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3,900 | Specifying nonspecific evidence | cs.AI | In an earlier article [J. Schubert, On nonspecific evidence, Int. J. Intell.
Syst. 8(6), 711-725 (1993)] we established within Dempster-Shafer theory a
criterion function called the metaconflict function. With this criterion we can
partition into subsets a set of several pieces of evidence with propositions
that are we... | computer science |
3,901 | Creating Prototypes for Fast Classification in Dempster-Shafer
Clustering | cs.AI | We develop a classification method for incoming pieces of evidence in
Dempster-Shafer theory. This methodology is based on previous work with
clustering and specification of originally nonspecific evidence. This
methodology is here put in order for fast classification of future incoming
pieces of evidence by comparing ... | computer science |
3,902 | Fast Dempster-Shafer clustering using a neural network structure | cs.AI | In this paper we study a problem within Dempster-Shafer theory where 2**n - 1
pieces of evidence are clustered by a neural structure into n clusters. The
clustering is done by minimizing a metaconflict function. Previously we
developed a method based on iterative optimization. However, for large scale
problems we need ... | computer science |
3,903 | A neural network and iterative optimization hybrid for Dempster-Shafer
clustering | cs.AI | In this paper we extend an earlier result within Dempster-Shafer theory
["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc.
Seventh Int. Conf. Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence
are clustered int... | computer science |
3,904 | Simultaneous Dempster-Shafer clustering and gradual determination of
number of clusters using a neural network structure | cs.AI | In this paper we extend an earlier result within Dempster-Shafer theory
["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc.
Seventh Int. Conf. Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU'98)] where several pieces of evidence were
clustered into a fixed... | computer science |
3,905 | Fast Dempster-Shafer clustering using a neural network structure | cs.AI | In this article we study a problem within Dempster-Shafer theory where 2**n -
1 pieces of evidence are clustered by a neural structure into n clusters. The
clustering is done by minimizing a metaconflict function. Previously we
developed a method based on iterative optimization. However, for large scale
problems we nee... | computer science |
3,906 | Managing Inconsistent Intelligence | cs.AI | In this paper we demonstrate that it is possible to manage intelligence in
constant time as a pre-process to information fusion through a series of
processes dealing with issues such as clustering reports, ranking reports with
respect to importance, extraction of prototypes from clusters and immediate
classification of... | computer science |
3,907 | Dempster-Shafer clustering using Potts spin mean field theory | cs.AI | In this article we investigate a problem within Dempster-Shafer theory where
2**q - 1 pieces of evidence are clustered into q clusters by minimizing a
metaconflict function, or equivalently, by minimizing the sum of weight of
conflict over all clusters. Previously one of us developed a method based on a
Hopfield and Ta... | computer science |
3,908 | Conflict-based Force Aggregation | cs.AI | In this paper we present an application where we put together two methods for
clustering and classification into a force aggregation method. Both methods are
based on conflicts between elements. These methods work with different type of
elements (intelligence reports, vehicles, military units) on different
hierarchical... | computer science |
3,909 | Reliable Force Aggregation Using a Refined Evidence Specification from
Dempster-Shafer Clustering | cs.AI | In this paper we develop methods for selection of templates and use these
templates to recluster an already performed Dempster-Shafer clustering taking
into account intelligence to template fit during the reclustering phase. By
this process the risk of erroneous force aggregation based on some misplace
pieces of eviden... | computer science |
3,910 | Clustering belief functions based on attracting and conflicting
metalevel evidence | cs.AI | In this paper we develop a method for clustering belief functions based on
attracting and conflicting metalevel evidence. Such clustering is done when the
belief functions concern multiple events, and all belief functions are mixed
up. The clustering process is used as the means for separating the belief
functions into... | computer science |
3,911 | Robust Report Level Cluster-to-Track Fusion | cs.AI | In this paper we develop a method for report level tracking based on
Dempster-Shafer clustering using Potts spin neural networks where clusters of
incoming reports are gradually fused into existing tracks, one cluster for each
track. Incoming reports are put into a cluster and continuous reclustering of
older reports i... | computer science |
3,912 | Neural realisation of the SP theory: cell assemblies revisited | cs.AI | This paper describes how the elements of the SP theory (Wolff, 2003a) may be
realised with neural structures and processes. To the extent that this is
successful, the insights that have been achieved in the SP theory - the
integration and simplification of a range of phenomena in perception and
cognition - may be incor... | computer science |
3,913 | Artificial Neural Networks for Beginners | cs.NE | The scope of this teaching package is to make a brief induction to Artificial
Neural Networks (ANNs) for people who have no previous knowledge of them. We
first make a brief introduction to models of networks, for then describing in
general terms ANNs. As an application, we explain the backpropagation
algorithm, since ... | computer science |
3,914 | What Is Working Memory and Mental Imagery? A Robot that Learns to
Perform Mental Computations | cs.AI | This paper goes back to Turing (1936) and treats his machine as a cognitive
model (W,D,B), where W is an "external world" represented by memory device (the
tape divided into squares), and (D,B) is a simple robot that consists of the
sensory-motor devices, D, and the brain, B. The robot's sensory-motor devices
(the "eye... | computer science |
3,915 | Computational complexity and simulation of rare events of Ising spin
glasses | cs.NE | We discuss the computational complexity of random 2D Ising spin glasses,
which represent an interesting class of constraint satisfaction problems for
black box optimization. Two extremal cases are considered: (1) the +/- J spin
glass, and (2) the Gaussian spin glass. We also study a smooth transition
between these two ... | computer science |
3,916 | Parameter-less hierarchical BOA | cs.NE | The parameter-less hierarchical Bayesian optimization algorithm (hBOA)
enables the use of hBOA without the need for tuning parameters for solving each
problem instance. There are three crucial parameters in hBOA: (1) the selection
pressure, (2) the window size for restricted tournaments, and (3) the
population size. Al... | computer science |
3,917 | Speculation on graph computation architectures and computing via
synchronization | cs.NE | A speculative overview of a future topic of research. The paper is a
collection of ideas concerning two related areas:
1) Graph computation machines ("computing with graphs"). This is the class of
models of computation in which the state of the computation is represented as a
graph or network.
2) Arc-based neural n... | computer science |
3,918 | Evolution of a Subsumption Architecture Neurocontroller | cs.AI | An approach to robotics called layered evolution and merging features from
the subsumption architecture into evolutionary robotics is presented, and its
advantages are discussed. This approach is used to construct a layered
controller for a simulated robot that learns which light source to approach in
an environment wi... | computer science |
3,919 | Multidimensional data classification with artificial neural networks | cs.NE | Multi-dimensional data classification is an important and challenging problem
in many astro-particle experiments. Neural networks have proved to be versatile
and robust in multi-dimensional data classification. In this article we shall
study the classification of gamma from the hadrons for the MAGIC Experiment.
Two neu... | computer science |
3,920 | Vector Symbolic Architectures answer Jackendoff's challenges for
cognitive neuroscience | cs.NE | Jackendoff (2002) posed four challenges that linguistic combinatoriality and
rules of language present to theories of brain function. The essence of these
problems is the question of how to neurally instantiate the rapid construction
and transformation of the compositional structures that are typically taken to
be the ... | computer science |
3,921 | Web Usage Mining Using Artificial Ant Colony Clustering and Genetic
Programming | cs.AI | The rapid e-commerce growth has made both business community and customers
face a new situation. Due to intense competition on one hand and the customer's
option to choose from several alternatives business community has realized the
necessity of intelligent marketing strategies and relationship management. Web
usage m... | computer science |
3,922 | Swarms on Continuous Data | cs.AI | While being it extremely important, many Exploratory Data Analysis (EDA)
systems have the inhability to perform classification and visualization in a
continuous basis or to self-organize new data-items into the older ones
(evenmore into new labels if necessary), which can be crucial in KDD -
Knowledge Discovery, Retrie... | computer science |
3,923 | The Biological Concept of Neoteny in Evolutionary Colour Image
Segmentation - Simple Experiments in Simple Non-Memetic Genetic Algorithms | cs.AI | Neoteny, also spelled Paedomorphosis, can be defined in biological terms as
the retention by an organism of juvenile or even larval traits into later life.
In some species, all morphological development is retarded; the organism is
juvenilized but sexually mature. Such shifts of reproductive capability would
appear to ... | computer science |
3,924 | Artificial Neoteny in Evolutionary Image Segmentation | cs.AI | Neoteny, also spelled Paedomorphosis, can be defined in biological terms as
the retention by an organism of juvenile or even larval traits into later life.
In some species, all morphological development is retarded; the organism is
juvenilized but sexually mature. Such shifts of reproductive capability would
appear to ... | computer science |
3,925 | Map Segmentation by Colour Cube Genetic K-Mean Clustering | cs.AI | Segmentation of a colour image composed of different kinds of texture regions
can be a hard problem, namely to compute for an exact texture fields and a
decision of the optimum number of segmentation areas in an image when it
contains similar and/or unstationary texture fields. In this work, a method is
described for e... | computer science |
3,926 | Neural network ensembles: Evaluation of aggregation algorithms | cs.AI | Ensembles of artificial neural networks show improved generalization
capabilities that outperform those of single networks. However, for aggregation
to be effective, the individual networks must be as accurate and diverse as
possible. An important problem is, then, how to tune the aggregate members in
order to have an ... | computer science |
3,927 | Population Sizing for Genetic Programming Based Upon Decision Making | cs.AI | This paper derives a population sizing relationship for genetic programming
(GP). Following the population-sizing derivation for genetic algorithms in
Goldberg, Deb, and Clark (1992), it considers building block decision making as
a key facet. The analysis yields a GP-unique relationship because it has to
account for b... | computer science |
3,928 | Oiling the Wheels of Change: The Role of Adaptive Automatic Problem
Decomposition in Non--Stationary Environments | cs.NE | Genetic algorithms (GAs) that solve hard problems quickly, reliably and
accurately are called competent GAs. When the fitness landscape of a problem
changes overtime, the problem is called non--stationary, dynamic or
time--variant problem. This paper investigates the use of competent GAs for
optimizing non--stationary ... | computer science |
3,929 | Sub-Structural Niching in Non-Stationary Environments | cs.NE | Niching enables a genetic algorithm (GA) to maintain diversity in a
population. It is particularly useful when the problem has multiple optima
where the aim is to find all or as many as possible of these optima. When the
fitness landscape of a problem changes overtime, the problem is called
non--stationary, dynamic or ... | computer science |
3,930 | Sub-structural Niching in Estimation of Distribution Algorithms | cs.NE | We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomp... | computer science |
3,931 | Scalability of Genetic Programming and Probabilistic Incremental Program
Evolution | cs.NE | This paper discusses scalability of standard genetic programming (GP) and the
probabilistic incremental program evolution (PIPE). To investigate the need for
both effective mixing and linkage learning, two test problems are considered:
ORDER problem, which is rather easy for any recombination-based GP, and TRAP or
the ... | computer science |
3,932 | Multiobjective hBOA, Clustering, and Scalability | cs.NE | This paper describes a scalable algorithm for solving multiobjective
decomposable problems by combining the hierarchical Bayesian optimization
algorithm (hBOA) with the nondominated sorting genetic algorithm (NSGA-II) and
clustering in the objective space. It is first argued that for good
scalability, clustering or som... | computer science |
3,933 | Decomposable Problems, Niching, and Scalability of Multiobjective
Estimation of Distribution Algorithms | cs.NE | The paper analyzes the scalability of multiobjective estimation of
distribution algorithms (MOEDAs) on a class of boundedly-difficult
additively-separable multiobjective optimization problems. The paper
illustrates that even if the linkage is correctly identified, massive
multimodality of the search problems can easily... | computer science |
3,934 | Property analysis of symmetric travelling salesman problem instances
acquired through evolution | cs.NE | We show how an evolutionary algorithm can successfully be used to evolve a
set of difficult to solve symmetric travelling salesman problem instances for
two variants of the Lin-Kernighan algorithm. Then we analyse the instances in
those sets to guide us towards deferring general knowledge about the efficiency
of the tw... | computer science |
3,935 | Fitness Uniform Deletion: A Simple Way to Preserve Diversity | cs.NE | A commonly experienced problem with population based optimisation methods is
the gradual decline in population diversity that tends to occur over time. This
can slow a system's progress or even halt it completely if the population
converges on a local optimum from which it cannot escape. In this paper we
present the Fi... | computer science |
3,936 | Learning Polynomial Networks for Classification of Clinical
Electroencephalograms | cs.AI | We describe a polynomial network technique developed for learning to classify
clinical electroencephalograms (EEGs) presented by noisy features. Using an
evolutionary strategy implemented within Group Method of Data Handling, we
learn classification models which are comprehensively described by sets of
short-term polyn... | computer science |
3,937 | A Learning Algorithm for Evolving Cascade Neural Networks | cs.NE | A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is
described. An ECNN starts to learn with one input node and then adding new
inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly
minimal number of input and hidden neurons as well as connections. The
algorithm was successf... | computer science |
3,938 | Self-Organizing Multilayered Neural Networks of Optimal Complexity | cs.NE | The principles of self-organizing the neural networks of optimal complexity
is considered under the unrepresentative learning set. The method of
self-organizing the multi-layered neural networks is offered and used to train
the logical neural networks which were applied to the medical diagnostics. | computer science |
3,939 | Diagnostic Rule Extraction Using Neural Networks | cs.NE | The neural networks have trained on incomplete sets that a doctor could
collect. Trained neural networks have correctly classified all the presented
instances. The number of intervals entered for encoding the quantitative
variables is equal two. The number of features as well as the number of neurons
and layers in trai... | computer science |
3,940 | Polynomial Neural Networks Learnt to Classify EEG Signals | cs.NE | A neural network based technique is presented, which is able to successfully
extract polynomial classification rules from labeled electroencephalogram (EEG)
signals. To represent the classification rules in an analytical form, we use
the polynomial neural networks trained by a modified Group Method of Data
Handling (GM... | computer science |
3,941 | A Neural Network Decision Tree for Learning Concepts from EEG Data | cs.NE | To learn the multi-class conceptions from the electroencephalogram (EEG) data
we developed a neural network decision tree (DT), that performs the linear
tests, and a new training algorithm. We found that the known methods fail
inducting the classification models when the data are presented by the features
some of them ... | computer science |
3,942 | An Evolving Cascade Neural Network Technique for Cleaning Sleep
Electroencephalograms | cs.NE | Evolving Cascade Neural Networks (ECNNs) and a new training algorithm capable
of selecting informative features are described. The ECNN initially learns with
one input node and then evolves by adding new inputs as well as new hidden
neurons. The resultant ECNN has a near minimal number of hidden neurons and
inputs. The... | computer science |
3,943 | Self-Organization of the Neuron Collective of Optimal Complexity | cs.NE | The optimal complexity of neural networks is achieved when the
self-organization principles is used to eliminate the contradictions existing
in accordance with the K. Godel theorem about incompleteness of the systems
based on axiomatics. The principle of S. Beer exterior addition the Heuristic
Group Method of Data Hand... | computer science |
3,944 | On Self-Regulated Swarms, Societal Memory, Speed and Dynamics | cs.NE | We propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the
advantageous characteristics of Swarm Intelligence as the emergence of a
societal environmental memory or cognitive map via collective pheromone laying
in the landscape (properly balancing the exploration/exploitation nature of our
dynamic search st... | computer science |
3,945 | Evolving Stochastic Learning Algorithm Based on Tsallis Entropic Index | cs.NE | In this paper, inspired from our previous algorithm, which was based on the
theory of Tsallis statistical mechanics, we develop a new evolving stochastic
learning algorithm for neural networks. The new algorithm combines
deterministic and stochastic search steps by employing a different adaptive
stepsize for each netwo... | computer science |
3,946 | "Going back to our roots": second generation biocomputing | cs.AI | Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that ... | computer science |
3,947 | Instantaneously Trained Neural Networks | cs.NE | This paper presents a review of instantaneously trained neural networks
(ITNNs). These networks trade learning time for size and, in the basic model, a
new hidden node is created for each training sample. Various versions of the
corner-classification family of ITNNs, which have found applications in
artificial intellig... | computer science |
3,948 | A Study on the Global Convergence Time Complexity of Estimation of
Distribution Algorithms | cs.AI | The Estimation of Distribution Algorithm is a new class of population based
search methods in that a probabilistic model of individuals is estimated based
on the high quality individuals and used to generate the new individuals. In
this paper we compute 1) some upper bounds on the number of iterations required
for glob... | computer science |
3,949 | May We Have Your Attention: Analysis of a Selective Attention Task | cs.NE | In this paper we present a deeper analysis than has previously been carried
out of a selective attention problem, and the evolution of continuous-time
recurrent neural networks to solve it. We show that the task has a rich
structure, and agents must solve a variety of subproblems to perform well. We
consider the relati... | computer science |
3,950 | Searching for Globally Optimal Functional Forms for Inter-Atomic
Potentials Using Parallel Tempering and Genetic Programming | cs.NE | We develop a Genetic Programming-based methodology that enables discovery of
novel functional forms for classical inter-atomic force-fields, used in
molecular dynamics simulations. Unlike previous efforts in the field, that fit
only the parameters to the fixed functional forms, we instead use a novel
algorithm to searc... | computer science |
3,951 | An associative memory for the on-line recognition and prediction of
temporal sequences | cs.NE | This paper presents the design of an associative memory with feedback that is
capable of on-line temporal sequence learning. A framework for on-line sequence
learning has been proposed, and different sequence learning models have been
analysed according to this framework. The network model is an associative
memory with... | computer science |
3,952 | Evolutionary Optimization in an Algorithmic Setting | cs.NE | Evolutionary processes proved very useful for solving optimization problems.
In this work, we build a formalization of the notion of cooperation and
competition of multiple systems working toward a common optimization goal of
the population using evolutionary computation techniques. It is justified that
evolutionary al... | computer science |
3,953 | Learning and discrimination through STDP in a top-down modulated
associative memory | cs.NE | This article underlines the learning and discrimination capabilities of a
model of associative memory based on artificial networks of spiking neurons.
Inspired from neuropsychology and neurobiology, the model implements top-down
modulations, as in neocortical layer V pyramidal neurons, with a learning rule
based on syn... | computer science |
3,954 | On the Benefits of Inoculation, an Example in Train Scheduling | cs.AI | The local reconstruction of a railway schedule following a small perturbation
of the traffic, seeking minimization of the total accumulated delay, is a very
difficult and tightly constrained combinatorial problem. Notoriously enough,
the railway company's public image degrades proportionally to the amount of
daily dela... | computer science |
3,955 | Overcoming Hierarchical Difficulty by Hill-Climbing the Building Block
Structure | cs.NE | The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are
well-suited for hierarchical problems, where efficient solving requires proper
problem decomposition and assembly of solution from sub-solution with strong
non-linear interdependencies. The paper proposes a hill-climber operating over
the building... | computer science |
3,956 | Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings | cs.AI | The work proposes the application of fuzzy set theory (FST) to diagnose the
condition of high voltage bushings. The diagnosis uses dissolved gas analysis
(DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil
impregnated paper (OIP) bushings. FST and neural networks are compared in terms
of accura... | computer science |
3,957 | A Study in a Hybrid Centralised-Swarm Agent Community | cs.NE | This paper describes a systems architecture for a hybrid Centralised/Swarm
based multi-agent system. The issue of local goal assignment for agents is
investigated through the use of a global agent which teaches the agents
responses to given situations. We implement a test problem in the form of a
Pursuit game, where th... | computer science |
3,958 | Condition Monitoring of HV Bushings in the Presence of Missing Data
Using Evolutionary Computing | cs.NE | The work proposes the application of neural networks with particle swarm
optimisation (PSO) and genetic algorithms (GA) to compensate for missing data
in classifying high voltage bushings. The classification is done using DGA data
from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates
methods for oi... | computer science |
3,959 | On complexity of optimized crossover for binary representations | cs.NE | We consider the computational complexity of producing the best possible
offspring in a crossover, given two solutions of the parents. The crossover
operators are studied on the class of Boolean linear programming problems,
where the Boolean vector of variables is used as the solution representation.
By means of efficie... | computer science |
3,960 | Analysis of Estimation of Distribution Algorithms and Genetic Algorithms
on NK Landscapes | cs.NE | This study analyzes performance of several genetic and evolutionary
algorithms on randomly generated NK fitness landscapes with various values of n
and k. A large number of NK problem instances are first generated for each n
and k, and the global optimum of each instance is obtained using the
branch-and-bound algorithm... | computer science |
3,961 | iBOA: The Incremental Bayesian Optimization Algorithm | cs.NE | This paper proposes the incremental Bayesian optimization algorithm (iBOA),
which modifies standard BOA by removing the population of solutions and using
incremental updates of the Bayesian network. iBOA is shown to be able to learn
and exploit unrestricted Bayesian networks using incremental techniques for
updating bo... | computer science |
3,962 | On the Effects of Idiotypic Interactions for Recommendation Communities
in Artificial Immune Systems | cs.NE | It has previously been shown that a recommender based on immune system
idiotypic principles can out perform one based on correlation alone. This paper
reports the results of work in progress, where we undertake some investigations
into the nature of this beneficial effect. The initial findings are that the
immune syste... | computer science |
3,963 | A Recommender System based on the Immune Network | cs.NE | The immune system is a complex biological system with a highly distributed,
adaptive and self-organising nature. This paper presents an artificial immune
system (AIS) that exploits some of these characteristics and is applied to the
task of film recommendation by collaborative filtering (CF). Natural evolution
and in p... | computer science |
3,964 | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary
Algorithm | cs.NE | This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
search a larger sear... | computer science |
3,965 | Movie Recommendation Systems Using An Artificial Immune System | cs.NE | We apply the Artificial Immune System (AIS) technology to the Collaborative
Filtering (CF) technology when we build the movie recommendation system. Two
different affinity measure algorithms of AIS, Kendall tau and Weighted Kappa,
are used to calculate the correlation coefficients for this movie
recommendation system. ... | computer science |
3,966 | Artificial Immune Systems (AIS) - A New Paradigm for Heuristic Decision
Making | cs.NE | Over the last few years, more and more heuristic decision making techniques
have been inspired by nature, e.g. evolutionary algorithms, ant colony
optimisation and simulated annealing. More recently, a novel computational
intelligence technique inspired by immunology has emerged, called Artificial
Immune Systems (AIS).... | computer science |
3,967 | Permeability Analysis based on information granulation theory | cs.NE | This paper describes application of information granulation theory, on the
analysis of "lugeon data". In this manner, using a combining of Self Organizing
Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are
obtained. Balancing of crisp granules and sub- fuzzy granules, within non fuzzy
infor... | computer science |
3,968 | Graphical Estimation of Permeability Using RST&NFIS | cs.NE | This paper pursues some applications of Rough Set Theory (RST) and
neural-fuzzy model to analysis of "lugeon data". In the manner, using Self
Organizing Map (SOM) as a pre-processing the data are scaled and then the
dominant rules by RST, are elicited. Based on these rules variations of
permeability in the different le... | computer science |
3,969 | An Artificial Immune System as a Recommender System for Web Sites | cs.NE | Artificial Immune Systems have been used successfully to build recommender
systems for film databases. In this research, an attempt is made to extend this
idea to web site recommendation. A collection of more than 1000 individuals web
profiles (alternatively called preferences / favourites / bookmarks file) will
be use... | computer science |
3,970 | Explicit Learning: an Effort towards Human Scheduling Algorithms | cs.NE | Scheduling problems are generally NP-hard combinatorial problems, and a lot
of research has been done to solve these problems heuristically. However, most
of the previous approaches are problem-specific and research into the
development of a general scheduling algorithm is still in its infancy.
Mimicking the natural ... | computer science |
3,971 | Contact state analysis using NFIS and SOM | cs.NE | This paper reports application of neuro- fuzzy inference system (NFIS) and
self organizing feature map neural networks (SOM) on detection of contact state
in a block system. In this manner, on a simple system, the evolution of contact
states, by parallelization of DDA, has been investigated. So, a comparison
between NF... | computer science |
3,972 | Cognitive Architecture for Direction of Attention Founded on Subliminal
Memory Searches, Pseudorandom and Nonstop | cs.AI | By way of explaining how a brain works logically, human associative memory is
modeled with logical and memory neurons, corresponding to standard digital
circuits. The resulting cognitive architecture incorporates basic psychological
elements such as short term and long term memory. Novel to the architecture are
memory ... | computer science |
3,973 | An Evolutionary-Based Approach to Learning Multiple Decision Models from
Underrepresented Data | cs.AI | The use of multiple Decision Models (DMs) enables to enhance the accuracy in
decisions and at the same time allows users to evaluate the confidence in
decision making. In this paper we explore the ability of multiple DMs to learn
from a small amount of verified data. This becomes important when data samples
are difficu... | computer science |
3,974 | Distributed Constrained Optimization with Semicoordinate Transformations | cs.NE | Recent work has shown how information theory extends conventional
full-rationality game theory to allow bounded rational agents. The associated
mathematical framework can be used to solve constrained optimization problems.
This is done by translating the problem into an iterated game, where each agent
controls a differ... | computer science |
3,975 | Design of a P System based Artificial Graph Chemistry | cs.NE | Artificial Chemistries (ACs) are symbolic chemical metaphors for the
exploration of Artificial Life, with specific focus on the origin of life. In
this work we define a P system based artificial graph chemistry to understand
the principles leading to the evolution of life-like structures in an AC set up
and to develop ... | computer science |
3,976 | On the Optimal Convergence Probability of Univariate Estimation of
Distribution Algorithms | cs.NE | In this paper, we obtain bounds on the probability of convergence to the
optimal solution for the compact Genetic Algorithm (cGA) and the Population
Based Incremental Learning (PBIL). We also give a sufficient condition for
convergence of these algorithms to the optimal solution and compute a range of
possible values o... | computer science |
3,977 | A Step Forward in Studying the Compact Genetic Algorithm | cs.NE | The compact Genetic Algorithm (cGA) is an Estimation of Distribution
Algorithm that generates offspring population according to the estimated
probabilistic model of the parent population instead of using traditional
recombination and mutation operators. The cGA only needs a small amount of
memory; therefore, it may be ... | computer science |
3,978 | A nonclassical symbolic theory of working memory, mental computations,
and mental set | cs.AI | The paper tackles four basic questions associated with human brain as a
learning system. How can the brain learn to (1) mentally simulate different
external memory aids, (2) perform, in principle, any mental computations using
imaginary memory aids, (3) recall the real sensory and motor events and
synthesize a combinat... | computer science |
3,979 | Improvements of real coded genetic algorithms based on differential
operators preventing premature convergence | cs.NE | This paper presents several types of evolutionary algorithms (EAs) used for
global optimization on real domains. The interest has been focused on
multimodal problems, where the difficulties of a premature convergence usually
occurs. First the standard genetic algorithm (SGA) using binary encoding of
real values and its... | computer science |
3,980 | A competitive comparison of different types of evolutionary algorithms | cs.NE | This paper presents comparison of several stochastic optimization algorithms
developed by authors in their previous works for the solution of some problems
arising in Civil Engineering. The introduced optimization methods are: the
integer augmented simulated annealing (IASA), the real-coded augmented
simulated annealin... | computer science |
3,981 | Back analysis of microplane model parameters using soft computing
methods | cs.NE | A new procedure based on layered feed-forward neural networks for the
microplane material model parameters identification is proposed in the present
paper. Novelties are usage of the Latin Hypercube Sampling method for the
generation of training sets, a systematic employment of stochastic sensitivity
analysis and a gen... | computer science |
3,982 | Symbolic Computing with Incremental Mindmaps to Manage and Mine Data
Streams - Some Applications | cs.NE | In our understanding, a mind-map is an adaptive engine that basically works
incrementally on the fundament of existing transactional streams. Generally,
mind-maps consist of symbolic cells that are connected with each other and that
become either stronger or weaker depending on the transactional stream. Based
on the un... | computer science |
3,983 | Adaptive Learning with Binary Neurons | cs.AI | A efficient incremental learning algorithm for classification tasks, called
NetLines, well adapted for both binary and real-valued input patterns is
presented. It generates small compact feedforward neural networks with one
hidden layer of binary units and binary output units. A convergence theorem
ensures that solutio... | computer science |
3,984 | Feasibility of random basis function approximators for modeling and
control | cs.NE | We discuss the role of random basis function approximators in modeling and
control. We analyze the published work on random basis function approximators
and demonstrate that their favorable error rate of convergence O(1/n) is
guaranteed only with very substantial computational resources. We also discuss
implications of... | computer science |
3,985 | Experiment Study of Entropy Convergence of Ant Colony Optimization | cs.NE | Ant colony optimization (ACO) has been applied to the field of combinatorial
optimization widely. But the study of convergence theory of ACO is rare under
general condition. In this paper, the authors try to find the evidence to prove
that entropy is related to the convergence of ACO, especially to the estimation
of th... | computer science |
3,986 | On the Workings of Genetic Algorithms: The Genoclique Fixing Hypothesis | cs.NE | We recently reported that the simple genetic algorithm (SGA) is capable of
performing a remarkable form of sublinear computation which has a
straightforward connection with the general problem of interacting attributes
in data-mining. In this paper we explain how the SGA can leverage this
computational proficiency to p... | computer science |
3,987 | Do not Choose Representation just Change: An Experimental Study in
States based EA | cs.NE | Our aim in this paper is to analyse the phenotypic effects (evolvability) of
diverse coding conversion operators in an instance of the states based
evolutionary algorithm (SEA). Since the representation of solutions or the
selection of the best encoding during the optimization process has been proved
to be very importa... | computer science |
3,988 | Weak Evolvability Equals Strong Evolvability | cs.AI | An updated version will be uploaded later. | computer science |
3,989 | Another Look at Quantum Neural Computing | cs.NE | The term quantum neural computing indicates a unity in the functioning of the
brain. It assumes that the neural structures perform classical processing and
that the virtual particles associated with the dynamical states of the
structures define the underlying quantum state. We revisit the concept and also
summarize new... | computer science |
3,990 | Neural Networks for Dynamic Shortest Path Routing Problems - A Survey | cs.NE | This paper reviews the overview of the dynamic shortest path routing problem
and the various neural networks to solve it. Different shortest path
optimization problems can be solved by using various neural networks
algorithms. The routing in packet switched multi-hop networks can be described
as a classical combinatori... | computer science |
3,991 | Apply Ant Colony Algorithm to Search All Extreme Points of Function | cs.AI | To find all extreme points of multimodal functions is called extremum
problem, which is a well known difficult issue in optimization fields. Applying
ant colony optimization (ACO) to solve this problem is rarely reported. The
method of applying ACO to solve extremum problem is explored in this paper.
Experiment shows t... | computer science |
3,992 | Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms | cs.NE | The art of solving the Mastermind puzzle was initiated by Donald Knuth and is
already more than 30 years old; despite that, it still receives much attention
in operational research and computer games journals, not to mention the
nature-inspired stochastic algorithm literature. In this paper we try to
suggest a strategy... | computer science |
3,993 | Using CODEQ to Train Feed-forward Neural Networks | cs.NE | CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of
concepts from chaotic search, opposition-based learning, differential evolution
and quantum mechanics. CODEQ has successfully been used to solve different
types of problems (e.g. constrained, integer-programming, engineering) with
excellent r... | computer science |
3,994 | Experimenting with Innate Immunity | cs.AI | In a previous paper the authors argued the case for incorporating ideas from
innate immunity into artificial immune systems (AISs) and presented an outline
for a conceptual framework for such systems. A number of key general properties
observed in the biological innate and adaptive immune systems were highlighted,
and ... | computer science |
3,995 | Nurse Rostering with Genetic Algorithms | cs.AI | In recent years genetic algorithms have emerged as a useful tool for the
heuristic solution of complex discrete optimisation problems. In particular
there has been considerable interest in their use in tackling problems arising
in the areas of scheduling and timetabling. However, the classical genetic
algorithm paradig... | computer science |
3,996 | Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for
Anomoly Detection | cs.AI | Dendritic cells are antigen presenting cells that provide a vital link
between the innate and adaptive immune system. Research into this family of
cells has revealed that they perform the role of coordinating T-cell based
immune responses, both reactive and for generating tolerance. We have derived
an algorithm based o... | computer science |
3,997 | PCA 4 DCA: The Application Of Principal Component Analysis To The
Dendritic Cell Algorithm | cs.AI | As one of the newest members in the field of artificial immune systems (AIS),
the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural
dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training
data, instead domain or expert knowledge is required to predetermine the
mapping between i... | computer science |
3,998 | Oil Price Trackers Inspired by Immune Memory | cs.AI | We outline initial concepts for an immune inspired algorithm to evaluate and
predict oil price time series data. The proposed solution evolves a short term
pool of trackers dynamically, with each member attempting to map trends and
anticipate future price movements. Successful trackers feed into a long term
memory pool... | computer science |
3,999 | STORM - A Novel Information Fusion and Cluster Interpretation Technique | cs.AI | Analysis of data without labels is commonly subject to scrutiny by
unsupervised machine learning techniques. Such techniques provide more
meaningful representations, useful for better understanding of a problem at
hand, than by looking only at the data itself. Although abundant expert
knowledge exists in many areas whe... | computer science |
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