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14,002 | A Hybrid ACO Algorithm for the Next Release Problem | cs.NE | In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO)
for Next Release Problem (NRP). NRP, a NP-hard problem in requirement
engineering, is to balance customer requests, resource constraints, and
requirement dependencies by requirement selection. Inspired by the successes of
Ant Colony Optimizatio... | computer science |
14,003 | A Sport Tournament Scheduling by Genetic Algorithm with Swapping Method | cs.NE | A sport tournament problem is considered the Traveling Tournament Problem
(TTP). One interesting type is the mirrored Traveling Tournament Problem
(mTTP). The objective of the problem is to minimize either the total number of
traveling or the total distances of traveling or both. This research aims to
find an optimized... | computer science |
14,004 | Interval Arithmetic and Interval-Aware Operators for Genetic Programming | cs.NE | Symbolic regression via genetic programming is a flexible approach to machine
learning that does not require up-front specification of model structure.
However, traditional approaches to symbolic regression require the use of
protected operators, which can lead to perverse model characteristics and poor
generalisation.... | computer science |
14,005 | Learning Linear Feature Space Transformations in Symbolic Regression | cs.NE | We propose a new type of leaf node for use in Symbolic Regression (SR) that
performs linear combinations of feature variables (LCF). These nodes can be
handled in three different modes -- an unsynchronized mode, where all LCFs are
free to change on their own, a synchronized mode, where LCFs are sorted into
groups in wh... | computer science |
14,006 | The Emergence of Canalization and Evolvability in an Open-Ended,
Interactive Evolutionary System | cs.NE | Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is develo... | computer science |
14,007 | LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization
Techniques | cs.NE | Optimization techniques play an important role in several scientific and
real-world applications, thus becoming of great interest for the community. As
a consequence, a number of open-source libraries are available in the
literature, which ends up fostering the research and development of new
techniques and application... | computer science |
14,008 | Discovering Evolutionary Stepping Stones through Behavior Domination | cs.NE | Behavior domination is proposed as a tool for understanding and harnessing
the power of evolutionary systems to discover and exploit useful stepping
stones. Novelty search has shown promise in overcoming deception by collecting
diverse stepping stones, and several algorithms have been proposed that combine
novelty with... | computer science |
14,009 | Genetic Algorithm Based Floor Planning System | cs.NE | Genetic Algorithms are widely used in many different optimization problems
including layout design. The layout of the shelves play an important role in
the total sales metrics for superstores since this affects the customers'
shopping behaviour. This paper employed a genetic algorithm based approach to
design shelf lay... | computer science |
14,010 | Tapping the sensorimotor trajectory | cs.NE | In this paper, we propose the concept of sensorimotor tappings, a new
graphical technique that explicitly represents relations between the time steps
of an agent's sensorimotor loop and a single training step of an adaptive
internal model. In the simplest case this is a relation linking two time steps.
In realistic cas... | computer science |
14,011 | Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms | cs.NE | The evolutionary edit distance between two individuals in a population, i.e.,
the amount of applications of any genetic operator it would take the
evolutionary process to generate one individual starting from the other, seems
like a promising estimate for the diversity between said individuals. We
introduce genealogica... | computer science |
14,012 | How to Read Many-Objective Solution Sets in Parallel Coordinates | cs.NE | Rapid development of evolutionary algorithms in handling many-objective
optimization problems requires viable methods of visualizing a high-dimensional
solution set. Parallel coordinates which scale well to high-dimensional data
are such a method, and have been frequently used in evolutionary many-objective
optimizatio... | computer science |
14,013 | Quantified advantage of discontinuous weight selection in approximations
with deep neural networks | cs.NE | We consider approximations of 1D Lipschitz functions by deep ReLU networks of
a fixed width. We prove that without the assumption of continuous weight
selection the uniform approximation error is lower than with this assumption at
least by a factor logarithmic in the size of the network. | computer science |
14,014 | Evolutionary learning of fire fighting strategies | cs.NE | The dynamic problem of enclosing an expanding fire can be modelled by a
discrete variant in a grid graph. While the fire expands to all neighbouring
cells in any time step, the fire fighter is allowed to block $c$ cells in the
average outside the fire in the same time interval. It was shown that the
success of the fire... | computer science |
14,015 | Developing All-Skyrmion Spiking Neural Network | cs.NE | In this work, we have proposed a revolutionary neuromorphic computing
methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN). Such
proposed methodology is based on our finding that skyrmion is a topological
stable spin texture and its spatiotemporal motion along the magnetic nano-track
intuitively interp... | computer science |
14,016 | Making up for the deficit in a marathon run | cs.NE | To predict the final result of an athlete in a marathon run thoroughly is the
eternal desire of each trainer. Usually, the achieved result is weaker than the
predicted one due to the objective (e.g., environmental conditions) as well as
subjective factors (e.g., athlete's malaise). Therefore, making up for the
deficit ... | computer science |
14,017 | Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural
Networks | cs.NE | While Deep Neural Networks (DNNs) push the state-of-the-art in many machine
learning applications, they often require millions of expensive floating-point
operations for each input classification. This computation overhead limits the
applicability of DNNs to low-power, embedded platforms and incurs high cost in
data ce... | computer science |
14,018 | An overview and comparative analysis of Recurrent Neural Networks for
Short Term Load Forecasting | cs.NE | The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to th... | computer science |
14,019 | A natural approach to studying schema processing | cs.NE | The Building Block Hypothesis (BBH) states that adaptive systems combine good
partial solutions (so-called building blocks) to find increasingly better
solutions. It is thought that Genetic Algorithms (GAs) implement the BBH.
However, for GAs building blocks are semi-theoretical objects in that they are
thought only to... | computer science |
14,020 | Ensemble of heterogeneous flexible neural trees using multiobjective
genetic programming | cs.NE | Machine learning algorithms are inherently multiobjective in nature, where
approximation error minimization and model's complexity simplification are two
conflicting objectives. We proposed a multiobjective genetic programming (MOGP)
for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible
feedforwa... | computer science |
14,021 | A Survey of Neuromorphic Computing and Neural Networks in Hardware | cs.NE | Neuromorphic computing has come to refer to a variety of brain-inspired
computers, devices, and models that contrast the pervasive von Neumann computer
architecture. This biologically inspired approach has created highly connected
synthetic neurons and synapses that can be used to model neuroscience theories
as well as... | computer science |
14,022 | Parameter Adaptation and Criticality in Particle Swarm Optimization | cs.NE | Generality is one of the main advantages of heuristic algorithms, as such,
multiple parameters are exposed to the user with the objective of allowing them
to shape the algorithms to their specific needs. Parameter selection,
therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting
good parameter ... | computer science |
14,023 | Diffusion-based neuromodulation can eliminate catastrophic forgetting in
simple neural networks | cs.NE | A long-term goal of AI is to produce agents that can learn a diversity of
skills throughout their lifetimes and continuously improve those skills via
experience. A longstanding obstacle towards that goal is catastrophic
forgetting, which is when learning new information erases previously learned
information. Catastroph... | computer science |
14,024 | Parallel and in-process compilation of individuals for genetic
programming on GPU | cs.NE | Three approaches to implement genetic programming on GPU hardware are
compilation, interpretation and direct generation of machine code. The compiled
approach is known to have a prohibitive overhead compared to other two. This
paper investigates methods to accelerate compilation of individuals for genetic
programming o... | computer science |
14,025 | Block building programming for symbolic regression | cs.NE | Symbolic regression that aims to detect underlying data-driven models has
become increasingly important for industrial data analysis. For most existing
algorithms such as genetic programming (GP), the convergence speed might be too
slow for large-scale problems with a large number of variables. This situation
may becom... | computer science |
14,026 | A divide and conquer method for symbolic regression | cs.NE | Symbolic regression aims to find a function that best explains the
relationship between independent variables and the objective value based on a
given set of sample data. Genetic programming (GP) is usually considered as an
appropriate method for the problem since it can optimize functional structure
and coefficients s... | computer science |
14,027 | Fast-Slow Recurrent Neural Networks | cs.NE | Processing sequential data of variable length is a major challenge in a wide
range of applications, such as speech recognition, language modeling,
generative image modeling and machine translation. Here, we address this
challenge by proposing a novel recurrent neural network (RNN) architecture, the
Fast-Slow RNN (FS-RN... | computer science |
14,028 | Neural Decomposition of Time-Series Data for Effective Generalization | cs.NE | We present a neural network technique for the analysis and extrapolation of
time-series data called Neural Decomposition (ND). Units with a sinusoidal
activation function are used to perform a Fourier-like decomposition of
training samples into a sum of sinusoids, augmented by units with nonperiodic
activation function... | computer science |
14,029 | Integer Echo State Networks: Hyperdimensional Reservoir Computing | cs.NE | We propose an integer approximation of Echo State Networks (ESN) based on the
mathematics of hyperdimensional computing. The reservoir of the proposed
Integer Echo State Network (intESN) contains only n-bits integers and replaces
the recurrent matrix multiply with an efficient cyclic shift operation. Such an
architectu... | computer science |
14,030 | Neuroevolution on the Edge of Chaos | cs.NE | Echo state networks represent a special type of recurrent neural networks.
Recent papers stated that the echo state networks maximize their computational
performance on the transition between order and chaos, the so-called edge of
chaos. This work confirms this statement in a comprehensive set of experiments.
Furthermo... | computer science |
14,031 | Surprise Search for Evolutionary Divergence | cs.NE | Inspired by the notion of surprise for unconventional discovery we introduce
a general search algorithm we name surprise search as a new method of
evolutionary divergent search. Surprise search is grounded in the divergent
search paradigm and is fabricated within the principles of evolutionary search.
The algorithm mim... | computer science |
14,032 | Evolutionary Multitasking for Multiobjective Continuous Optimization:
Benchmark Problems, Performance Metrics and Baseline Results | cs.NE | In this report, we suggest nine test problems for multi-task multi-objective
optimization (MTMOO), each of which consists of two multiobjective optimization
tasks that need to be solved simultaneously. The relationship between tasks
varies between different test problems, which would be helpful to have a
comprehensive ... | computer science |
14,033 | Global Convergence of the (1+1) Evolution Strategy | cs.NE | We establish global convergence of the (1+1)-ES algorithm, i.e., convergence
to a critical point independent of the initial state.
The analysis is based on two ingredients. We establish a sufficient decrease
condition for elitist, rank-based evolutionary algorithms, formulated for an
essentially monotonically transfo... | computer science |
14,034 | Evolutionary Multitasking for Single-objective Continuous Optimization:
Benchmark Problems, Performance Metric, and Baseline Results | cs.NE | In this report, we suggest nine test problems for multi-task single-objective
optimization (MTSOO), each of which consists of two single-objective
optimization tasks that need to be solved simultaneously. The relationship
between tasks varies between different test problems, which would be helpful to
have a comprehensi... | computer science |
14,035 | Improving Gravitational Search Algorithm Performance with Artificial Bee
Colony Algorithm for Constrained Numerical Optimization | cs.NE | In this paper, we propose an improved gravitational search algorithm named
GSABC. The algorithm improves gravitational search algorithm (GSA) results
improved by using artificial bee colony algorithm (ABC) to solve constrained
numerical optimization problems. In GSA, solutions are attracted towards each
other by applyi... | computer science |
14,036 | Noisy Softplus: an activation function that enables SNNs to be trained
as ANNs | cs.NE | We extended the work of proposed activation function, Noisy Softplus, to fit
into training of layered up spiking neural networks (SNNs). Thus, any ANN
employing Noisy Softplus neurons, even of deep architecture, can be trained
simply by the traditional algorithm, for example Back Propagation (BP), and the
trained weigh... | computer science |
14,037 | Investigating the Parameter Space of Evolutionary Algorithms | cs.NE | The practice of evolutionary algorithms involves the tuning of many
parameters. How big should the population be? How many generations should the
algorithm run? What is the (tournament selection) tournament size? What
probabilities should one assign to crossover and mutation? Through an extensive
series of experiments ... | computer science |
14,038 | Prediction of Muscle Activations for Reaching Movements using Deep
Neural Networks | cs.NE | The motor control problem involves determining the time-varying muscle
activation trajectories required to accomplish a given movement. Muscle
redundancy makes motor control a challenging task: there are many possible
activation trajectories that accomplish the same movement. Despite this
redundancy, most movements are... | computer science |
14,039 | Temporally Efficient Deep Learning with Spikes | cs.NE | The vast majority of natural sensory data is temporally redundant. Video
frames or audio samples which are sampled at nearby points in time tend to have
similar values. Typically, deep learning algorithms take no advantage of this
redundancy to reduce computation. This can be an obscene waste of energy. We
present a va... | computer science |
14,040 | MATIC: Adaptation and In-situ Canaries for Energy-Efficient Neural
Network Acceleration | cs.NE | - The primary author has withdrawn this paper due to conflict of interest -
We present MATIC (Memory-Adaptive Training and In-situ Canaries), a voltage
scaling methodology that addresses the SRAM efficiency bottleneck in DNN
accelerators. To overscale DNN weight SRAMs, MATIC combines specific
characteristics of destruc... | computer science |
14,041 | The Evolution of Neural Network-Based Chart Patterns: A Preliminary
Study | cs.NE | A neural network-based chart pattern represents adaptive parametric features,
including non-linear transformations, and a template that can be applied in the
feature space. The search of neural network-based chart patterns has been
unexplored despite its potential expressiveness. In this paper, we formulate a
general c... | computer science |
14,042 | Genetic Algorithm with Optimal Recombination for the Asymmetric
Travelling Salesman Problem | cs.NE | We propose a new genetic algorithm with optimal recombination for the
asymmetric instances of travelling salesman problem. The algorithm incorporates
several new features that contribute to its effectiveness: (i) Optimal
recombination problem is solved within crossover operator. (ii) A new mutation
operator performs a ... | computer science |
14,043 | Machine Learning Approaches to Energy Consumption Forecasting in
Households | cs.NE | We consider the problem of power demand forecasting in residential
micro-grids. Several approaches using ARMA models, support vector machines, and
recurrent neural networks that perform one-step ahead predictions have been
proposed in the literature. Here, we extend them to perform multi-step ahead
forecasting and we c... | computer science |
14,044 | A Distance Between Populations for n-Points Crossover in Genetic
Algorithms | cs.NE | Genetic algorithms (GAs) are an optimization technique that has been
successfully used on many real-world problems. There exist different approaches
to their theoretical study. In this paper we complete a recently presented
approach to model one-point crossover using pretopologies (or Cech topologies)
in two ways. Firs... | computer science |
14,045 | Time Series Forecasting Based on Augmented Long Short-Term Memory | cs.NE | In this paper, we use recurrent autoencoder model to predict the time series
in single and multiple steps ahead. Previous prediction methods, such as
recurrent neural network (RNN) and deep belief network (DBN) models, cannot
learn long term dependencies. And conventional long short-term memory (LSTM)
model doesn't rem... | computer science |
14,046 | Modeling preference time in middle distance triathlons | cs.NE | Modeling preference time in triathlons means predicting the intermediate
times of particular sports disciplines by a given overall finish time in a
specific triathlon course for the athlete with the known personal best result.
This is a hard task for athletes and sport trainers due to a lot of different
factors that ne... | computer science |
14,047 | Structure Optimization for Deep Multimodal Fusion Networks using
Graph-Induced Kernels | cs.NE | A popular testbed for deep learning has been multimodal recognition of human
activity or gesture involving diverse inputs such as video, audio, skeletal
pose and depth images. Deep learning architectures have excelled on such
problems due to their ability to combine modality representations at different
levels of nonli... | computer science |
14,048 | Identification of non-linear behavior models with restricted or
redundant data | cs.NE | This study presents a new strategy for the identification of material
parameters in the case of restricted or redundant data, based on a hybrid
approach combining a genetic algorithm and the Levenberg-Marquardt method. The
proposed methodology consists essentially in a statistically based topological
analysis of the se... | computer science |
14,049 | How Noisy Data Affects Geometric Semantic Genetic Programming | cs.NE | Noise is a consequence of acquiring and pre-processing data from the
environment, and shows fluctuations from different sources---e.g., from
sensors, signal processing technology or even human error. As a machine
learning technique, Genetic Programming (GP) is not immune to this problem,
which the field has frequently ... | computer science |
14,050 | Theory of the superposition principle for randomized connectionist
representations in neural networks | cs.NE | To understand cognitive reasoning in the brain, it has been proposed that
symbols and compositions of symbols are represented by activity patterns
(vectors) in a large population of neurons. Formal models implementing this
idea [Plate 2003], [Kanerva 2009], [Gayler 2003], [Eliasmith 2012] include a
reversible superposi... | computer science |
14,051 | An HTM based cortical algorithm for detection of seismic waves | cs.NE | Recognizing seismic waves immediately is very important for the realization
of efficient disaster prevention. Generally these systems consist of a network
of seismic detectors that send real time data to a central server. The server
elaborates the data and attempts to recognize the first signs of an earthquake.
The cur... | computer science |
14,052 | Simultaneous Optimization of Neural Network Weights and Active Nodes
using Metaheuristics | cs.NE | Optimization of neural network (NN) significantly influenced by the transfer
function used in its active nodes. It has been observed that the homogeneity in
the activation nodes does not provide the best solution. Therefore, the
customizable transfer functions whose underlying parameters are subjected to
optimization w... | computer science |
14,053 | ACO for Continuous Function Optimization: A Performance Analysis | cs.NE | The performance of the meta-heuristic algorithms often depends on their
parameter settings. Appropriate tuning of the underlying parameters can
drastically improve the performance of a meta-heuristic. The Ant Colony
Optimization (ACO), a population based meta-heuristic algorithm inspired by the
foraging behavior of the... | computer science |
14,054 | Convergence Analysis of Backpropagation Algorithm for Designing an
Intelligent System for Sensing Manhole Gases | cs.NE | Human fatalities are reported due to the excessive proportional presence of
hazardous gas components in the manhole, such as Hydrogen Sulfide, Ammonia,
Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence,
predetermination of these gases is imperative. A neural network (NN) based
intelligent sensory sys... | computer science |
14,055 | Exploiting Active Subspaces in Global Optimization: How Complex is your
Problem? | cs.NE | When applying optimization method to a real-world problem, the possession of
prior knowledge and preliminary analysis on the landscape of a global
optimization problem can give us an insight into the complexity of the problem.
This knowledge can better inform us in deciding what optimization method should
be used to ta... | computer science |
14,056 | Backpropagation in matrix notation | cs.NE | In this note we calculate the gradient of the network function in matrix
notation. | computer science |
14,057 | Hardware-efficient on-line learning through pipelined truncated-error
backpropagation in binary-state networks | cs.NE | Artificial neural networks (ANNs) trained using backpropagation are powerful
learning architectures that have achieved state-of-the-art performance in
various benchmarks. Significant effort has been devoted to developing custom
silicon devices to accelerate inference in ANNs. Accelerating the training
phase, however, h... | computer science |
14,058 | Gray-box optimization and factorized distribution algorithms: where two
worlds collide | cs.NE | The concept of gray-box optimization, in juxtaposition to black-box
optimization, revolves about the idea of exploiting the problem structure to
implement more efficient evolutionary algorithms (EAs). Work on factorized
distribution algorithms (FDAs), whose factorizations are directly derived from
the problem structure... | computer science |
14,059 | The detector principle of constructing artificial neural networks as an
alternative to the connectionist paradigm | cs.NE | Artificial neural networks (ANN) are inadequate to biological neural
networks. This inadequacy is manifested in the use of the obsolete model of the
neuron and the connectionist paradigm of constructing ANN. The result of this
inadequacy is the existence of many shortcomings of the ANN and the problems of
their practic... | computer science |
14,060 | Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal
Memories Based on Memristive Neuromorphic Network | cs.NE | We have calculated the key characteristics of associative
(content-addressable) spatial-temporal memories based on neuromorphic networks
with restricted connectivity - "CrossNets". Such networks may be naturally
implemented in nanoelectronic hardware using hybrid CMOS/memristor circuits,
which may feature extremely hig... | computer science |
14,061 | Quantum Computation via Sparse Distributed Representation | cs.NE | Quantum superposition says that any physical system simultaneously exists in
all of its possible states, the number of which is exponential in the number of
entities composing the system. The strength of presence of each possible state
in the superposition, i.e., its probability of being observed, is represented
by its... | computer science |
14,062 | Solving Mixed Model Workplace Time-dependent Assembly Line Balancing
Problem with FSS Algorithm | cs.NE | Balancing assembly lines, a family of optimization problems commonly known as
Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of
problems of enormous practical interest to manufacturing industry due to the
relevant frequency of this type of production paradigm. For this reason, many
researc... | computer science |
14,063 | Fish School Search Algorithm for Constrained Optimization | cs.NE | In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed ... | computer science |
14,064 | Simultaneously Solving Mixed Model Assembly Line Balancing and
Sequencing problems with FSS Algorithm | cs.NE | Many assembly lines related optimization problems have been tackled by
researchers in the last decades due to its relevance for the decision makers
within manufacturing industry. Many of theses problems, more specifically
Assembly Lines Balancing and Sequencing problems, are known to be NP-Hard.
Therefore, Computationa... | computer science |
14,065 | Building Graph Representations of Deep Vector Embeddings | cs.NE | Patterns stored within pre-trained deep neural networks compose large and
powerful descriptive languages that can be used for many different purposes.
Typically, deep network representations are implemented within vector embedding
spaces, which enables the use of traditional machine learning algorithms on top
of them. ... | computer science |
14,066 | Time Series Compression Based on Adaptive Piecewise Recurrent
Autoencoder | cs.NE | Time series account for a large proportion of the data stored in financial,
medical and scientific databases. The efficient storage of time series is
important in practical applications. In this paper, we propose a novel
compression scheme for time series. The encoder and decoder are both composed
by recurrent neural n... | computer science |
14,067 | An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with
Large Infeasible Regions | cs.NE | This paper proposes an improved epsilon constraint-handling mechanism, and
combines it with a decomposition-based multi-objective evolutionary algorithm
(MOEA/D) to solve constrained multi-objective optimization problems (CMOPs).
The proposed constrained multi-objective evolutionary algorithm (CMOEA) is
named MOEA/D-IE... | computer science |
14,068 | An Evolutionary Stochastic-Local-Search Framework for One-Dimensional
Cutting-Stock Problems | cs.NE | We introduce an evolutionary stochastic-local-search (SLS) algorithm for
addressing a generalized version of the so-called 1/V/D/R cutting-stock
problem. Cutting-stock problems are encountered often in industrial
environments and the ability to address them efficiently usually results in
large economic benefits. Tradit... | computer science |
14,069 | Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep
Learning Networks by Exploiting Numerical Precision Variability | cs.NE | Tartan (TRT), a hardware accelerator for inference with Deep Neural Networks
(DNNs), is presented and evaluated on Convolutional Neural Networks. TRT
exploits the variable per layer precision requirements of DNNs to deliver
execution time that is proportional to the precision p in bits used per layer
for convolutional ... | computer science |
14,070 | A Novel Neural Network Model Specified for Representing Logical
Relations | cs.NE | With computers to handle more and more complicated things in variable
environments, it becomes an urgent requirement that the artificial intelligence
has the ability of automatic judging and deciding according to numerous
specific conditions so as to deal with the complicated and variable cases. ANNs
inspired by brain ... | computer science |
14,071 | Preselection via Classification: A Case Study on Evolutionary
Multiobjective Optimization | cs.NE | In evolutionary algorithms, a preselection operator aims to select the
promising offspring solutions from a candidate offspring set. It is usually
based on the estimated or real objective values of the candidate offspring
solutions. In a sense, the preselection can be treated as a classification
procedure, which classi... | computer science |
14,072 | Standard Steady State Genetic Algorithms Can Hillclimb Faster than
Mutation-only Evolutionary Algorithms | cs.NE | Explaining to what extent the real power of genetic algorithms lies in the
ability of crossover to recombine individuals into higher quality solutions is
an important problem in evolutionary computation. In this paper we show how the
interplay between mutation and crossover can make genetic algorithms hillclimb
faster ... | computer science |
14,073 | Efficient Noisy Optimisation with the Sliding Window Compact Genetic
Algorithm | cs.NE | The compact genetic algorithm is an Estimation of Distribution Algorithm for
binary optimisation problems. Unlike the standard Genetic Algorithm, no
cross-over or mutation is involved. Instead, the compact Genetic Algorithm uses
a virtual population represented as a probability distribution over the set of
binary strin... | computer science |
14,074 | A learning framework for winner-take-all networks with stochastic
synapses | cs.NE | Many recent generative models make use of neural networks to transform the
probability distribution of a simple low-dimensional noise process into the
complex distribution of the data. This raises the question of whether
biological networks operate along similar principles to implement a
probabilistic model of the envi... | computer science |
14,075 | Fast Modeling Methods for Complex System with Separable Features | cs.NE | Data-driven modeling plays an increasingly important role in different areas
of engineering. For most of existing methods, such as genetic programming (GP),
the convergence speed might be too slow for large scale problems with a large
number of variables. Fortunately, in many applications, the target models are
separab... | computer science |
14,076 | Weight-based Fish School Search algorithm for Many-Objective
Optimization | cs.NE | Optimization problems with more than one objective consist in a very
attractive topic for researchers due to its applicability in real-world
situations. Over the years, the research effort in Computational Intelligence
area resulted in algorithms able to achieve good results by solving problems
with more than one confl... | computer science |
14,077 | Restricted Boltzmann machine to determine the input weights for extreme
learning machines | cs.NE | The Extreme Learning Machine (ELM) is a single-hidden layer feedforward
neural network (SLFN) learning algorithm that can learn effectively and
quickly. The ELM training phase assigns the input weights and bias randomly and
does not change them in the whole process. Although the network works well, the
random weights i... | computer science |
14,078 | Boltzmann machines for time-series | cs.NE | We review Boltzmann machines extended for time-series. These models often
have recurrent structure, and back propagration through time (BPTT) is used to
learn their parameters. The per-step computational complexity of BPTT in online
learning, however, grows linearly with respect to the length of preceding
time-series (... | computer science |
14,079 | Boltzmann machines and energy-based models | cs.NE | We review Boltzmann machines and energy-based models. A Boltzmann machine
defines a probability distribution over binary-valued patterns. One can learn
parameters of a Boltzmann machine via gradient based approaches in a way that
log likelihood of data is increased. The gradient and Laplacian of a Boltzmann
machine adm... | computer science |
14,080 | Learning the Enigma with Recurrent Neural Networks | cs.NE | Recurrent neural networks (RNNs) represent the state of the art in
translation, image captioning, and speech recognition. They are also capable of
learning algorithmic tasks such as long addition, copying, and sorting from a
set of training examples. We demonstrate that RNNs can learn decryption
algorithms -- the mappi... | computer science |
14,081 | A parameterized activation function for learning fuzzy logic operations
in deep neural networks | cs.NE | We present a deep learning architecture for learning fuzzy logic expressions.
Our model uses an innovative, parameterized, differentiable activation function
that can learn a number of logical operations by gradient descent. This
activation function allows a neural network to determine the relationships
between its inp... | computer science |
14,082 | Slope Stability Analysis with Geometric Semantic Genetic Programming | cs.NE | Genetic programming has been widely used in the engineering field. Compared
with the conventional genetic programming and artificial neural network,
geometric semantic genetic programming (GSGP) is superior in astringency and
computing efficiency. In this paper, GSGP is adopted for the classification and
regression ana... | computer science |
14,083 | Neural Distributed Autoassociative Memories: A Survey | cs.NE | Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) fo... | computer science |
14,084 | Theoretical Analysis of Stochastic Search Algorithms | cs.NE | Theoretical analyses of stochastic search algorithms, albeit few, have always
existed since these algorithms became popular. Starting in the nineties a
systematic approach to analyse the performance of stochastic search heuristics
has been put in place. This quickly increasing basis of results allows,
nowadays, the ana... | computer science |
14,085 | What Weights Work for You? Adapting Weights for Any Pareto Front Shape
in Decomposition-based Evolutionary Multi-Objective Optimisation | cs.NE | The quality of solution sets generated by decomposition-based evolutionary
multiobjective optimisation (EMO) algorithms depends heavily on the consistency
between a given problem's Pareto front shape and the specified weights'
distribution. A set of weights distributed uniformly in a simplex often lead to
a set of well... | computer science |
14,086 | Applying ACO To Large Scale TSP Instances | cs.NE | Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven
successful at solving Travelling Salesman Problems (TSP). However, ACO suffers
from two issues; the first is that the technique has significant memory
requirements for storing pheromone levels on edges between cities and second,
the iterative p... | computer science |
14,087 | Evolution of Convolutional Highway Networks | cs.NE | Convolutional highways are deep networks based on multiple stacked
convolutional layers for feature preprocessing. We introduce an evolutionary
algorithm (EA) for optimization of the structure and hyperparameters of
convolutional highways and demonstrate the potential of this optimization
setting on the well-known MNIS... | computer science |
14,088 | Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic
Traveling Salesman Problem | cs.NE | Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm
based on the self-organizing behavior of individuals in a simulated social
environment. SOMA performs iterative computations on a population of potential
solutions in the given search space to obtain an optimal solution. In this
paper, an Opportun... | computer science |
14,089 | Spatio-temporal Learning with Arrays of Analog Nanosynapses | cs.NE | Emerging nanodevices such as resistive memories are being considered for
hardware realizations of a variety of artificial neural networks (ANNs),
including highly promising online variants of the learning approaches known as
reservoir computing (RC) and the extreme learning machine (ELM). We propose an
RC/ELM inspired ... | computer science |
14,090 | Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage
Requirement | cs.NE | This paper presents a storage-efficient learning model titled Recursive
Binary Neural Networks for sensing devices having a limited amount of on-chip
data storage such as < 100's kilo-Bytes. The main idea of the proposed model is
to recursively recycle data storage of synaptic weights (parameters) during
training. This... | computer science |
14,091 | $ε$-Lexicase selection: a probabilistic and multi-objective
analysis of lexicase selection in continuous domains | cs.NE | Lexicase selection is a parent selection method that considers training cases
individually, rather than in aggregate, when performing parent selection.
Whereas previous work has demonstrated the ability of lexicase selection to
solve difficult problems, the central goal of this paper is to develop the
theoretical under... | computer science |
14,092 | Geometric Semantic Genetic Programming Algorithm and Slump Prediction | cs.NE | Research on the performance of recycled concrete as building material in the
current world is an important subject. Given the complex composition of
recycled concrete, conventional methods for forecasting slump scarcely obtain
satisfactory results. Based on theory of nonlinear prediction method, we
propose a recycled c... | computer science |
14,093 | Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks
Based on Back Propagation with Binary Activations | cs.NE | We present a new back propagation based training algorithm for discrete-time
spiking neural networks (SNN). Inspired by recent deep learning algorithms on
binarized neural networks, binary activation with a straight-through gradient
estimator is used to model the leaky integrate-fire spiking neuron, overcoming
the diff... | computer science |
14,094 | Opposition based Ensemble Micro Differential Evolution | cs.NE | Differential evolution (DE) algorithm with a small population size is called
Micro-DE (MDE). A small population size decreases the computational complexity
but also reduces the exploration ability of DE by limiting the population
diversity. In this paper, we propose the idea of combining ensemble mutation
scheme select... | computer science |
14,095 | Robust Associative Memories Naturally Occuring From Recurrent Hebbian
Networks Under Noise | cs.NE | The brain is a noisy system subject to energy constraints. These facts are
rarely taken into account when modelling artificial neural networks. In this
paper, we are interested in demonstrating that those factors can actually lead
to the appearance of robust associative memories. We first propose a simplified
model of ... | computer science |
14,096 | Optimizing PID parameters with machine learning | cs.NE | This paper examines the Evolutionary programming (EP) method for optimizing
PID parameters. PID is the most common type of regulator within control theory,
partly because it's relatively simple and yields stable results for most
applications. The p, i and d parameters vary for each application; therefore,
choosing the ... | computer science |
14,097 | PSA: A novel optimization algorithm based on survival rules of porcellio
scaber | cs.NE | Bio-inspired algorithms have received a significant amount of attention in
both academic and engineering societies. In this paper, based on the
observation of two major survival rules of a species of woodlice, i.e.,
porcellio scaber, we design and propose an algorithm called the porcellio
scaber algorithm (PSA) for sol... | computer science |
14,098 | Adaptive Generation-Based Evolution Control for Gaussian Process
Surrogate Models | cs.NE | The interest in accelerating black-box optimizers has resulted in several
surrogate model-assisted version of the Covariance Matrix Adaptation Evolution
Strategy, a state-of-the-art continuous black-box optimizer. The version called
Surrogate CMA-ES uses Gaussian processes or random forests surrogate models
with a gene... | computer science |
14,099 | A Many-Objective Evolutionary Algorithm with Angle-Based Selection and
Shift-Based Density Estimation | cs.NE | Evolutionary many-objective optimization has been gaining increasing
attention from the evolutionary computation research community. Much effort has
been devoted to addressing this issue by improving the scalability of
multiobjective evolutionary algorithms, such as Pareto-based,
decomposition-based, and indicator-base... | computer science |
14,100 | The use of neural networks in the analysis of sleep stages and the
diagnosis of narcolepsy | cs.NE | We used neural networks in ~3,000 sleep recordings from over 10 locations to
automate sleep stage scoring, producing a probability distribution called an
hypnodensity graph. Accuracy was validated in 70 subjects scored by six
technicians (gold standard). Our best model performed better than any
individual scorer, reach... | computer science |
14,101 | A batching and scheduling optimisation for a cutting work-center:
Acta-Mobilier case study | cs.NE | The purpose of this study is to investigate an approach to group lots in
batches and to schedule these batches on Acta-Mobilier cutting work-center
while taking into account numerous constraints and objectives. The specific
batching method was proposed to handle the Acta-Mobilier problem and a
mathematical formalisatio... | computer science |
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