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14,102 | A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation | cs.NE | In this paper, we propose an efficient approximated rank one update for
covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two
evolution paths as simple as that of CMA-ES, while avoiding the computational
matrix decomposition. We analyze the algorithms' properties and behaviors. We
experimentally... | computer science |
14,103 | Porcellio scaber algorithm (PSA) for solving constrained optimization
problems | cs.NE | In this paper, we extend a bio-inspired algorithm called the porcellio scaber
algorithm (PSA) to solve constrained optimization problems, including a
constrained mixed discrete-continuous nonlinear optimization problem. Our
extensive experiment results based on benchmark optimization problems show that
the PSA has a be... | computer science |
14,104 | STDP Based Pruning of Connections and Weight Quantization in Spiking
Neural Networks for Energy Efficient Recognition | cs.NE | Spiking Neural Networks (SNNs) with a large number of weights and varied
weight distribution can be difficult to implement in emerging in-memory
computing hardware due to the limitations on crossbar size (implementing dot
product), the constrained number of conductance levels in non-CMOS devices and
the power budget. W... | computer science |
14,105 | Efficient Computation in Adaptive Artificial Spiking Neural Networks | cs.NE | Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sha... | computer science |
14,106 | Evolution in Virtual Worlds | cs.NE | This chapter discusses the possibility of instilling a virtual world with
mechanisms for evolution and natural selection in order to generate rich
ecosystems of complex organisms in a process akin to biological evolution. Some
previous work in the area is described, and successes and failures are
discussed. The compone... | computer science |
14,107 | Moderate Environmental Variation Promotes the Evolution of Robust
Solutions | cs.NE | Previous evolutionary studies demonstrated how evaluating evolving agents in
variable environmental conditions enable them to develop solutions that are
robust to environmental variation. We demonstrate how the robustness of the
agents can be further improved by exposing them also to environmental
variations throughout... | computer science |
14,108 | Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep
Convolutional Neural Network-Based Transfer Learning | cs.NE | Early fault diagnosis in complex mechanical systems such as gearbox has
always been a great challenge, even with the recent development in deep neural
networks. The performance of a classic fault diagnosis system predominantly
depends on the features extracted and the classifier subsequently applied.
Although a large n... | computer science |
14,109 | Efficient Licence Plate Detection By Unique Edge Detection Algorithm and
Smarter Interpretation Through IoT | cs.NE | Vehicles play a vital role in modern day transportation systems. Number plate
provides a standard means of identification for any vehicle. To serve this
purpose, automatic licence plate recognition system was developed. This
consisted of four major steps: Pre-processing of the obtained image, extraction
of licence plat... | computer science |
14,110 | BAS: Beetle Antennae Search Algorithm for Optimization Problems | cs.NE | Meta-heuristic algorithms have become very popular because of powerful
performance on the optimization problem. A new algorithm called beetle antennae
search algorithm (BAS) is proposed in the paper inspired by the searching
behavior of longhorn beetles. The BAS algorithm imitates the function of
antennae and the rando... | computer science |
14,111 | Beetle Antennae Search without Parameter Tuning (BAS-WPT) for
Multi-objective Optimization | cs.NE | Beetle antennae search (BAS) is an efficient meta-heuristic algorithm
inspired by foraging behaviors of beetles. This algorithm includes several
parameters for tuning and the existing results are limited to solve single
objective optimization. This work pushes forward the research on BAS by
providing one variant that r... | computer science |
14,112 | BP-STDP: Approximating Backpropagation using Spike Timing Dependent
Plasticity | cs.NE | The problem of training spiking neural networks (SNNs) is a necessary
precondition to understanding computations within the brain, a field still in
its infancy. Previous work has shown that supervised learning in multi-layer
SNNs enables bio-inspired networks to recognize patterns of stimuli through
hierarchical featur... | computer science |
14,113 | Neural Networks Architecture Evaluation in a Quantum Computer | cs.NE | In this work, we propose a quantum algorithm to evaluate neural networks
architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The
proposed algorithm is based on a quantum associative memory and the learning
algorithm for artificial neural networks. Unlike conventional algorithms for
evaluating ne... | computer science |
14,114 | Reliability and Sharpness in Border Crossing Traffic Interval Prediction | cs.NE | Short-term traffic volume prediction models have been extensively studied in
the past few decades. However, most of the previous studies only focus on
single-value prediction. Considering the uncertain and chaotic nature of the
transportation system, an accurate and reliable prediction interval with upper
and lower bou... | computer science |
14,115 | Concurrent Pump Scheduling and Storage Level Optimization Using
Meta-Models and Evolutionary Algorithms | cs.NE | In spite of the growing computational power offered by the commodity
hardware, fast pump scheduling of complex water distribution systems is still a
challenge. In this paper, the Artificial Neural Network (ANN) meta-modeling
technique has been employed with a Genetic Algorithm (GA) for simultaneously
optimizing the pum... | computer science |
14,116 | On evolutionary selection of blackjack strategies | cs.NE | We apply the approach of evolutionary programming to the problem of
optimization of the blackjack basic strategy. We demonstrate that the
population of initially random blackjack strategies evolves and saturates to a
profitable performance in about one hundred generations. The resulting strategy
resembles the known bla... | computer science |
14,117 | Bidirectional deep-readout echo state networks | cs.NE | We propose a deep architecture for the classification of multivariate time
series. By means of a recurrent and untrained reservoir we generate a vectorial
representation that embeds temporal relationships in the data. To improve the
memorization capability, we implement a bidirectional reservoir, whose last
state captu... | computer science |
14,118 | Addressing Expensive Multi-objective Games with Postponed Preference
Articulation via Memetic Co-evolution | cs.NE | This paper presents algorithmic and empirical contributions demonstrating
that the convergence characteristics of a co-evolutionary approach to tackle
Multi-Objective Games (MOGs) with postponed preference articulation can often
be hampered due to the possible emergence of the so-called Red Queen effect.
Accordingly, i... | computer science |
14,119 | Community detection with spiking neural networks for neuromorphic
hardware | cs.NE | We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing... | computer science |
14,120 | Two-Archive Evolutionary Algorithm for Constrained Multi-Objective
Optimization | cs.NE | When solving constrained multi-objective optimization problems, an important
issue is how to balance convergence, diversity and feasibility simultaneously.
To address this issue, this paper proposes a parameter-free constraint handling
technique, two-archive evolutionary algorithm, for constrained multi-objective
optim... | computer science |
14,121 | Novel Adaptive Genetic Algorithm Sample Consensus | cs.NE | Random sample consensus (RANSAC) is a successful algorithm in model fitting
applications. It is vital to have strong exploration phase when there are an
enormous amount of outliers within the dataset. Achieving a proper model is
guaranteed by pure exploration strategy of RANSAC. However, finding the optimum
result requ... | computer science |
14,122 | Collaborative Evolution of 3D Models | cs.NE | We present a computational model of creative design based on collaborative
interactive genetic algorithms. In our model, designers individually guide
interactive genetic algorithms (IGAs) to generate and explore potential design
solutions quickly. Collaboration is supported by allowing designers to share
solutions amon... | computer science |
14,123 | Parameters Optimization of Deep Learning Models using Particle Swarm
Optimization | cs.NE | Deep learning has been successfully applied in several fields such as machine
translation, manufacturing, and pattern recognition. However, successful
application of deep learning depends upon appropriately setting its parameters
to achieve high quality results. The number of hidden layers and the number of
neurons in ... | computer science |
14,124 | Adversarial Networks for Prostate Cancer Detection | cs.NE | The large number of trainable parameters of deep neural networks renders them
inherently data hungry. This characteristic heavily challenges the medical
imaging community and to make things even worse, many imaging modalities are
ambiguous in nature leading to rater-dependant annotations that current loss
formulations ... | computer science |
14,125 | Universal discrete-time reservoir computers with stochastic inputs and
linear readouts using non-homogeneous state-affine systems | cs.NE | A new class of non-homogeneous state-affine systems is introduced for use in
reservoir computing. Sufficient conditions are identified that guarantee first,
that the associated reservoir computers with linear readouts are causal,
time-invariant, and satisfy the fading memory property and second, that a
subset of this c... | computer science |
14,126 | Improving Brain Storm Optimization Algorithm via Simplex Search | cs.NE | Through modeling human's brainstorming process, the brain storm optimization
(BSO) algorithm has become a promising population based evolution algorithm.
However, BSO is often good at global exploration but not good enough at local
exploitation, just like most global optimization algorithms. In this paper, the
Nelder-M... | computer science |
14,127 | Backpropagation generalized for output derivatives | cs.NE | Backpropagation algorithm is the cornerstone for neural network analysis.
Paper extends it for training any derivatives of neural network's output with
respect to its input. By the dint of it feedforward networks can be used to
solve or verify solutions of partial or simple, linear or nonlinear
differential equations. ... | computer science |
14,128 | Robustness, Evolvability and Phenotypic Complexity: Insights from
Evolving Digital Circuits | cs.NE | We show how the characteristics of the evolutionary algorithm influence the
evolvability of candidate solutions, i.e. the propensity of evolving
individuals to generate better solutions as a result of genetic variation. More
specifically, (1+{\lambda}) evolutionary strategies largely outperform
({\mu}+1) evolutionary s... | computer science |
14,129 | Enhancing approximation abilities of neural networks by training
derivatives | cs.NE | Method for increasing precision of feedforward networks is presented. With
the aid of it they can serve as a better tool for describing smooth functions.
Namely, it is shown that when training uses derivatives of target function up
to the fourth order, approximation can be nearly machine precise. It is
demonstrated in ... | computer science |
14,130 | A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder
for Image Classification | cs.NE | Convolutional auto-encoders have shown their remarkable performance in
stacking to deep convolutional neural networks for classifying image data
during past several years. However, they are unable to construct the
state-of-the-art convolutional neural networks due to their intrinsic
architectures. In this regard, we pr... | computer science |
14,131 | Evolving Unsupervised Deep Neural Networks for Learning Meaningful
Representations | cs.NE | Deep Learning (DL) aims at learning the \emph{meaningful representations}. A
meaningful representation refers to the one that gives rise to significant
performance improvement of associated Machine Learning (ML) tasks by replacing
the raw data as the input. However, optimal architecture design and model
parameter estim... | computer science |
14,132 | Neural networks catching up with finite differences in solving partial
differential equations in higher dimensions | cs.NE | Fully connected multilayer perceptrons are used for obtaining numerical
solutions of partial differential equations in various dimensions. Independent
variables are fed into the input layer, and the output is considered as
solution's value. To train such a network one can use square of equation's
residual as a cost fun... | computer science |
14,133 | Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved
Particle Swarm Optimization | cs.NE | Fuzzy clustering has become a widely used data mining technique and plays an
important role in grouping, traversing and selectively using data for user
specified applications. The deterministic Fuzzy C-Means (FCM) algorithm may
result in suboptimal solutions when applied to multidimensional data in
real-world, time-con... | computer science |
14,134 | Self-adaptation of Genetic Operators Through Genetic Programming
Techniques | cs.NE | Here we propose an evolutionary algorithm that self modifies its operators at
the same time that candidate solutions are evolved. This tackles convergence
and lack of diversity issues, leading to better solutions. Operators are
represented as trees and are evolved using genetic programming (GP) techniques.
The proposed... | computer science |
14,135 | Selective-Candidate Framework with Similarity Selection Rule for
Evolutionary Optimization | cs.NE | This paper proposes to resolve limitations of the traditional
one-reproduction (OR) framework which produces only one candidate in a single
reproduction procedure. A selective-candidate framework with similarity
selection rule (SCSS) is suggested to make possible, a selective direction of
search. In the SCSS framework,... | computer science |
14,136 | On the Relationship Between the OpenAI Evolution Strategy and Stochastic
Gradient Descent | cs.NE | Because stochastic gradient descent (SGD) has shown promise optimizing neural
networks with millions of parameters and few if any alternatives are known to
exist, it has moved to the heart of leading approaches to reinforcement
learning (RL). For that reason, the recent result from OpenAI showing that a
particular kind... | computer science |
14,137 | Virtual Sensor Modelling using Neural Networks with Coefficient-based
Adaptive Weights and Biases Search Algorithm for Diesel Engines | cs.NE | With the explosion in the field of Big Data and introduction of more
stringent emission norms every three to five years, automotive companies must
not only continue to enhance the fuel economy ratings of their products, but
also provide valued services to their customers such as delivering engine
performance and health... | computer science |
14,138 | Learning Based on CC1 and CC4 Neural Networks | cs.NE | We propose that a general learning system should have three kinds of agents
corresponding to sensory, short-term, and long-term memory that implicitly will
facilitate context-free and context-sensitive aspects of learning. These three
agents perform mututally complementary functions that capture aspects of the
human co... | computer science |
14,139 | Towards co-evolution of fitness predictors and Deep Neural Networks | cs.NE | Deep neural networks proved to be a very useful and powerful tool with many
practical applications. They especially excel at learning from large data sets
with labeled samples. However, in order to achieve good learning results, the
network architecture has to be carefully designed. Creating an optimal topology
require... | computer science |
14,140 | Interactive Decomposition Multi-Objective Optimization via Progressively
Learned Value Functions | cs.NE | Decomposition has become an increasingly popular technique for evolutionary
multi-objective optimization (EMO). A decomposition-based EMO algorithm is
usually designed to approximate a whole Pareto-optimal front (PF). However, in
practice, the decision maker (DM) might only be interested in her/his region of
interest (... | computer science |
14,141 | Recent Advances in Recurrent Neural Networks | cs.NE | Recurrent neural networks (RNNs) are capable of learning features and long
term dependencies from sequential and time-series data. The RNNs have a stack
of non-linear units where at least one connection between units forms a
directed cycle. A well-trained RNN can model any dynamical system; however,
training RNNs is mo... | computer science |
14,142 | DENSER: Deep Evolutionary Network Structured Representation | cs.NE | Deep Evolutionary Network Structured Representation (DENSER) is a novel
approach to automatically design Artificial Neural Networks (ANNs) using
Evolutionary Computation (EC). The algorithm not only searches for the best
network topology (e.g., number of layers, type of layers), but also tunes
hyper-parameters, such as... | computer science |
14,143 | Dynamic Island Model based on Spectral Clustering in Genetic Algorithm | cs.NE | How to maintain relative high diversity is important to avoid premature
convergence in population-based optimization methods. Island model is widely
considered as a major approach to achieve this because of its flexibility and
high efficiency. The model maintains a group of sub-populations on different
islands and allo... | computer science |
14,144 | Complexity Theory for Discrete Black-Box Optimization Heuristics | cs.NE | A predominant topic in the theory of evolutionary algorithms and, more
generally, theory of randomized black-box optimization techniques is running
time analysis. Running time analysis aims at understanding the performance of a
given heuristic on a given problem by bounding the number of function
evaluations that are n... | computer science |
14,145 | Australia's long-term electricity demand forecasting using deep neural
networks | cs.NE | Accurate prediction of long-term electricity demand has a significant role in
demand side management and electricity network planning and operation. Demand
over-estimation results in over-investment in network assets, driving up the
electricity prices, while demand under-estimation may lead to under-investment
resultin... | computer science |
14,146 | On Enhancing Genetic Algorithms Using New Crossovers | cs.NE | This paper investigates the use of more than one crossover operator to
enhance the performance of genetic algorithms. Novel crossover operators are
proposed such as the Collision crossover, which is based on the physical rules
of elastic collision, in addition to proposing two selection strategies for the
crossover ope... | computer science |
14,147 | Data-driven forecasting of solar irradiance | cs.NE | This paper describes a flexible approach to short term prediction of
meteorological variables. In particular, we focus on the prediction of the
solar irradiance one hour ahead, a task that has high practical value when
optimizing solar energy resources. As D\'efi EGC 2018 provides us with time
series data for multiple ... | computer science |
14,148 | Overcoming the vanishing gradient problem in plain recurrent networks | cs.NE | Plain recurrent networks greatly suffer from the vanishing gradient problem
while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and
Gated Recurrent Unit (GRU) deliver promising results in many sequence learning
tasks through sophisticated network designs. This paper shows how we can
address this pr... | computer science |
14,149 | Improving TSP Solutions Using GA with a New Hybrid Mutation Based on
Knowledge and Randomness | cs.NE | Genetic algorithm (GA) is an efficient tool for solving optimization problems
by evolving solutions, as it mimics the Darwinian theory of natural evolution.
The mutation operator is one of the key success factors in GA, as it is
considered the exploration operator of GA. Various mutation operators exist to
solve hard c... | computer science |
14,150 | Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean
Optimisation | cs.NE | Selection hyper-heuristics are randomised search methodologies which choose
and execute heuristics from a set of low-level heuristics. Recent research for
the LeadingOnes benchmark function has shown that the standard Simple Random,
Permutation, Random Gradient, Greedy and Reinforcement Learning selection
mechanisms sh... | computer science |
14,151 | Free Energy Minimization Using the 2-D Cluster Variation Method: Initial
Code Verification and Validation | cs.NE | A new approach for general artificial intelligence (GAI), building on neural
network deep learning architectures, can make use of one or more hidden layers
that have the ability to continuously reach a free energy minimum even after
input stimulus is removed, allowing for a variety of possible behaviors. One
reason tha... | computer science |
14,152 | Deep Interactive Evolution | cs.NE | This paper describes an approach that combines generative adversarial
networks (GANs) with interactive evolutionary computation (IEC). While GANs can
be trained to produce lifelike images, they are normally sampled randomly from
the learned distribution, providing limited control over the resulting output.
On the other... | computer science |
14,153 | Sine Cosine Crow Search Algorithm: A powerful hybrid meta heuristic for
global optimization | cs.NE | This paper presents a novel hybrid algorithm named Since Cosine Crow Search
Algorithm. To propose the SCCSA, two novel algorithms are considered including
Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of
the two algorithms are considered and utilize to design an efficient hybrid
algorithm... | computer science |
14,154 | The Benefits of Population Diversity in Evolutionary Algorithms: A
Survey of Rigorous Runtime Analyses | cs.NE | Population diversity is crucial in evolutionary algorithms to enable global
exploration and to avoid poor performance due to premature convergence. This
book chapter reviews runtime analyses that have shown benefits of population
diversity, either through explicit diversity mechanisms or through naturally
emerging dive... | computer science |
14,155 | Multi-Layer Competitive-Cooperative Framework for Performance
Enhancement of Differential Evolution | cs.NE | Differential Evolution (DE) is one of the most powerful optimizers in the
evolutionary algorithm (EA) family. In recent years, many DE variants have been
proposed to enhance performance. However, when compared with each other,
significant differences in performances are seldomly observed. To meet this
challenge of a mo... | computer science |
14,156 | Improved Runtime Bounds for the Univariate Marginal Distribution
Algorithm via Anti-Concentration | cs.NE | Unlike traditional evolutionary algorithms which produce offspring via
genetic operators, Estimation of Distribution Algorithms (EDAs) sample
solutions from probabilistic models which are learned from selected
individuals. It is hoped that EDAs may improve optimisation performance on
epistatic fitness landscapes by lea... | computer science |
14,157 | Memory-Augmented Neural Networks for Predictive Process Analytics | cs.NE | Process analytics involves a sophisticated layer of data analytics built over
the traditional notion of process mining. The flexible execution of business
process instances involves multiple critical decisions including what task to
perform next and what resources to allocate to a task. In this paper, we
explore the ap... | computer science |
14,158 | Resset: A Recurrent Model for Sequence of Sets with Applications to
Electronic Medical Records | cs.NE | Modern healthcare is ripe for disruption by AI. A game changer would be
automatic understanding the latent processes from electronic medical records,
which are being collected for billions of people worldwide. However, these
healthcare processes are complicated by the interaction between at least three
dynamic componen... | computer science |
14,159 | An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep
Convolutional Neural Networks using Stochastic Computing | cs.NE | With recent trend of wearable devices and Internet of Things (IoTs), it
becomes attractive to develop hardware-based deep convolutional neural networks
(DCNNs) for embedded applications, which require low power/energy consumptions
and small hardware footprints. Recent works demonstrated that the Stochastic
Computing (S... | computer science |
14,160 | An Adaptive Genetic Algorithm for Solving N-Queens Problem | cs.NE | In this paper a Metaheuristic approach for solving the N-Queens Problem is
introduced to find the best possible solution in a reasonable amount of time.
Genetic Algorithm is used with a novel fitness function as the Metaheuristic.
The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a
way so that... | computer science |
14,161 | Brain-inspired photonic signal processor for periodic pattern generation
and chaotic system emulation | cs.NE | Reservoir computing is a bio-inspired computing paradigm for processing
time-dependent signals. Its hardware implementations have received much
attention because of their simplicity and remarkable performance on a series of
benchmark tasks. In previous experiments the output was uncoupled from the
system and in most ca... | computer science |
14,162 | LightNN: Filling the Gap between Conventional Deep Neural Networks and
Binarized Networks | cs.NE | Application-specific integrated circuit (ASIC) implementations for Deep
Neural Networks (DNNs) have been adopted in many systems because of their
higher classification speed. However, although they may be characterized by
better accuracy, larger DNNs require significant energy and area, thereby
limiting their wide adop... | computer science |
14,163 | Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic
System-on-a-Chip | cs.NE | Emerging non-volatile memory (NVM), or memristive, devices promise
energy-efficient realization of deep learning, when efficiently integrated with
mixed-signal integrated circuits on a CMOS substrate. Even though several
algorithmic challenges need to be addressed to turn the vision of memristive
Neuromorphic Systems-o... | computer science |
14,164 | Using a reservoir computer to learn chaotic attractors, with
applications to chaos synchronisation and cryptography | cs.NE | Using the machine learning approach known as reservoir computing, it is
possible to train one dynamical system to emulate another. We show that such
trained reservoir computers reproduce the properties of the attractor of the
chaotic system sufficiently well to exhibit chaos synchronisation. That is, the
trained reserv... | computer science |
14,165 | Drift Theory in Continuous Search Spaces: Expected Hitting Time of the
(1+1)-ES with 1/5 Success Rule | cs.NE | This paper explores the use of the standard approach for proving runtime
bounds in discrete domains---often referred to as drift analysis---in the
context of optimization on a continuous domain. Using this framework we analyze
the (1+1) Evolution Strategy with one-fifth success rule on the sphere
function. To deal with... | computer science |
14,166 | MOEA/D with Angle-based Constrained Dominance Principle for Constrained
Multi-objective Optimization Problems | cs.NE | This paper proposes a novel constraint-handling mechanism named angle-based
constrained dominance principle (ACDP) embedded in a decomposition-based
multi-objective evolutionary algorithm (MOEA/D) to solve constrained
multi-objective optimization problems (CMOPs). To maintain the diversity of the
working population, AC... | computer science |
14,167 | Optimal approximation of continuous functions by very deep ReLU networks | cs.NE | We prove that deep ReLU neural networks with conventional fully-connected
architectures with $W$ weights can approximate continuous $\nu$-variate
functions $f$ with uniform error not exceeding $a_\nu\omega_f(c_\nu
W^{-2/\nu}),$ where $\omega_f$ is the modulus of continuity of $f$ and $a_\nu,
c_\nu$ are some $\nu$-depen... | computer science |
14,168 | Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice
Resource Management in 5G Networks | cs.NE | In the context of Fifth Generation (5G) mobile networks, the concept of
"slice as a service" (SaaS) promotes mobile network operators to flexibly share
infrastructures with mobile service providers and stakeholders. However, it
also challenges with an emerging demand for efficient online algorithms to
optimize the requ... | computer science |
14,169 | A theoretical guideline for designing an effective adaptive particle
swarm | cs.NE | In this paper we theoretically investigate underlying assumptions that have
been used for designing adaptive particle swarm optimization algorithms in the
past years. We relate these assumptions to the movement patterns of particles
controlled by coefficient values (inertia weight and acceleration coefficient)
and intr... | computer science |
14,170 | Discrepancy-based Evolutionary Diversity Optimization | cs.NE | Diversity plays a crucial role in evolutionary computation. While diversity
has been mainly used to prevent the population of an evolutionary algorithm
from premature convergence, the use of evolutionary algorithms to obtain a
diverse set of solutions has gained increasing attention in recent years.
Diversity optimizat... | computer science |
14,171 | Evolution of Images with Diversity and Constraints Using a Generator
Network | cs.NE | Evolutionary search has been extensively used to generate artistic images.
Raw images have high dimensionality which makes a direct search for an image
challenging. In previous work this problem has been addressed by using compact
symbolic encodings or by constraining images with priors. Recent developments
in deep lea... | computer science |
14,172 | A Machine Learning Approach for Virtual Flow Metering and Forecasting | cs.NE | We are concerned with robust and accurate forecasting of multiphase flow
rates in wells and pipelines during oil and gas production. In practice, the
possibility to physically measure the rates is often limited; besides, it is
desirable to estimate future values of multiphase rates based on the previous
behavior of the... | computer science |
14,173 | Implementation of Neural Network and feature extraction to classify ECG
signals | cs.NE | This paper presents a suitable and efficient implementation of a feature
extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG)
signals, for detection and classification of four cardiac diseases: Sleep
Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial
Fibrillation (AF) and differen... | computer science |
14,174 | Layer-wise synapse optimization for implementing neural networks on
general neuromorphic architectures | cs.NE | Deep artificial neural networks (ANNs) can represent a wide range of complex
functions. Implementing ANNs in Von Neumann computing systems, though, incurs a
high energy cost due to the bottleneck created between CPU and memory.
Implementation on neuromorphic systems may help to reduce energy demand.
Conventional ANNs m... | computer science |
14,175 | Towards Deep Representation Learning with Genetic Programming | cs.NE | Genetic Programming (GP) is an evolutionary algorithm commonly used for
machine learning tasks. In this paper we present a method that allows GP to
transform the representation of a large-scale machine learning dataset into a
more compact representation, by means of processing features from the original
representation ... | computer science |
14,176 | Complex-valued Neural Networks with Non-parametric Activation Functions | cs.NE | Complex-valued neural networks (CVNNs) are a powerful modeling tool for
domains where data can be naturally interpreted in terms of complex numbers.
However, several analytical properties of the complex domain (e.g.,
holomorphicity) make the design of CVNNs a more challenging task than their
real counterpart. In this p... | computer science |
14,177 | Reservoir computing with simple oscillators: Virtual and real networks | cs.NE | The reservoir computing scheme is a machine learning mechanism which utilizes
the naturally occuring computational capabilities of dynamical systems. One
important subset of systems that has proven powerful both in experiments and
theory are delay-systems. In this work, we investigate the reservoir computing
performanc... | computer science |
14,178 | Improved Regularity Model-based EDA for Many-objective Optimization | cs.NE | The performance of multi-objective evolutionary algorithms deteriorates
appreciably in solving many-objective optimization problems which encompass
more than three objectives. One of the known rationales is the loss of
selection pressure which leads to the selected parents not generating promising
offspring towards Par... | computer science |
14,179 | IGD Indicator-based Evolutionary Algorithm for Many-objective
Optimization Problems | cs.NE | Inverted Generational Distance (IGD) has been widely considered as a reliable
performance indicator to concurrently quantify the convergence and diversity of
multi- and many-objective evolutionary algorithms. In this paper, an IGD
indicator-based evolutionary algorithm for solving many-objective optimization
problems (... | computer science |
14,180 | Back to Basics: Benchmarking Canonical Evolution Strategies for Playing
Atari | cs.NE | Evolution Strategies (ES) have recently been demonstrated to be a viable
alternative to reinforcement learning (RL) algorithms on a set of challenging
deep RL problems, including Atari games and MuJoCo humanoid locomotion
benchmarks. While the ES algorithms in that work belonged to the specialized
class of natural evol... | computer science |
14,181 | Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting
Evolution Direction with Archive | cs.NE | As a typical model-based evolutionary algorithm (EA), estimation of
distribution algorithm (EDA) possesses unique characteristics and has been
widely applied to global optimization. However, the common-used Gaussian EDA
(GEDA) usually suffers from premature convergence which severely limits its
search efficiency. This ... | computer science |
14,182 | Boosting Cooperative Coevolution for Large Scale Optimization with a
Fine-Grained Computation Resource Allocation Strategy | cs.NE | Cooperative coevolution (CC) has shown great potential in solving large scale
optimization problems (LSOPs). However, traditional CC algorithms often waste
part of computation resource (CR) as they equally allocate CR among all the
subproblems. The recently developed contribution-based CC (CBCC) algorithms
improve the ... | computer science |
14,183 | Surrogate Model Assisted Cooperative Coevolution for Large Scale
Optimization | cs.NE | It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original... | computer science |
14,184 | Avoiding overfitting of multilayer perceptrons by training derivatives | cs.NE | Resistance to overfitting is observed for neural networks trained with
extended backpropagation algorithm. In addition to target values, its cost
function uses derivatives of those up to the $4^{\mathrm{th}}$ order. For
common applications of neural networks, high order derivatives are not readily
available, so simpler... | computer science |
14,185 | A Global Information Based Adaptive Threshold for Grouping Large Scale
Global Optimization Problems | cs.NE | By taking the idea of divide-and-conquer, cooperative coevolution (CC)
provides a powerful architecture for large scale global optimization (LSGO)
problems, but its efficiency relies highly on the decomposition strategy. It
has been shown that differential grouping (DG) performs well on decomposing
LSGO problems by eff... | computer science |
14,186 | Exploiting the Potential of Standard Convolutional Autoencoders for
Image Restoration by Evolutionary Search | cs.NE | Researchers have applied deep neural networks to image restoration tasks, in
which they proposed various network architectures, loss functions, and training
methods. In particular, adversarial training, which is employed in recent
studies, seems to be a key ingredient to success. In this paper, we show that
simple conv... | computer science |
14,187 | A theory of sequence indexing and working memory in recurrent neural
networks | cs.NE | To accommodate structured approaches of neural computation, we propose a
class of recurrent neural networks for indexing and storing sequences of
symbols or analog data vectors. These networks with randomized input weights
and orthogonal recurrent weights implement coding principles previously
described in vector symbo... | computer science |
14,188 | Optimal localist and distributed coding of spatiotemporal spike patterns
through STDP and coincidence detection | cs.NE | Repeating spatiotemporal spike patterns exist and carry information. Here we
investigate how a single neuron can optimally signal the presence of one given
pattern (localist coding), or of either one of several patterns (distributed
coding, i.e. the neuron's response is ambiguous but the identity of the pattern
could b... | computer science |
14,189 | Enhancing Cooperative Coevolution for Large Scale Optimization by
Adaptively Constructing Surrogate Models | cs.NE | It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original... | computer science |
14,190 | Niching an Archive-based Gaussian Estimation of Distribution Algorithm
via Adaptive Clustering | cs.NE | As a model-based evolutionary algorithm, estimation of distribution algorithm
(EDA) possesses unique characteristics and has been widely applied to global
optimization. However, traditional Gaussian EDA (GEDA) may suffer from
premature convergence and has a high risk of falling into local optimum when
dealing with mult... | computer science |
14,191 | On the Effectiveness of Simple Success-Based Parameter Selection
Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark
Problems | cs.NE | Despite significant empirical and theoretically supported evidence that
non-static parameter choices can be strongly beneficial in evolutionary
computation, the question how to best adjust parameter values plays only a
marginal role in contemporary research on discrete black-box optimization. This
has led to the unsati... | computer science |
14,192 | Style Memory: Making a Classifier Network Generative | cs.NE | Deep networks have shown great performance in classification tasks. However,
the parameters learned by the classifier networks usually discard stylistic
information of the input, in favour of information strictly relevant to
classification. We introduce a network that has the capacity to do both
classification and reco... | computer science |
14,193 | Multi-objective evolution for 3D RTS Micro | cs.NE | We attack the problem of controlling teams of autonomous units during
skirmishes in real-time strategy games. Earlier work had shown promise in
evolving control algorithm parameters that lead to high performance team
behaviors similar to those favored by good human players in real-time strategy
games like Starcraft. Th... | computer science |
14,194 | An FPGA-based Massively Parallel Neuromorphic Cortex Simulator | cs.NE | This paper presents a massively parallel and scalable neuromorphic cortex
simulator designed for simulating large and structurally connected spiking
neural networks, such as complex models of various areas of the cortex. The
main novelty of this work is the abstraction of a neuromorphic architecture
into clusters repre... | computer science |
14,195 | The Surprising Creativity of Digital Evolution: A Collection of
Anecdotes from the Evolutionary Computation and Artificial Life Research
Communities | cs.NE | Biological evolution provides a creative fount of complex and subtle
adaptations, often surprising the scientists who discover them. However,
because evolution is an algorithmic process that transcends the substrate in
which it occurs, evolution's creativity is not limited to nature. Indeed, many
researchers in the fie... | computer science |
14,196 | Bit-Tactical: Exploiting Ineffectual Computations in Convolutional
Neural Networks: Which, Why, and How | cs.NE | We show that, during inference with Convolutional Neural Networks (CNNs),
more than 2x to $8x ineffectual work can be exposed if instead of targeting
those weights and activations that are zero, we target different combinations
of value stream properties. We demonstrate a practical application with
Bit-Tactical (TCL), ... | computer science |
14,197 | Enhancing Evolutionary Optimization in Uncertain Environments by
Allocating Evaluations via Multi-armed Bandit Algorithms | cs.NE | Optimization problems with uncertain fitness functions are common in the real
world, and present unique challenges for evolutionary optimization approaches.
Existing issues include excessively expensive evaluation, lack of solution
reliability, and incapability in maintaining high overall fitness during
optimization. U... | computer science |
14,198 | Enhanced Optimization with Composite Objectives and Novelty Selection | cs.NE | An important benefit of multi-objective search is that it maintains a diverse
population of candidates, which helps in deceptive problems in particular. Not
all diversity is useful, however: candidates that optimize only one objective
while ignoring others are rarely helpful. This paper proposes a solution: The
origina... | computer science |
14,199 | Conditional Activation for Diverse Neurons in Heterogeneous Networks | cs.NE | In this paper, we propose a new scheme for modelling the diverse behavior of
neurons. We introduce the conditional activation, in which a neurons activation
function is dynamically modified by a control signal. We apply this method to
recreate behavior of special neurons existing in the human auditory and visual
system... | computer science |
14,200 | Multi-objective Analysis of MAP-Elites Performance | cs.NE | In certain complex optimization tasks, it becomes necessary to use multiple
measures to characterize the performance of different algorithms. This paper
presents a method that combines ordinal effect sizes with Pareto dominance to
analyze such cases. Since the method is ordinal, it can also generalize across
different ... | computer science |
14,201 | A mullti- or many- objective evolutionary algorithm with global loop
update | cs.NE | Multi- or many-objective evolutionary algorithm- s(MOEAs), especially the
decomposition-based MOEAs have been widely concerned in recent years. The
decomposition-based MOEAs emphasize convergence and diversity in a simple model
and have made a great success in dealing with theoretical and practical multi-
or many-objec... | computer science |
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