Search results for "evolutionary computation"
showing 10 items of 113 documents
NP-completeness of the hamming salesman problem
1985
It is shown that the traveling salesman problem, where cities are bit strings with Hamming distances, is NP-complete.
Claws contained in all n-tournaments
1993
Abstract We prove that any claw of order n with degree d≤ 3 8 n is n-unavoidable, which means that any tournament of order n contains it as a subdigraph. A simple corollary is that any tournament has a directed Hamiltonian path.
On second maximal subgroups of Sylow subgroups of finite groups
2011
Abstract Finite groups in which the second maximal subgroups of the Sylow p -subgroups, p a fixed prime, cover or avoid the chief factors of some of its chief series are completely classified.
Training Artificial Neural Networks With Improved Particle Swarm Optimization
2020
Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is util…
Evolutionary White-Box Software Test with the EvoTest Framework: A Progress Report
2009
Evolutionary white-box software testing has been extensively researched but is not yet applied in industry. In order to investigate the reasons for this, we evaluated a prototype version of a tool, representing the state-of-the-art for evolutionary structural testing, which is targeted at industrial use. The focus was on the applicability of the structural test tool in the industrial context and not on assessment of the test cases generated. Four case studies, each consisting of an embedded software module from the automotive industry implemented in the C language, were evaluated with the tool. The case studies had to be customized to cope with the limitations of the tool and in all, test c…
Structural Health Monitoring Procedure for Composite Structures through the use of Artificial Neural Networks
2015
In this paper different architectures of Artificial Neural Networks (ANNs) for structural damage detection are studied. The main objective is to investigate an ANN able to detect and localize damage without any prior knowledge on its characteristics so as to serve as a real-time data processor for Structural Health Monitoring (SHM) systems. Two different architectures are studied: the standard feed-forward Multi Layer Perceptron (MLP) and the Radial Basis Function (RBF) ANNs. The training data are given, in terms of a Damage Index ℑD, properly defined using a piezoelectric sensor signal output to obtain suitable information on the damage position and dimensions. The electromechanical respon…
Velocity sensorless control of a PMSM actuator directly driven an uncertain two-mass system using RKF tuned with an evolutionary algorithm
2010
This paper proposes a solution to tune an observer keeping robust closed loop performances for the sensorless motion control of an uncertain mechanical load directly driven by a PMSM through a flexible axis. An evolutionary algorithm optimizes the observers degrees of freedom. Experiments show that performances are effectively maintained.
Forecasting Exchange Rates Volatilities Using Artificial Neural Networks
2000
This paper employs Artificial Neural Networks to forecast volatilities of the exchange rates of six currencies against the Spanish peseta. First, we propose to use ANN as an alternative to parametric volatility models, then, we employ them as an aggregation procedure to build hybrid models. Though we do not find a systematic superiority of ANN, our results suggest that they are an interesting alternative to classical parametric volatility models.
Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)
2021
Artificial neural network (ANN) is a supervised learning algorithm, where parameters are learned by several back-and-forth iterations of passing the inputs through the network, comparing the output with the expected labels, and correcting the parameters. Inspired by a recent work of Boer and Kramer (2020), we investigate a different problem: Suppose an observer can view how the ANN parameters evolve over many iterations, but the dataset is oblivious to him. For instance, this can be an adversary eavesdropping on a multi-party computation of an ANN parameters (where intermediate parameters are leaked). Can he form a system of equations, and solve it to recover the dataset?
Quantum autoencoders via quantum adders with genetic algorithms
2017
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoe…