increasing overall efficiency and decreasing time to
fully deplete an environm... | computer science |
13,592 | A Non-Binary Associative Memory with Exponential Pattern Retrieval
Capacity and Iterative Learning: Extended Results | cs.NE | We consider the problem of neural association for a network of non-binary
neurons. Here, the task is to first memorize a set of patterns using a network
of neurons whose states assume values from a finite number of integer levels.
Later, the same network should be able to recall previously memorized patterns
from their... | computer science |
13,593 | An analysis of NK and generalized NK landscapes | cs.NE | Simulated landscapes have been used for decades to evaluate search strategies
whose goal is to find the landscape location with maximum fitness. Applications
include modeling the capacity of enzymes to catalyze reactions and the clinical
effectiveness of medical treatments. Understanding properties of landscapes is
imp... | computer science |
13,594 | Estimating Sectoral Pollution Load in Lagos, Nigeria Using Data Mining
Techniques | cs.NE | Industrial pollution is often considered to be one of the prime factors
contributing to air, water and soil pollution. Sectoral pollution loads
(ton/yr) into different media (i.e. air, water and land) in Lagos were
estimated using Industrial Pollution Projected System (IPPS). These were
further studied using Artificial... | computer science |
13,595 | Segmentation of Alzheimers Disease in PET scan datasets using MATLAB | cs.NE | Positron Emission Tomography (PET) scan images are one of the bio medical
imaging techniques similar to that of MRI scan images but PET scan images are
helpful in finding the development of tumors.The PET scan images requires
expertise in the segmentation where clustering plays an important role in the
automation proce... | computer science |
13,596 | PSO based Neural Networks vs. Traditional Statistical Models for
Seasonal Time Series Forecasting | cs.NE | Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecas... | computer science |
13,597 | Polyploidy and Discontinuous Heredity Effect on Evolutionary
Multi-Objective Optimization | cs.NE | This paper examines the effect of mimicking discontinuous heredity caused by
carrying more than one chromosome in some living organisms cells in
Evolutionary Multi-Objective Optimization algorithms. In this representation,
the phenotype may not fully reflect the genotype. By doing so we are mimicking
living organisms i... | computer science |
13,598 | Parameter Identification of Induction Motor Using Modified Particle
Swarm Optimization Algorithm | cs.NE | This paper presents a new technique for induction motor parameter
identification. The proposed technique is based on a simple startup test using
a standard V/F inverter. The recorded startup currents are compared to that
obtained by simulation of an induction motor model. A Modified PSO optimization
is used to find out... | computer science |
13,599 | Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms | cs.NE | The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed... | computer science |
13,600 | Combining Drift Analysis and Generalized Schema Theory to Design
Efficient Hybrid and/or Mixed Strategy EAs | cs.NE | Hybrid and mixed strategy EAs have become rather popular for tackling various
complex and NP-hard optimization problems. While empirical evidence suggests
that such algorithms are successful in practice, rather little theoretical
support for their success is available, not mentioning a solid mathematical
foundation tha... | computer science |
13,601 | Geiringer Theorems: From Population Genetics to Computational
Intelligence, Memory Evolutive Systems and Hebbian Learning | cs.NE | The classical Geiringer theorem addresses the limiting frequency of
occurrence of various alleles after repeated application of crossover. It has
been adopted to the setting of evolutionary algorithms and, a lot more
recently, reinforcement learning and Monte-Carlo tree search methodology to
cope with a rather challeng... | computer science |
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