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13,302 | Are complex systems hard to evolve? | cs.NE | Evolutionary complexity is here measured by the number of trials/evaluations
needed for evolving a logical gate in a non-linear medium. Behavioural
complexity of the gates evolved is characterised in terms of cellular automata
behaviour. We speculate that hierarchies of behavioural and evolutionary
complexities are iso... | computer science |
13,303 | Prospective Algorithms for Quantum Evolutionary Computation | cs.NE | This effort examines the intersection of the emerging field of quantum
computing and the more established field of evolutionary computation. The goal
is to understand what benefits quantum computing might offer to computational
intelligence and how computational intelligence paradigms might be implemented
as quantum pr... | computer science |
13,304 | Fast Density Codes for Image Data | cs.NE | Recently, a new method for encoding data sets in the form of "Density Codes"
was proposed in the literature (Courrieu, 2006). This method allows to compare
sets of points belonging to every multidimensional space, and to build shape
spaces invariant to a wide variety of affine and non-affine transformations.
However, t... | computer science |
13,305 | Solving Time of Least Square Systems in Sigma-Pi Unit Networks | cs.NE | The solving of least square systems is a useful operation in
neurocomputational modeling of learning, pattern matching, and pattern
recognition. In these last two cases, the solution must be obtained on-line,
thus the time required to solve a system in a plausible neural architecture is
critical. This paper presents a ... | computer science |
13,306 | Fast Computation of Moore-Penrose Inverse Matrices | cs.NE | Many neural learning algorithms require to solve large least square systems
in order to obtain synaptic weights. Moore-Penrose inverse matrices allow for
solving such systems, even with rank deficiency, and they provide minimum-norm
vectors of synaptic weights, which contribute to the regularization of the
input-output... | computer science |
13,307 | CMA-ES with Two-Point Step-Size Adaptation | cs.NE | We combine a refined version of two-point step-size adaptation with the
covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we
suggest polished formulae for the learning rate of the covariance matrix and
the recombination weights. In contrast to cumulative step-size adaptation or to
the 1/5-th succe... | computer science |
13,308 | Stochastic Optimization Approaches for Solving Sudoku | cs.NE | In this paper the Sudoku problem is solved using stochastic search techniques
and these are: Cultural Genetic Algorithm (CGA), Repulsive Particle Swarm
Optimization (RPSO), Quantum Simulated Annealing (QSA) and the Hybrid method
that combines Genetic Algorithm with Simulated Annealing (HGASA). The results
obtained show... | computer science |
13,309 | Neural network learning of optimal Kalman prediction and control | cs.NE | Although there are many neural network (NN) algorithms for prediction and for
control, and although methods for optimal estimation (including filtering and
prediction) and for optimal control in linear systems were provided by Kalman
in 1960 (with nonlinear extensions since then), there has been, to my
knowledge, no NN... | computer science |
13,310 | Structural Damage Detection Using Randomized Trained Neural Networks | cs.NE | A computationally method on damage detection problems in structures was
conducted using neural networks. The problem that is considered in this works
consists of estimating the existence, location and extent of stiffness
reduction in structure which is indicated by the changes of the structural
static parameters such a... | computer science |
13,311 | A computational approach to the covert and overt deployment of spatial
attention | cs.NE | Popular computational models of visual attention tend to neglect the
influence of saccadic eye movements whereas it has been shown that the primates
perform on average three of them per seconds and that the neural substrate for
the deployment of attention and the execution of an eye movement might
considerably overlap.... | computer science |
13,312 | Hybrid Neural Network Architecture for On-Line Learning | cs.NE | Approaches to machine intelligence based on brain models have stressed the
use of neural networks for generalization. Here we propose the use of a hybrid
neural network architecture that uses two kind of neural networks
simultaneously: (i) a surface learning agent that quickly adapt to new modes of
operation; and, (ii)... | computer science |
13,313 | Using Dissortative Mating Genetic Algorithms to Track the Extrema of
Dynamic Deceptive Functions | cs.NE | Traditional Genetic Algorithms (GAs) mating schemes select individuals for
crossover independently of their genotypic or phenotypic similarities. In
Nature, this behaviour is known as random mating. However, non-random schemes -
in which individuals mate according to their kinship or likeness - are more
common in natur... | computer science |
13,314 | The use of invariant moments in hand-written character recognition | cs.NE | The goal of this paper is to present the implementation of a Radial Basis
Function neural network with built-in knowledge to recognize hand-written
characters. The neural network includes in its architecture gates controlled by
an attraction/repulsion system of coefficients. These coefficients are derived
from a prepro... | computer science |
13,315 | An Immune System Inspired Approach to Automated Program Verification | cs.NE | An immune system inspired Artificial Immune System (AIS) algorithm is
presented, and is used for the purposes of automated program verification.
Relevant immunological concepts are discussed and the field of AIS is briefly
reviewed. It is proposed to use this AIS algorithm for a specific automated
program verification ... | computer science |
13,316 | Still doing evolutionary algorithms with Perl | cs.NE | Algorithm::Evolutionary (A::E from now on) was introduced in 2002, after a
talk in YAPC::EU in Munich. 7 years later, A::E is in its 0.67 version (past
its "number of the beast" 0.666), and has been used extensively, to the point
of being the foundation of much of the (computer) science being done by our
research group... | computer science |
13,317 | Training Process Reduction Based On Potential Weights Linear Analysis To
Accelarate Back Propagation Network | cs.NE | Learning is the important property of Back Propagation Network (BPN) and
finding the suitable weights and thresholds during training in order to improve
training time as well as achieve high accuracy. Currently, data pre-processing
such as dimension reduction input values and pre-training are the contributing
factors i... | computer science |
13,318 | A quantum diffusion network | cs.NE | Wong's diffusion network is a stochastic, zero-input Hopfield network with a
Gibbs stationary distribution over a bounded, connected continuum. Previously,
logarithmic thermal annealing was demonstrated for the diffusion network and
digital versions of it were studied and applied to imaging. Recently, "quantum"
anneale... | computer science |
13,319 | Techniques for Highly Multiobjective Optimisation: Some Nondominated
Points are Better than Others | cs.NE | The research area of evolutionary multiobjective optimization (EMO) is
reaching better understandings of the properties and capabilities of EMO
algorithms, and accumulating much evidence of their worth in practical
scenarios. An urgent emerging issue is that the favoured EMO algorithms scale
poorly when problems have m... | computer science |
13,320 | Location of Single Neuron Memories in a Hebbian Network | cs.NE | This paper reports the results of an experiment on the use of Kak's B-Matrix
approach to spreading activity in a Hebbian neural network. Specifically, it
concentrates on the memory retrieval from single neurons and compares the
performance of the B-Matrix approach to that of the traditional approach. | computer science |
13,321 | Neural-estimator for the surface emission rate of atmospheric gases | cs.NE | The emission rate of minority atmospheric gases is inferred by a new approach
based on neural networks. The neural network applied is the multi-layer
perceptron with backpropagation algorithm for learning. The identification of
these surface fluxes is an inverse problem. A comparison between the new
neural-inversion an... | computer science |
13,322 | NeuralNetwork Based 3D Surface Reconstruction | cs.NE | This paper proposes a novel neural-network-based adaptive hybrid-reflectance
three-dimensional (3-D) surface reconstruction model. The neural network
combines the diffuse and specular components into a hybrid model. The proposed
model considers the characteristics of each point and the variant albedo to
prevent the rec... | computer science |
13,323 | Optimal Design of Fuzzy Based Power System Stabilizer Self Tuned by
Robust Search Algorithm | cs.NE | In the interconnected power system network, instability problems are caused
mainly by the low frequency oscillations of 0.2 to 2.5 Hz. The supplementary
control signal in addition with AVR and high gain excitation systems are
provided by means of Power System Stabilizer (PSS). Conventional power system
stabilizers prov... | computer science |
13,324 | Existence and Global Logarithmic Stability of Impulsive Neural Networks
with Time Delay | cs.NE | The stability and convergence of the neural networks are the fundamental
characteristics in the Hopfield type networks. Since time delay is ubiquitous
in most physical and biological systems, more attention is being made for the
delayed neural networks. The inclusion of time delay into a neural model is
natural due to ... | computer science |
13,325 | Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic Algorithms | cs.NE | This paper describes a new method for the synthesis of planar antenna arrays
using fuzzy genetic algorithms (FGAs) by optimizing phase excitation
coefficients to best meet a desired radiation pattern. We present the
application of a rigorous optimization technique based on fuzzy genetic
algorithms (FGAs), the optimizin... | computer science |
13,326 | Implementation of an Innovative Bio Inspired GA and PSO Algorithm for
Controller design considering Steam GT Dynamics | cs.NE | The Application of Bio Inspired Algorithms to complicated Power System
Stability Problems has recently attracted the researchers in the field of
Artificial Intelligence. Low frequency oscillations after a disturbance in a
Power system, if not sufficiently damped, can drive the system unstable. This
paper provides a sys... | computer science |
13,327 | Implementing Genetic Algorithms on Arduino Micro-Controllers | cs.NE | Since their conception in 1975, Genetic Algorithms have been an extremely
popular approach to find exact or approximate solutions to optimization and
search problems. Over the last years there has been an enhanced interest in the
field with related techniques, such as grammatical evolution, being developed.
Unfortunate... | computer science |
13,328 | Multi Product Inventory Optimization using Uniform Crossover Genetic
Algorithm | cs.NE | Inventory management is considered to be an important field in Supply Chain
Management because the cost of inventories in a supply chain accounts for about
30 percent of the value of the product. The service provided to the customer
eventually gets enhanced once the efficient and effective management of
inventory is ca... | computer science |
13,329 | Efficient Inventory Optimization of Multi Product, Multiple Suppliers
with Lead Time using PSO | cs.NE | With information revolution, increased globalization and competition, supply
chain has become longer and more complicated than ever before. These
developments bring supply chain management to the forefront of the managements
attention. Inventories are very important in a supply chain. The total
investment in inventorie... | computer science |
13,330 | Nature inspired artificial intelligence based adaptive traffic flow
distribution in computer network | cs.NE | Because of the stochastic nature of traffic requirement matrix, it is very
difficult to get the optimal traffic distribution to minimize the delay even
with adaptive routing protocol in a fixed connection network where capacity
already defined for each link. Hence there is a requirement to define such a
method, which c... | computer science |
13,331 | On Analysis and Evaluation of Multi-Sensory Cognitive Learning of a
Mathematical Topic Using Artificial Neural Networks | cs.NE | This piece of research belongs to the field of educational assessment issue
based upon the cognitive multimedia theory. Considering that theory; visual and
auditory material should be presented simultaneously to reinforce the retention
of a mathematical learned topic, a carefully computer-assisted learning (CAL)
module... | computer science |
13,332 | Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard
Walks | cs.NE | This paper extends the analogies employed in the development of
quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard
walks, called QHW. A novel quantum-inspired evolutionary algorithm, called
HQEA, for solving combinatorial optimization problems, is also proposed. The
novelty of HQEA lies in ... | computer science |
13,333 | Neuroevolutionary optimization | cs.NE | This paper presents an application of evolutionary search procedures to
artificial neural networks. Here, we can distinguish among three kinds of
evolution in artificial neural networks, i.e. the evolution of connection
weights, of architectures, and of learning rules. We review each kind of
evolution in detail and ana... | computer science |
13,334 | A Gibbs distribution that learns from GA dynamics | cs.NE | A general procedure of average-case performance evaluation for population
dynamics such as genetic algorithms (GAs) is proposed and its validity is
numerically examined. We introduce a learning algorithm of Gibbs distributions
from training sets which are gene configurations (strings) generated by GA in
order to figure... | computer science |
13,335 | Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks | cs.NE | Mobility prediction allows estimating the stability of paths in a mobile
wireless Ad Hoc networks. Identifying stable paths helps to improve routing by
reducing the overhead and the number of connection interruptions. In this
paper, we introduce a neural network based method for mobility prediction in Ad
Hoc networks. ... | computer science |
13,336 | Artificial Neural Network based Diagnostic Model For Causes of Success
and Failures | cs.NE | In this paper an attempt has been made to identify most important human
resource factors and propose a diagnostic model based on the back-propagation
and connectionist model approaches of artificial neural network (ANN). The
focus of the study is on the mobile -communication industry of India. The ANN
based approach is... | computer science |
13,337 | Improving GPS/INS Integration through Neural Networks | cs.NE | The Global Positioning Systems (GPS) and Inertial Navigation System (INS)
technology have attracted a considerable importance recently because of its
large number of solutions serving both military as well as civilian
applications. This paper aims to develop a more efficient and especially a
faster method for processin... | computer science |
13,338 | Emergence of Complex-Like Cells in a Temporal Product Network with Local
Receptive Fields | cs.NE | We introduce a new neural architecture and an unsupervised algorithm for
learning invariant representations from temporal sequence of images. The system
uses two groups of complex cells whose outputs are combined multiplicatively:
one that represents the content of the image, constrained to be constant over
several con... | computer science |
13,339 | Efficient Discovery of Large Synchronous Events in Neural Spike Streams | cs.NE | We address the problem of finding patterns from multi-neuronal spike trains
that give us insights into the multi-neuronal codes used in the brain and help
us design better brain computer interfaces. We focus on the synchronous firings
of groups of neurons as these have been shown to play a major role in coding
and comm... | computer science |
13,340 | Active Sites model for the B-Matrix Approach | cs.NE | This paper continues on the work of the B-Matrix approach in hebbian learning
proposed by Dr. Kak. It reports the results on methods of improving the memory
retrieval capacity of the hebbian neural network which implements the B-Matrix
approach. Previously, the approach to retrieving the memories from the network
was t... | computer science |
13,341 | Results of Evolution Supervised by Genetic Algorithms | cs.NE | A series of results of evolution supervised by genetic algorithms with
interest to agricultural and horticultural fields are reviewed. New obtained
original results from the use of genetic algorithms on structure-activity
relationships are reported. | computer science |
13,342 | Artificial Neural Networks, Symmetries and Differential Evolution | cs.NE | Neuroevolution is an active and growing research field, especially in times
of increasingly parallel computing architectures. Learning methods for
Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution
is mainly based on Monte-Carlo techniques and belongs to the group of global
search methods, ... | computer science |
13,343 | Performance Analysis of Estimation of Distribution Algorithm and Genetic
Algorithm in Zone Routing Protocol | cs.NE | In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone
Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic
Algorithm (GA). It is an evolutionary approach, and used when the network size
grows and the search space increases. When the destination is outside the zone,
EDA is appl... | computer science |
13,344 | Unary Coding for Neural Network Learning | cs.NE | This paper presents some properties of unary coding of significance for
biological learning and instantaneously trained neural networks. | computer science |
13,345 | A Constructive Algorithm for Feedforward Neural Networks for Medical
Diagnostic Reasoning | cs.NE | This research is to search for alternatives to the resolution of complex
medical diagnosis where human knowledge should be apprehended in a general
fashion. Successful application examples show that human diagnostic
capabilities are significantly worse than the neural diagnostic system. Our
research describes a constru... | computer science |
13,346 | An Algorithm to Extract Rules from Artificial Neural Networks for
Medical Diagnosis Problems | cs.NE | Artificial neural networks (ANNs) have been successfully applied to solve a
variety of classification and function approximation problems. Although ANNs
can generally predict better than decision trees for pattern classification
problems, ANNs are often regarded as black boxes since their predictions cannot
be explaine... | computer science |
13,347 | Extraction of Symbolic Rules from Artificial Neural Networks | cs.NE | Although backpropagation ANNs generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., their predictions cannot be explained as those of decision trees. In many
applications, it is desirable to extract knowledge from trained ANNs for the
users to... | computer science |
13,348 | Medical diagnosis using neural network | cs.NE | This research is to search for alternatives to the resolution of complex
medical diagnosis where human knowledge should be apprehended in a general
fashion. Successful application examples show that human diagnostic
capabilities are significantly worse than the neural diagnostic system. This
paper describes a modified ... | computer science |
13,349 | RGANN: An Efficient Algorithm to Extract Rules from ANNs | cs.NE | This paper describes an efficient rule generation algorithm, called rule
generation from artificial neural networks (RGANN) to generate symbolic rules
from ANNs. Classification rules are sought in many areas from automatic
knowledge acquisition to data mining and ANN rule extraction. This is because
classification rule... | computer science |
13,350 | Extracting Symbolic Rules for Medical Diagnosis Problem | cs.NE | Neural networks (NNs) have been successfully applied to solve a variety of
application problems involving classification and function approximation.
Although backpropagation NNs generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., their predi... | computer science |
13,351 | Pattern Classification using Simplified Neural Networks | cs.NE | In recent years, many neural network models have been proposed for pattern
classification, function approximation and regression problems. This paper
presents an approach for classifying patterns from simplified NNs. Although the
predictive accuracy of ANNs is often higher than that of other methods or human
experts, i... | computer science |
13,352 | Rule Extraction using Artificial Neural Networks | cs.NE | Artificial neural networks have been successfully applied to a variety of
business application problems involving classification and regression. Although
backpropagation neural networks generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., the... | computer science |
13,353 | REx: An Efficient Rule Generator | cs.NE | This paper describes an efficient algorithm REx for generating symbolic rules
from artificial neural network (ANN). Classification rules are sought in many
areas from automatic knowledge acquisition to data mining and ANN rule
extraction. This is because classification rules possess some attractive
features. They are e... | computer science |
13,354 | A Genetic Algorithm for the Multi-Pickup and Delivery Problem with time
windows | cs.NE | In This paper we present a genetic algorithm for the multi-pickup and
delivery problem with time windows (m-PDPTW). The m-PDPTW is an optimization
vehicles routing problem which must meet requests for transport between
suppliers and customers satisfying precedence, capacity and time constraints.
This paper purposes a b... | computer science |
13,355 | Soil Classification Using GATree | cs.NE | This paper details the application of a genetic programming framework for
classification of decision tree of Soil data to classify soil texture. The
database contains measurements of soil profile data. We have applied GATree for
generating classification decision tree. GATree is a decision tree builder that
is based on... | computer science |
13,356 | On the approximation ability of evolutionary optimization with
application to minimum set cover | cs.NE | Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural
evolution. They are often used to obtain satisficing solutions in practice. In
this paper, we investigate a largely underexplored issue: the approximation
performance of EAs in terms of how close the solution obtained is to an optimal
solution. ... | computer science |
13,357 | DXNN Platform: The Shedding of Biological Inefficiencies | cs.NE | This paper introduces a novel type of memetic algorithm based Topology and
Weight Evolving Artificial Neural Network (TWEANN) system called DX Neural
Network (DXNN). DXNN implements a number of interesting features, amongst which
is: a simple and database friendly tuple based encoding method, a 2 phase
neuroevolutionar... | computer science |
13,358 | Faster Black-Box Algorithms Through Higher Arity Operators | cs.NE | We extend the work of Lehre and Witt (GECCO 2010) on the unbiased black-box
model by considering higher arity variation operators. In particular, we show
that already for binary operators the black-box complexity of \leadingones
drops from $\Theta(n^2)$ for unary operators to $O(n \log n)$. For \onemax, the
$\Omega(n \... | computer science |
13,359 | Toward Measuring the Scaling of Genetic Programming | cs.NE | Several genetic programming systems are created, each solving a different
problem. In these systems, the median number of generations G needed to evolve
a working program is measured. The behavior of G is observed as the difficulty
of the problem is increased. In these systems, the density D of working
programs in the ... | computer science |
13,360 | Improving the character recognition efficiency of feed forward BP neural
network | cs.NE | This work is focused on improving the character recognition capability of
feed-forward back-propagation neural network by using one, two and three hidden
layers and the modified additional momentum term. 182 English letters were
collected for this work and the equivalent binary matrix form of these
characters was appli... | computer science |
13,361 | Cost effective approach on feature selection using genetic algorithms
and fuzzy logic for diabetes diagnosis | cs.NE | A way to enhance the performance of a model that combines genetic algorithms
and fuzzy logic for feature selection and classification is proposed. Early
diagnosis of any disease with less cost is preferable. Diabetes is one such
disease. Diabetes has become the fourth leading cause of death in developed
countries and t... | computer science |
13,362 | Memory Retrieval in the B-Matrix Neural Network | cs.NE | This paper is an extension to the memory retrieval procedure of the B-Matrix
approach [6],[17] to neural network learning. The B-Matrix is a part of the
interconnection matrix generated from the Hebbian neural network, and in memory
retrieval, the B-matrix is clamped with a small fragment of the memory. The
fragment gr... | computer science |
13,363 | Design and classification of dynamic multi-objective optimization
problems | cs.NE | In this work we provide a formal model for the different time-dependent
components that can appear in dynamic multi-objective optimization problems,
along with a classification of these components. Four main classes are
identified, corresponding to the influence of the parameters, objective
functions, previous states o... | computer science |
13,364 | Using Variable Threshold to Increase Capacity in a Feedback Neural
Network | cs.NE | The article presents new results on the use of variable thresholds to
increase the capacity of a feedback neural network. Non-binary networks are
also considered in this analysis. | computer science |
13,365 | Computational Complexity Results for Genetic Programming and the Sorting
Problem | cs.NE | Genetic Programming (GP) has found various applications. Understanding this
type of algorithm from a theoretical point of view is a challenging task. The
first results on the computational complexity of GP have been obtained for
problems with isolated program semantics. With this paper, we push forward the
computationa... | computer science |
13,366 | Recalling of Images using Hopfield Neural Network Model | cs.NE | In the present paper, an effort has been made for storing and recalling
images with Hopfield Neural Network Model of auto-associative memory. Images
are stored by calculating a corresponding weight matrix. Thereafter, starting
from an arbitrary configuration, the memory will settle on exactly that stored
image, which i... | computer science |
13,367 | A Novel Crossover Operator for Genetic Algorithms: Ring Crossover | cs.NE | The genetic algorithm (GA) is an optimization and search technique based on
the principles of genetics and natural selection. A GA allows a population
composed of many individuals to evolve under specified selection rules to a
state that maximizes the "fitness" function. In that process, crossover
operator plays an imp... | computer science |
13,368 | Neural network to identify individuals at health risk | cs.NE | The risk of diseases such as heart attack and high blood pressure could be
reduced by adequate physical activity. However, even though majority of general
population claims to perform some physical exercise, only a minority exercises
enough to keep a healthy living style. Thus, physical inactivity has become one
of the... | computer science |
13,369 | Convergence Analysis of Differential Evolution Variants on Unconstrained
Global Optimization Functions | cs.NE | In this paper, we present an empirical study on convergence nature of
Differential Evolution (DE) variants to solve unconstrained global optimization
problems. The aim is to identify the competitive nature of DE variants in
solving the problem at their hand and compare. We have chosen fourteen
benchmark functions group... | computer science |
13,370 | Ant Colony Optimization and Hypergraph Covering Problems | cs.NE | Ant Colony Optimization (ACO) is a very popular metaheuristic for solving
computationally hard combinatorial optimization problems. Runtime analysis of
ACO with respect to various pseudo-boolean functions and different graph based
combinatorial optimization problems has been taken up in recent years. In this
paper, we ... | computer science |
13,371 | The Exact Schema Theorem | cs.NE | A schema is a naturally defined subset of the space of fixed-length binary
strings. The Holland Schema Theorem gives a lower bound on the expected
fraction of a population in a schema after one generation of a simple genetic
algorithm. This paper gives formulas for the exact expected fraction of a
population in a schem... | computer science |
13,372 | Distributed Evolutionary Computation using REST | cs.NE | This paper analises distributed evolutionary computation based on the
Representational State Transfer (REST) protocol, which overlays a farming model
on evolutionary computation. An approach to evolutionary distributed
optimisation of multilayer perceptrons (MLP) using REST and language Perl has
been done. In these exp... | computer science |
13,373 | SOAP vs REST: Comparing a master-slave GA implementation | cs.NE | In this paper, a high-level comparison of both SOAP (Simple Object Access
Protocol) and REST (Representational State Transfer) is made. These are the two
main approaches for interfacing to the web with web services. Both approaches
are different and present some advantages and disadvantages for interfacing to
web servi... | computer science |
13,374 | Finite First Hitting Time versus Stochastic Convergence in Particle
Swarm Optimisation | cs.NE | We reconsider stochastic convergence analyses of particle swarm optimisation,
and point out that previously obtained parameter conditions are not always
sufficient to guarantee mean square convergence to a local optimum. We show
that stagnation can in fact occur for non-trivial configurations in non-optimal
parts of th... | computer science |
13,375 | Evolution of Things | cs.NE | Evolution is one of the major omnipresent powers in the universe that has
been studied for about two centuries. Recent scientific and technical
developments make it possible to make the transition from passively
understanding to actively mastering evolution. As of today, the only area where
human experimenters can desi... | computer science |
13,376 | A Neural Network Model for Construction Projects Site Overhead Cost
Estimating in Egypt | cs.NE | Estimating of the overhead costs of building construction projects is an
important task in the management of these projects. The quality of construction
management depends heavily on their accurate cost estimation. Construction
costs prediction is a very difficult and sophisticated task especially when
using manual cal... | computer science |
13,377 | Using Hopfield to Solve Resource-Leveling Problem | cs.NE | Although the traditional permute matrix coming along with Hopfield is able to
describe many common problems, it seems to have limitation in solving more
complicated problem with more constrains, like resource leveling which is
actually a NP problem. This paper tries to find a better solution for it by
using neural netw... | computer science |
13,378 | A Computational Framework for Nonlinear Dimensionality Reduction of
Large Data Sets: The Exploratory Inspection Machine (XIM) | cs.NE | In this paper, we present a novel computational framework for nonlinear
dimensionality reduction which is specifically suited to process large data
sets: the Exploratory Inspection Machine (XIM). XIM introduces a conceptual
cross-link between hitherto separate domains of machine learning, namely
topographic vector quan... | computer science |
13,379 | Evolutionary Biclustering of Clickstream Data | cs.NE | Biclustering is a two way clustering approach involving simultaneous
clustering along two dimensions of the data matrix. Finding biclusters of web
objects (i.e. web users and web pages) is an emerging topic in the context of
web usage mining. It overcomes the problem associated with traditional
clustering methods by al... | computer science |
13,380 | Why 'GSA: A Gravitational Search Algorithm' Is Not Genuinely Based on
the Law of Gravity | cs.NE | Why 'GSA: A Gravitational Search Algorithm' Is Not Genuinely Based on the Law
of Gravity | computer science |
13,381 | Black-Box Complexities of Combinatorial Problems | cs.NE | Black-box complexity is a complexity theoretic measure for how difficult a
problem is to be optimized by a general purpose optimization algorithm. It is
thus one of the few means trying to understand which problems are tractable for
genetic algorithms and other randomized search heuristics.
Most previous work on blac... | computer science |
13,382 | Evolving A-Type Artificial Neural Networks | cs.NE | We investigate Turing's notion of an A-type artificial neural network. We
study a refinement of Turing's original idea, motivated by work of Teuscher,
Bull, Preen and Copeland. Our A-types can process binary data by accepting and
outputting sequences of binary vectors; hence we can associate a function to an
A-type, an... | computer science |
13,383 | A Novel and Robust Evolution Algorithm for Optimizing Complicated
Functions | cs.NE | In this paper, a novel mutation operator of differential evolution algorithm
is proposed. A new algorithm called divergence differential evolution algorithm
(DDEA) is developed by combining the new mutation operator with divergence
operator and assimilation operator (divergence operator divides population,
and, assimil... | computer science |
13,384 | Convergence Properties of Two (μ + λ) Evolutionary
Algorithms On OneMax and Royal Roads Test Functions | cs.NE | We present a number of bounds on convergence time for two elitist
population-based Evolutionary Algorithms using a recombination operator
k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the
effect of distribution of elite species and population size. | computer science |
13,385 | Convergence of a Recombination-Based Elitist Evolutionary Algorithm on
the Royal Roads Test Function | cs.NE | We present an analysis of the performance of an elitist Evolutionary
algorithm using a recombination operator known as 1-Bit-Swap on the Royal Roads
test function based on a population. We derive complete, approximate and
asymptotic convergence rates for the algorithm. The complete model shows the
benefit of the size o... | computer science |
13,386 | Tight Bounds on the Optimization Time of the (1+1) EA on Linear
Functions | cs.NE | The analysis of randomized search heuristics on classes of functions is
fundamental for the understanding of the underlying stochastic process and the
development of suitable proof techniques. Recently, remarkable progress has
been made in bounding the expected optimization time of the simple (1+1) EA on
the class of l... | computer science |
13,387 | Novel Analysis of Population Scalability in Evolutionary Algorithms | cs.NE | Population-based evolutionary algorithms (EAs) have been widely applied to
solve various optimization problems. The question of how the performance of a
population-based EA depends on the population size arises naturally. The
performance of an EA may be evaluated by different measures, such as the
average convergence r... | computer science |
13,388 | Ant Colony Optimization of Rough Set for HV Bushings Fault Detection | cs.NE | Most transformer failures are attributed to bushings failures. Hence it is
necessary to monitor the condition of bushings. In this paper three methods are
developed to monitor the condition of oil filled bushing. Multi-layer
perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are
developed and combi... | computer science |
13,389 | Artificial Neural Network and Rough Set for HV Bushings Condition
Monitoring | cs.NE | Most transformer failures are attributed to bushings failures. Hence it is
necessary to monitor the condition of bushings. In this paper three methods are
developed to monitor the condition of oil filled bushing. Multi-layer
perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are
developed and combi... | computer science |
13,390 | Using Genetic Algorithm in the Evolutionary Design of Sequential Logic
Circuits | cs.NE | Evolvable hardware (EHW) is a set of techniques that are based on the idea of
combining reconfiguration hardware systems with evolutionary algorithms. In
other word, EHW has two sections; the reconfigurable hardware and evolutionary
algorithm where the configurations are under the control of an evolutionary
algorithm. ... | computer science |
13,391 | Towards Analyzing Crossover Operators in Evolutionary Search via General
Markov Chain Switching Theorem | cs.NE | Evolutionary algorithms (EAs), simulating the evolution process of natural
species, are used to solve optimization problems. Crossover (also called
recombination), originated from simulating the chromosome exchange phenomena in
zoogamy reproduction, is widely employed in EAs to generate offspring
solutions, of which th... | computer science |
13,392 | An efficient implementation of the simulated annealing heuristic for the
quadratic assignment problem | cs.NE | The quadratic assignment problem (QAP) is one of the most difficult
combinatorial optimization problems. One of the most powerful and commonly used
heuristics to obtain approximations to the optimal solution of the QAP is
simulated annealing (SA). We present an efficient implementation of the SA
heuristic which perform... | computer science |
13,393 | Particle Swarm Optimization Framework for Low Power Testing of VLSI
Circuits | cs.NE | Power dissipation in sequential circuits is due to increased toggling count
of Circuit under Test, which depends upon test vectors applied. If successive
test vectors sequences have more toggling nature then it is sure that toggling
rate of flip flops is higher. Higher toggling for flip flops results more power
dissipa... | computer science |
13,394 | Biologically inspired design framework for Robot in Dynamic Environments
using Framsticks | cs.NE | Robot design complexity is increasing day by day especially in automated
industries. In this paper we propose biologically inspired design framework for
robots in dynamic world on the basis of Co-Evolution, Virtual Ecology, Life
time learning which are derived from biological creatures. We have created a
virtual kheper... | computer science |
13,395 | Production System Rules as Protein Complexes from Genetic Regulatory
Networks | cs.NE | This short paper introduces a new way by which to design production system
rules. An indirect encoding scheme is presented which views such rules as
protein complexes produced by the temporal behaviour of an artificial genetic
regulatory network. This initial study begins by using a simple Boolean
regulatory network to... | computer science |
13,396 | Self-Organisation of Evolving Agent Populations in Digital Ecosystems | cs.NE | We investigate the self-organising behaviour of Digital Ecosystems, because a
primary motivation for our research is to exploit the self-organising
properties of biological ecosystems. We extended a definition for the
complexity, grounded in the biological sciences, providing a measure of the
information in an organism... | computer science |
13,397 | Statistical Approach for Selecting Elite Ants | cs.NE | Applications of ACO algorithms to obtain better solutions for combinatorial
optimization problems have become very popular in recent years. In ACO
algorithms, group of agents repeatedly perform well defined actions and
collaborate with other ants in order to accomplish the defined task. In this
paper, we introduce new ... | computer science |
13,398 | Handwritten digit Recognition using Support Vector Machine | cs.NE | Handwritten Numeral recognition plays a vital role in postal automation
services especially in countries like India where multiple languages and
scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural
Network (NN) and HMM are popular methods in handwritten word recognition
system. The hybrid system giv... | computer science |
13,399 | Reducing the Arity in Unbiased Black-Box Complexity | cs.NE | We show that for all $1<k \leq \log n$ the $k$-ary unbiased black-box
complexity of the $n$-dimensional $\onemax$ function class is $O(n/k)$. This
indicates that the power of higher arity operators is much stronger than what
the previous $O(n/\log k)$ bound by Doerr et al. (Faster black-box algorithms
through higher ar... | computer science |
13,400 | Computational Complexity Analysis of Multi-Objective Genetic Programming | cs.NE | The computational complexity analysis of genetic programming (GP) has been
started recently by analyzing simple (1+1) GP algorithms for the problems ORDER
and MAJORITY. In this paper, we study how taking the complexity as an
additional criteria influences the runtime behavior. We consider
generalizations of ORDER and M... | computer science |
13,401 | Hybridizing PSM and RSM Operator for Solving NP-Complete Problems:
Application to Travelling Salesman Problem | cs.NE | In this paper, we present a new mutation operator, Hybrid Mutation (HPRM),
for a genetic algorithm that generates high quality solutions to the Traveling
Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring
from a pair of parents by hybridizing two mutation operators, PSM and RSM. The
efficiency... | computer science |
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