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13,702 | Linear Antenna Array with Suppressed Sidelobe and Sideband Levels using
Time Modulation | cs.NE | In this paper, the goal is to achieve an ultra low sidelobe level (SLL) and
sideband levels (SBL) of a time modulated linear antenna array. The approach
followed here is not to give fixed level of excitation to the elements of an
array, but to change it dynamically with time. The excitation levels of the
different arra... | computer science |
13,703 | Genetic Algorithm for Designing a Convenient Facility Layout for a
Circular Flow Path | cs.NE | In this paper, we present a heuristic for designing facility layouts that are
convenient for designing a unidirectional loop for material handling. We use
genetic algorithm where the objective function and crossover and mutation
operators have all been designed specifically for this purpose. Our design is
made under fl... | computer science |
13,704 | Memory Capacity of a Random Neural Network | cs.NE | This paper considers the problem of information capacity of a random neural
network. The network is represented by matrices that are square and
symmetrical. The matrices have a weight which determines the highest and lowest
possible value found in the matrix. The examined matrices are randomly
generated and analyzed by... | computer science |
13,705 | A Bayesian Interpretation of the Particle Swarm Optimization and Its
Kernel Extension | cs.NE | Particle swarm optimization is a popular method for solving difficult
optimization problems. There have been attempts to formulate the method in
formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with
the aim to achieve more generality and explain the practical behavior of the
method. Here we pre... | computer science |
13,706 | Storing cycles in Hopfield-type networks with pseudoinverse learning
rule: admissibility and network topology | cs.NE | Cyclic patterns of neuronal activity are ubiquitous in animal nervous
systems, and partially responsible for generating and controlling rhythmic
movements such as locomotion, respiration, swallowing and so on. Clarifying the
role of the network connectivities for generating cyclic patterns is
fundamental for understand... | computer science |
13,707 | A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling
Problems | cs.NE | Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop
Scheduling Problem is categorized under popular scheduling problems. In this
research work, Bacterial Foraging Optimization was hybridized with Ant Colony
Optimization and a new technique Hybrid Bacterial Foraging Optimization for
solving Job S... | computer science |
13,708 | Ecosystem-Oriented Distributed Evolutionary Computing | cs.NE | We create a novel optimisation technique inspired by natural ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
genes which are distributed in a peer-to-peer network, operating continuously
in time; this process feeds a second optimisation based on evolutionary
computing that ope... | computer science |
13,709 | Erratum: Simplified Drift Analysis for Proving Lower Bounds in
Evolutionary Computation | cs.NE | This erratum points out an error in the simplified drift theorem (SDT)
[Algorithmica 59(3), 369-386, 2011]. It is also shown that a minor modification
of one of its conditions is sufficient to establish a valid result. In many
respects, the new theorem is more general than before. We no longer assume a
Markov process n... | computer science |
13,710 | Comparing various regression methods on ensemble strategies in
differential evolution | cs.NE | Differential evolution possesses a multitude of various strategies for
generating new trial solutions. Unfortunately, the best strategy is not known
in advance. Moreover, this strategy usually depends on the problem to be
solved. This paper suggests using various regression methods (like random
forest, extremely random... | computer science |
13,711 | General Drift Analysis with Tail Bounds | cs.NE | Drift analysis is one of the state-of-the-art techniques for the runtime
analysis of randomized search heuristics (RSHs) such as evolutionary algorithms
(EAs), simulated annealing etc. The vast majority of existing drift theorems
yield bounds on the expected value of the hitting time for a target state,
e.g., the set o... | computer science |
13,712 | Non-Elitist Genetic Algorithm as a Local Search Method | cs.NE | Sufficient conditions are found under which the iterated non-elitist genetic
algorithm with tournament selection first visits a local optimum in
polynomially bounded time on average. It is shown that these conditions are
satisfied on a class of problems with guaranteed local optima (GLO) if
appropriate parameters of th... | computer science |
13,713 | A Brief Review of Nature-Inspired Algorithms for Optimization | cs.NE | Swarm intelligence and bio-inspired algorithms form a hot topic in the
developments of new algorithms inspired by nature. These nature-inspired
metaheuristic algorithms can be based on swarm intelligence, biological
systems, physical and chemical systems. Therefore, these algorithms can be
called swarm-intelligence-bas... | computer science |
13,714 | The Fitness Level Method with Tail Bounds | cs.NE | The fitness-level method, also called the method of f-based partitions, is an
intuitive and widely used technique for the running time analysis of randomized
search heuristics. It was originally defined to prove upper and lower bounds on
the expected running time. Recently, upper tail bounds were added to the
technique... | computer science |
13,715 | A new approach in dynamic traveling salesman problem: a hybrid of ant
colony optimization and descending gradient | cs.NE | Nowadays swarm intelligence-based algorithms are being used widely to
optimize the dynamic traveling salesman problem (DTSP). In this paper, we have
used mixed method of Ant Colony Optimization (AOC)and gradient descent to
optimize DTSP which differs with ACO algorithm in evaporation rate and
innovative data. This appr... | computer science |
13,716 | Neural Network Capacity for Multilevel Inputs | cs.NE | This paper examines the memory capacity of generalized neural networks.
Hopfield networks trained with a variety of learning techniques are
investigated for their capacity both for binary and non-binary alphabets. It is
shown that the capacity can be much increased when multilevel inputs are used.
New learning strategi... | computer science |
13,717 | The Novel Approach of Adaptive Twin Probability for Genetic Algorithm | cs.NE | The performance of GA is measured and analyzed in terms of its performance
parameters against variations in its genetic operators and associated
parameters. Since last four decades huge numbers of researchers have been
working on the performance of GA and its enhancement. This earlier research
work on analyzing the per... | computer science |
13,718 | Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New
Nature Inspired Discrete Meta-Heuristics | cs.NE | Many real world problems are NP-Hard problems are a very large part of them
can be represented as graph based problems. This makes graph theory a very
important and prevalent field of study. In this work a new bio-inspired
meta-heuristics called Green Heron Swarm Optimization (GHOSA) Algorithm is
being introduced which... | computer science |
13,719 | An Adaptive Amoeba Algorithm for Shortest Path Tree Computation in
Dynamic Graphs | cs.NE | This paper presents an adaptive amoeba algorithm to address the shortest path
tree (SPT) problem in dynamic graphs. In dynamic graphs, the edge weight
updates consists of three categories: edge weight increases, edge weight
decreases, the mixture of them. Existing work on this problem solve this issue
through analyzing... | computer science |
13,720 | Q-Gaussian Swarm Quantum Particle Intelligence on Predicting Global
Minimum of Potential Energy Function | cs.NE | We present a newly developed -Gaussian Swarm Quantum-like Particle
Optimization (q-GSQPO) algorithm to determine the global minimum of the
potential energy function. Swarm Quantum-like Particle Optimization (SQPO)
algorithms have been derived using different attractive potential fields to
represent swarm particles movi... | computer science |
13,721 | Polyhedrons and Perceptrons Are Functionally Equivalent | cs.NE | Mathematical definitions of polyhedrons and perceptron networks are
discussed. The formalization of polyhedrons is done in a rather traditional
way. For networks, previously proposed systems are developed. Perceptron
networks in disjunctive normal form (DNF) and conjunctive normal forms (CNF)
are introduced. The main t... | computer science |
13,722 | How Santa Fe Ants Evolve | cs.NE | The Santa Fe Ant model problem has been extensively used to investigate, test
and evaluate Evolutionary Computing systems and methods over the past two
decades. There is however no literature on its program structures that are
systematically used for fitness improvement, the geometries of those structures
and their dyn... | computer science |
13,723 | A preliminary survey on optimized multiobjective metaheuristic methods
for data clustering using evolutionary approaches | cs.NE | The present survey provides the state-of-the-art of research, copiously
devoted to Evolutionary Approach (EAs) for clustering exemplified with a
diversity of evolutionary computations. The Survey provides a nomenclature that
highlights some aspects that are very important in the context of evolutionary
data clustering.... | computer science |
13,724 | CACO : Competitive Ant Colony Optimization, A Nature-Inspired
Metaheuristic For Large-Scale Global Optimization | cs.NE | Large-scale problems are nonlinear problems that need metaheuristics, or
global optimization algorithms. This paper reviews nature-inspired
metaheuristics, then it introduces a framework named Competitive Ant Colony
Optimization inspired by the chemical communications among insects. Then a case
study is presented to in... | computer science |
13,725 | A natural-inspired optimization machine based on the annual migration of
salmons in nature | cs.NE | Bio inspiration is a branch of artificial simulation science that shows
pervasive contributions to variety of engineering fields such as automate
pattern recognition, systematic fault detection and applied optimization. In
this paper, a new metaheuristic optimizing algorithm that is the simulation of
The Great Salmon R... | computer science |
13,726 | An introduction to synchronous self-learning Pareto strategy | cs.NE | In last decades optimization and control of complex systems that possessed
various conflicted objectives simultaneously attracted an incremental interest
of scientists. This is because of the vast applications of these systems in
various fields of real life engineering phenomena that are generally multi
modal, non conv... | computer science |
13,727 | Autonomous Quantum Perceptron Neural Network | cs.NE | Recently, with the rapid development of technology, there are a lot of
applications require to achieve low-cost learning. However the computational
power of classical artificial neural networks, they are not capable to provide
low-cost learning. In contrast, quantum neural networks may be representing a
good computatio... | computer science |
13,728 | My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 | cs.NE | Deep Learning has attracted significant attention in recent years. Here I
present a brief overview of my first Deep Learner of 1991, and its historic
context, with a timeline of Deep Learning highlights. | computer science |
13,729 | Optimal parameter selection for unsupervised neural network using
genetic algorithm | cs.NE | K-means Fast Learning Artificial Neural Network (K-FLANN) is an unsupervised
neural network requires two parameters: tolerance and vigilance. Best
Clustering results are feasible only by finest parameters specified to the
neural network. Selecting optimal values for these parameters is a major
problem. To solve this is... | computer science |
13,730 | A comprehensive review of firefly algorithms | cs.NE | The firefly algorithm has become an increasingly important tool of Swarm
Intelligence that has been applied in almost all areas of optimization, as well
as engineering practice. Many problems from various areas have been
successfully solved using the firefly algorithm and its variants. In order to
use the algorithm to ... | computer science |
13,731 | From ADP to the Brain: Foundations, Roadmap, Challenges and Research
Priorities | cs.NE | This paper defines and discusses Mouse Level Computational Intelligence
(MLCI) as a grand challenge for the coming century. It provides a specific
roadmap to reach that target, citing relevant work and review papers and
discussing the relation to funding priorities in two NSF funding activities:
the ongoing Energy, Pow... | computer science |
13,732 | A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack
Problem | cs.NE | The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good sol... | computer science |
13,733 | A Theoretical Assessment of Solution Quality in Evolutionary Algorithms
for the Knapsack Problem | cs.NE | Evolutionary algorithms are well suited for solving the knapsack problem.
Some empirical studies claim that evolutionary algorithms can produce good
solutions to the 0-1 knapsack problem. Nonetheless, few rigorous investigations
address the quality of solutions that evolutionary algorithms may produce for
the knapsack ... | computer science |
13,734 | An effective AHP-based metaheuristic approach to solve supplier
selection problem | cs.NE | The supplier selection problem is based on electing the best supplier from a
group of pre-specified candidates, is identified as a Multi Criteria Decision
Making (MCDM), is proportionately significant in terms of qualitative and
quantitative attributes. It is a fundamental issue to achieve a trade-off
between such quan... | computer science |
13,735 | Influence of the learning method in the performance of feedforward
neural networks when the activity of neurons is modified | cs.NE | A method that allows us to give a different treatment to any neuron inside
feedforward neural networks is presented. The algorithm has been implemented
with two very different learning methods: a standard Back-propagation (BP)
procedure and an evolutionary algorithm. First, we have demonstrated that the
EA training met... | computer science |
13,736 | A Computationally Efficient Limited Memory CMA-ES for Large Scale
Optimization | cs.NE | We propose a computationally efficient limited memory Covariance Matrix
Adaptation Evolution Strategy for large scale optimization, which we call the
LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for
numerical optimization of non-linear, non-convex optimization problems in
continuous domain. Inspi... | computer science |
13,737 | Codynamic Fitness Landscapes of Coevolutionary Minimal Substrates | cs.NE | Coevolutionary minimal substrates are simple and abstract models that allow
studying the relationships and codynamics between objective and subjective
fitness. Using these models an approach is presented for defining and analyzing
fitness landscapes of coevolutionary problems. We devise similarity measures of
codynamic... | computer science |
13,738 | Input anticipating critical reservoirs show power law forgetting of
unexpected input events | cs.NE | Usually, reservoir computing shows an exponential memory decay. This paper
investigates under which circumstances echo state networks can show a power law
forgetting. That means traces of earlier events can be found in the reservoir
for very long time spans. Such a setting requires critical connectivity exactly
at the ... | computer science |
13,739 | A semantic network-based evolutionary algorithm for computational
creativity | cs.NE | We introduce a novel evolutionary algorithm (EA) with a semantic
network-based representation. For enabling this, we establish new formulations
of EA variation operators, crossover and mutation, that we adapt to work on
semantic networks. The algorithm employs commonsense reasoning to ensure all
operations preserve the... | computer science |
13,740 | Information Transfer in Swarms with Leaders | cs.NE | Swarm dynamics is the study of collections of agents that interact with one
another without central control. In natural systems, insects, birds, fish and
other large mammals function in larger units to increase the overall fitness of
the individuals. Their behavior is coordinated through local interactions to
enhance m... | computer science |
13,741 | Navigating Robot Swarms Using Collective Intelligence Learned from
Golden Shiner Fish | cs.NE | Navigating networked robot swarms often requires knowing where to go, sensing
the environment, and path-planning based on the destination and barriers in the
environment. Such a process is computationally intensive. Moreover, as the
network scales up, the computational load increases quadratically, or even
exponentiall... | computer science |
13,742 | Dispersion and Line Formation in Artificial Swarm Intelligence | cs.NE | One of the major motifs in collective or swarm intelligence is that, even
though individuals follow simple rules, the resulting global behavior can be
complex and intelligent. In artificial swarm systems, such as swarm robots, the
goal is to use systems that are as simple and cheap as possible, deploy many of
them, and... | computer science |
13,743 | Block matching algorithm for motion estimation based on Artificial Bee
Colony (ABC) | cs.NE | Block matching (BM) motion estimation plays a very important role in video
coding. In a BM approach, image frames in a video sequence are divided into
blocks. For each block in the current frame, the best matching block is
identified inside a region of the previous frame, aiming to minimize the sum of
absolute differen... | computer science |
13,744 | Supervised learning in Spiking Neural Networks with Limited Precision:
SNN/LP | cs.NE | A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural
Networks. This novel algorithm uses limited precision for both synaptic weights
and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for
the supervised training. The results are comparable or better than previously
publish... | computer science |
13,745 | A Proposed Infrastructure for Adding Online Interaction to Any
Evolutionary Domain | cs.NE | To address the difficulty of creating online collaborative evolutionary
systems, this paper presents a new prototype library called Worldwide
Infrastructure for Neuroevolution (WIN) and its accompanying site WIN Online
(http://winark.org/). The WIN library is a collection of software packages
built on top of Node.js th... | computer science |
13,746 | Charge Scheduling of an Energy Storage System under Time-of-use Pricing
and a Demand Charge | cs.NE | A real-coded genetic algorithm is used to schedule the charging of an energy
storage system (ESS), operated in tandem with renewable power by an electricity
consumer who is subject to time-of-use pricing and a demand charge. Simulations
based on load and generation profiles of typical residential customers show
that an... | computer science |
13,747 | Uncertainty And Evolutionary Optimization: A Novel Approach | cs.NE | Evolutionary algorithms (EA) have been widely accepted as efficient solvers
for complex real world optimization problems, including engineering
optimization. However, real world optimization problems often involve uncertain
environment including noisy and/or dynamic environments, which pose major
challenges to EA-based... | computer science |
13,748 | Global optimization using Lévy flights | cs.NE | This paper studies a class of enhanced diffusion processes in which random
walkers perform L\'evy flights and apply it for global optimization. L\'evy
flights offer controlled balance between exploitation and exploration. We
develop four optimization algorithms based on such properties. We compare new
algorithms with t... | computer science |
13,749 | Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm | cs.NE | Artificial Bee Colony (ABC) is a distinguished optimization strategy that can
resolve nonlinear and multifaceted problems. It is comparatively a
straightforward and modern population based probabilistic approach for
comprehensive optimization. In the vein of the other population based
algorithms, ABC is moreover comput... | computer science |
13,750 | Deep Recurrent Neural Networks for Time Series Prediction | cs.NE | Ability of deep networks to extract high level features and of recurrent
networks to perform time-series inference have been studied. In view of
universality of one hidden layer network at approximating functions under weak
constraints, the benefit of multiple layers is to enlarge the space of
dynamical systems approxi... | computer science |
13,751 | Trainable and Dynamic Computing: Error Backpropagation through Physical
Media | cs.NE | Machine learning algorithms, and more in particular neural networks, arguably
experience a revolution in terms of performance. Currently, the best systems we
have for speech recognition, computer vision and similar problems are based on
neural networks, trained using the half-century old backpropagation algorithm.
Desp... | computer science |
13,752 | A Numerical Optimization Algorithm Inspired by the Strawberry Plant | cs.NE | This paper proposes a new numerical optimization algorithm inspired by the
strawberry plant for solving complicated engineering problems. Plants like
strawberry develop both runners and roots for propagation and search for water
resources and minerals. In these plants, runners and roots can be thought of as
tools for g... | computer science |
13,753 | A CUDA-Based Real Parameter Optimization Benchmark | cs.NE | Benchmarking is key for developing and comparing optimization algorithms. In
this paper, a CUDA-based real parameter optimization benchmark (cuROB) is
introduced. Test functions of diverse properties are included within cuROB and
implemented efficiently with CUDA. Speedup of one order of magnitude can be
achieved in co... | computer science |
13,754 | Towards a Calculus of Echo State Networks | cs.NE | Reservoir computing is a recent trend in neural networks which uses the
dynamical perturbations on the phase space of a system to compute a desired
target function. We present how one can formulate an expectation of system
performance in a simple class of reservoir computing called echo state
networks. In contrast with... | computer science |
13,755 | Storing sequences in binary tournament-based neural networks | cs.NE | An extension to a recently introduced architecture of clique-based neural
networks is presented. This extension makes it possible to store sequences with
high efficiency. To obtain this property, network connections are provided with
orientation and with flexible redundancy carried by both spatial and temporal
redundan... | computer science |
13,756 | Tunably Rugged Landscapes with Known Maximum and Minimum | cs.NE | We propose NM landscapes as a new class of tunably rugged benchmark problems.
NM landscapes are well-defined on alphabets of any arity, including both
discrete and real-valued alphabets, include epistasis in a natural and
transparent manner, are proven to have known value and location of the global
maximum and, with so... | computer science |
13,757 | An Experimental Study of Adaptive Control for Evolutionary Algorithms | cs.NE | The balance of exploration versus exploitation (EvE) is a key issue on
evolutionary computation. In this paper we will investigate how an adaptive
controller aimed to perform Operator Selection can be used to dynamically
manage the EvE balance required by the search, showing that the search
strategies determined by thi... | computer science |
13,758 | Recurrent Neural Network Regularization | cs.NE | We present a simple regularization technique for Recurrent Neural Networks
(RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful
technique for regularizing neural networks, does not work well with RNNs and
LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show
that it su... | computer science |
13,759 | Particle Swarm Optimized Fuzzy Controller for Indirect Vector Control of
Multilevel Inverter Fed Induction Motor | cs.NE | The Particle Swarm Optimized (PSO) fuzzy controller has been proposed for
indirect vector control of induction motor. In this proposed scheme a Neutral
Point Clamped (NPC) multilevel inverter is used and hysteresis current control
technique has been adopted for switching the IGBTs. A Mamdani type fuzzy
controller is us... | computer science |
13,760 | eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification | cs.NE | Ant Colony Optimization (ACO) has been applied in supervised learning in
order to induce classification rules as well as decision trees, named
Ant-Miners. Although these are competitive classifiers, the stability of these
classifiers is an important concern that owes to their stochastic nature. In
this paper, to addres... | computer science |
13,761 | An improved genetic algorithm with a local optimization strategy and an
extra mutation level for solving traveling salesman problem | cs.NE | The Traveling salesman problem (TSP) is proved to be NP-complete in most
cases. The genetic algorithm (GA) is one of the most useful algorithms for
solving this problem. In this paper a conventional GA is compared with an
improved hybrid GA in solving TSP. The improved or hybrid GA consist of
conventional GA and two lo... | computer science |
13,762 | An OvS-MultiObjective Algorithm Approach for Lane Reversal Problem | cs.NE | The lane reversal has proven to be a useful method to mitigate traffic
congestion during rush hour or in case of specific events that affect high
traffic volumes. In this work we propose a methodology that is placed within
optimization via Simulation, by means of which a multi-objective genetic
algorithm and simulation... | computer science |
13,763 | Selection of Most Appropriate Backpropagation Training Algorithm in Data
Pattern Recognition | cs.NE | There are several training algorithms for backpropagation method in neural
network. Not all of these algorithms have the same accuracy level demonstrated
through the percentage level of suitability in recognizing patterns in the
data. In this research tested 12 training algorithms specifically in recognize
data pattern... | computer science |
13,764 | A High-Level Model of Neocortical Feedback Based on an Event Window
Segmentation Algorithm | cs.NE | The author previously presented an event window segmentation (EWS) algorithm
[5] that uses purely statistical methods to learn to recognize recurring
patterns in an input stream of events. In the following discussion, the EWS
algorithm is first extended to make predictions about future events. Next, this
extended algor... | computer science |
13,765 | An Analysis on Selection for High-Resolution Approximations in
Many-Objective Optimization | cs.NE | This work studies the behavior of three elitist multi- and many-objective
evolutionary algorithms generating a high-resolution approximation of the
Pareto optimal set. Several search-assessment indicators are defined to trace
the dynamics of survival selection and measure the ability to simultaneously
keep optimal solu... | computer science |
13,766 | Combating Corrupt Messages in Sparse Clustered Associative Memories | cs.NE | In this paper we analyze and extend the neural network based associative
memory proposed by Gripon and Berrou. This associative memory resembles the
celebrated Willshaw model with an added partite cluster structure. In the
literature, two retrieving schemes have been proposed for the network dynamics,
namely sum-of-sum... | computer science |
13,767 | Reservoir Computing using Cellular Automata | cs.NE | We introduce a novel framework of reservoir computing. Cellular automaton is
used as the reservoir of dynamical systems. Input is randomly projected onto
the initial conditions of automaton cells and nonlinear computation is
performed on the input via application of a rule in the automaton for a period
of time. The evo... | computer science |
13,768 | Enhanced Multiobjective Evolutionary Algorithm based on Decomposition
for Solving the Unit Commitment Problem | cs.NE | The unit commitment (UC) problem is a nonlinear, high-dimensional, highly
constrained, mixed-integer power system optimization problem and is generally
solved in the literature considering minimizing the system operation cost as
the only objective. However, due to increasing environmental concerns, the
recent attention... | computer science |
13,769 | Evolvability signatures of generative encodings: beyond standard
performance benchmarks | cs.NE | Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a speci... | computer science |
13,770 | Neural Turing Machines | cs.NE | We extend the capabilities of neural networks by coupling them to external
memory resources, which they can interact with by attentional processes. The
combined system is analogous to a Turing Machine or Von Neumann architecture
but is differentiable end-to-end, allowing it to be efficiently trained with
gradient desce... | computer science |
13,771 | Improvement of PSO algorithm by memory based gradient search -
application in inventory management | cs.NE | Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cann... | computer science |
13,772 | Neuroevolution in Games: State of the Art and Open Challenges | cs.NE | This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen... | computer science |
13,773 | Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex
Algorithm Applied to Optimization of Photovoltaic Cells | cs.NE | A new hybridization of the Cuckoo Search (CS) is developed and applied to
optimize multi-cell solar systems; namely multi-junction and split spectrum
cells. The new approach consists of combining the CS with the Nelder-Mead
method. More precisely, instead of using single solutions as nests for the CS,
we use the concep... | computer science |
13,774 | Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic
Operator | cs.NE | This paper research review Ant colony optimization (ACO) and Genetic
Algorithm (GA), both are two powerful meta-heuristics. This paper explains some
major defects of these two algorithm at first then proposes a new model for ACO
in which, artificial ants use a quick genetic operator and accelerate their
actions in sele... | computer science |
13,775 | Using Ants as a Genetic Crossover Operator in GLS to Solve STSP | cs.NE | Ant Colony Algorithm (ACA) and Genetic Local Search (GLS) are two
optimization algorithms that have been successfully applied to the Traveling
Salesman Problem (TSP). In this paper we define new crossover operator then
redefine ACAs ants as operate according to defined crossover operator then put
forward our GLS that u... | computer science |
13,776 | Diversity Handling In Evolutionary Landscape | cs.NE | The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population. Maintaining an optimal level of
diversity in the EA population is imperative to ensure that progress of the EA
search is unhindered by premature convergence to suboptimal solutions. Clearer
understandi... | computer science |
13,777 | Application of Multi-core Parallel Programming to a Combination of Ant
Colony Optimization and Genetic Algorithm | cs.NE | This Paper will deal with a combination of Ant Colony and Genetic Programming
Algorithm to optimize Travelling Salesmen problem (NP-Hard). However, the
complexity of the algorithm requires considerable computational time and
resources. Parallel implementation can reduce the computational time. In this
paper, emphasis i... | computer science |
13,778 | Model of Interaction between Learning and Evolution | cs.NE | The model of interaction between learning and evolutionary optimization is
designed and investigated. The evolving population of modeled organisms is
considered. The mechanism of the genetic assimilation of the acquired features
during a number of generations of Darwinian evolution is studied. It is shown
that the gene... | computer science |
13,779 | Echo State Condition at the Critical Point | cs.NE | Recurrent networks with transfer functions that fulfill the Lipschitz
continuity with K=1 may be echo state networks if certain limitations on the
recurrent connectivity are applied. It has been shown that it is sufficient if
the largest singular value of the recurrent connectivity is smaller than 1. The
main achieveme... | computer science |
13,780 | Scalability of using Restricted Boltzmann Machines for Combinatorial
Optimization | cs.NE | Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Restricted Boltzmann
Machines (RBMs) are generative neural networks with these desired properties.
We integrate an RBM into an EDA and evaluate the performance of this system in
solving combinat... | computer science |
13,781 | Seeding the Initial Population of Multi-Objective Evolutionary
Algorithms: A Computational Study | cs.NE | Most experimental studies initialize the population of evolutionary
algorithms with random genotypes. In practice, however, optimizers are
typically seeded with good candidate solutions either previously known or
created according to some problem-specific method. This "seeding" has been
studied extensively for single-o... | computer science |
13,782 | Learning Precise Spike Train to Spike Train Transformations in
Multilayer Feedforward Neuronal Networks | cs.NE | We derive a synaptic weight update rule for learning temporally precise spike
train to spike train transformations in multilayer feedforward networks of
spiking neurons. The framework, aimed at seamlessly generalizing error
backpropagation to the deterministic spiking neuron setting, is based strictly
on spike timing a... | computer science |
13,783 | Sparse, guided feature connections in an Abstract Deep Network | cs.NE | We present a technique for developing a network of re-used features, where
the topology is formed using a coarse learning method, that allows
gradient-descent fine tuning, known as an Abstract Deep Network (ADN). New
features are built based on observed co-occurrences, and the network is
maintained using a selection pr... | computer science |
13,784 | Analysis of Optimal Recombination in Genetic Algorithm for a Scheduling
Problem with Setups | cs.NE | In this paper, we perform an experimental study of optimal recombination
operator for makespan minimization problem on single machine with
sequence-dependent setup times ($1|s_{vu}|C_{\max}$). The computational
experiment on benchmark problems from TSPLIB library indicates practical
applicability of optimal recombinati... | computer science |
13,785 | The Computational Theory of Intelligence: Applications to Genetic
Programming and Turing Machines | cs.NE | In this paper, we continue the efforts of the Computational Theory of
Intelligence (CTI) by extending concepts to include computational processes in
terms of Genetic Algorithms (GA's) and Turing Machines (TM's). Active, Passive,
and Hybrid Computational Intelligence processes are also introduced and
discussed. We consi... | computer science |
13,786 | Classifier with Hierarchical Topographical Maps as Internal
Representation | cs.NE | In this study we want to connect our previously proposed context-relevant
topographical maps with the deep learning community. Our architecture is a
classifier with hidden layers that are hierarchical two-dimensional
topographical maps. These maps differ from the conventional self-organizing
maps in that their organiza... | computer science |
13,787 | Improved Parameter Identification Method Based on Moving Rate | cs.NE | To improve the problem that the parameter identification for fuzzy neural
network has many time complexities in calculating, an improved T-S fuzzy
inference method and an parameter identification method for fuzzy neural
network are proposed. It mainly includes three parts. First, improved fuzzy
inference method based o... | computer science |
13,788 | An Experimental Analysis of the Echo State Network Initialization Using
the Particle Swarm Optimization | cs.NE | This article introduces a robust hybrid method for solving supervised
learning tasks, which uses the Echo State Network (ESN) model and the Particle
Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with
the hidden-hidden weights fixed in the learning process. The recurrent part of
the network st... | computer science |
13,789 | Introduction and Ranking Results of the ICSI 2014 Competition on Single
Objective Optimization | cs.NE | This technical report includes the introduction and ranking results of the
ICSI 2014 Competition on Single Objective Optimization. | computer science |
13,790 | A New Repair Operator for Multi-objective Evolutionary Algorithm in
Constrained Optimization Problems | cs.NE | In this paper, we design a set of multi-objective constrained optimization
problems (MCOPs) and propose a new repair operator to address them. The
proposed repair operator is used to fix the solutions that violate the box
constraints. More specifically, it employs a reversed correction strategy that
can effectively avo... | computer science |
13,791 | Study of Some Recent Crossovers Effects on Speed and Accuracy of Genetic
Algorithm, Using Symmetric Travelling Salesman Problem | cs.NE | The Travelling Salesman Problem (TSP) is one of the most famous optimization
problems. The Genetic Algorithm (GA) is one of metaheuristics that have been
applied to TSP. The Crossover and mutation operators are two important elements
of GA. There are many TSP solver crossover operators. In this paper, we state
implemen... | computer science |
13,792 | Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th
Rule in Discrete Settings | cs.NE | While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influen... | computer science |
13,793 | Feasibility Preserving Constraint-Handling Strategies for Real Parameter
Evolutionary Optimization | cs.NE | Evolutionary Algorithms (EAs) are being routinely applied for a variety of
optimization tasks, and real-parameter optimization in the presence of
constraints is one such important area. During constrained optimization EAs
often create solutions that fall outside the feasible region; hence a viable
constraint- handling ... | computer science |
13,794 | Multi-swarm PSO algorithm for the Quadratic Assignment Problem: a
massive parallel implementation on the OpenCL platform | cs.NE | This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment
Problem (QAP) implemented on OpenCL platform. Our work was motivated by results
of time efficiency tests performed for single-swarm algorithm implementation
that showed clearly that the benefits of a parallel execution platform can be
fully ex... | computer science |
13,795 | Honeybees-inspired heuristic algorithms for numerical optimisation | cs.NE | Swarm intelligence is all about developing collective behaviours to solve
complex, ill-structured and large-scale problems. Efficiency in collective
behaviours depends on how to harmonise the individual contributions so that a
complementary collective effort can be achieved to offer a useful solution. The
main points i... | computer science |
13,796 | Rounding Methods for Neural Networks with Low Resolution Synaptic
Weights | cs.NE | Neural network algorithms simulated on standard computing platforms typically
make use of high resolution weights, with floating-point notation. However, for
dedicated hardware implementations of such algorithms, fixed-point synaptic
weights with low resolution are preferable. The basic approach of reducing the
resolut... | computer science |
13,797 | First Steps Towards a Runtime Comparison of Natural and Artificial
Evolution | cs.NE | Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse their runtime on many
illustrative problems. Here we apply this theory to a simple model of natural
evolution. In ... | computer science |
13,798 | When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization | cs.NE | It has been shown in the past that a multistart hillclimbing strategy
compares favourably to a standard genetic algorithm with respect to solving
instances of the multimodal problem generator. We extend that work and verify
if the utilization of diversity preservation techniques in the genetic
algorithm changes the out... | computer science |
13,799 | Optimal Convergence Rate in Feed Forward Neural Networks using HJB
Equation | cs.NE | A control theoretic approach is presented in this paper for both batch and
instantaneous updates of weights in feed-forward neural networks. The popular
Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal
weight update law. The remarkable contribution in this paper is that closed
form solutions ... | computer science |
13,800 | Toward Smart Power Grids: Communication Network Design for Power Grids
Synchronization | cs.NE | In smart power grids, keeping the synchronicity of generators and the
corresponding controls is of great importance. To do so, a simple model is
employed in terms of swing equation to represent the interactions among
dynamics of generators and feedback control. In case of having a communication
network available, the c... | computer science |
13,801 | Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation
Scheme | cs.NE | In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed
in order to find the best optimized multi-parameter path between two desired
points in regions. This algorithm recognizes paths, according to user desired
parameters using electronic maps. The proposed algorithm is a combination of A*
and ants... | computer science |
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