Search results for "evolutionary computation"

showing 10 items of 113 documents

Contextual neural-network based spectrum prediction for cognitive radio

2015

Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.

Cognitive modelComputational modelArtificial neural networkspectrum sensingbusiness.industryTime delay neural networkComputer scienceComputer Science::Neural and Evolutionary Computationartificial intelligenceCognitive networkMachine learningcomputer.software_genrecontextual predictionCognitive radioMultilayer perceptron5G communicationcontextual processingWirelessArtificial intelligencebusinesscomputer2015 Fourth International Conference on Future Generation Communication Technology (FGCT)
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SIMULATING THE NUMBER OF ROMANIAN`S PUPILS AND STUDENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES AND COUNTRY`S ECONOMIC ESTATE

2012

The authors present the result of a research that uses artificial neural networks in order to simulate the number of Romanian`s pupils and students (PAS), considering the country`s economic situation. The objective is to determine a better method to forecast the Romanian`s PAS considering its nonlinear behaviour. Also the ANN simulation offers an image about how inputs influence the PAS. In conclusion, the use of the ANN is considered a success and the authors determine the possibility that ANN research application be extended to other countries or even to the European zone.

Computer Science::Neural and Evolutionary ComputationRevista economica
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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?

FOS: Computer and information sciencesComputer Science - Machine LearningComputingMethodologies_PATTERNRECOGNITIONComputer Science - Cryptography and SecurityComputer Science::Neural and Evolutionary ComputationFOS: MathematicsNumerical Analysis (math.NA)Mathematics - Numerical AnalysisCryptography and Security (cs.CR)Computer Science::DatabasesMachine Learning (cs.LG)Computer Science::Cryptography and Security
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Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies

2018

We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…

Pareto optimalityMathematical optimizationComputer science0211 other engineering and technologiesEvolutionary algorithm02 engineering and technologyexpensive optimizationMulti-objective optimizationEvolutionary computationSet (abstract data type)optimointi0202 electrical engineering electronic engineering information engineeringmetamodellingRelevance (information retrieval)multiobjective optimizationBayesian optimizationta113021103 operations researchpareto-tehokkuusbayesilainen menetelmäBayesian optimizationmonitavoiteoptimointimachine learningkoneoppiminenheterogeneous objectivesBenchmark (computing)020201 artificial intelligence & image processing
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Parallel global optimization : structuring populations in differential evolution

2010

metaheuristicsoptimointistagnaatioglobal optimizationalgoritmitdifferentiaali evoluutioevoluutiolaskentaDifferential EvolutionEvolutionary computationevolutionary algorithmsmatemaattinen optimointiglobaali optimointitietojenkäsittely
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A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2009

In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widel…

Mathematical optimizationeducation.field_of_studyFitness functionDecision MakingPopulationEvolutionary algorithmInteractive evolutionary computationFunction (mathematics)Multi-objective optimizationPreferenceSet (abstract data type)Computational MathematicsData Interpretation StatisticalHumanseducationAlgorithmsMathematicsEvolutionary Computation
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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…

Embedded softwareTest caseSoftwareComputer sciencebusiness.industryWhite-box testingSystems engineeringSystem testingSoftware prototypingWhite boxSoftware engineeringbusinessEvolutionary computation2009 International Conference on Software Testing, Verification, and Validation Workshops
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Postoperative Lens Position Preoperatively Determined by Scheimpflug Photography

1999

The position of the artificial lens has an important influence on refractive power calculation. We compared the position of the crystalline lens with that of the artificial lens after cataract surgery by means of Scheimpflug photography. A difference in position of approximately 0.8 mm in the anterior direction could be determined.

medicine.medical_specialtygenetic structuresComputer Science::Neural and Evolutionary ComputationScheimpflug principlePhysics::OpticsAfter cataractOptical powerCataract ExtractionAstrophysics::Cosmology and Extragalactic Astrophysicslaw.inventionCataract extractionCellular and Molecular NeuroscienceLens Implantation IntraocularPosition (vector)lawProsthesis FittingOphthalmologyLens CrystallinePreoperative Caremental disordersPhotographymedicineHumansPostoperative PeriodLenses Intraocularbusiness.industryPhotographyGeneral Medicineeye diseasesSensory SystemsLens (optics)OphthalmologyOptometrysense organsbusinesspsychological phenomena and processesOphthalmic Research
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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…

FOS: Computer and information sciencesComputer Science::Machine Learning0301 basic medicineComputer Science - Machine LearningAdderPhysics and Astronomy (miscellaneous)Quantum machine learningField (physics)Computer scienceMaterials Science (miscellaneous)Computer Science::Neural and Evolutionary ComputationFOS: Physical sciencesData_CODINGANDINFORMATIONTHEORYTopology01 natural sciencesMachine Learning (cs.LG)Statistics::Machine Learning03 medical and health sciencesQuantum state0103 physical sciencesNeural and Evolutionary Computing (cs.NE)Electrical and Electronic Engineering010306 general physicsQuantumQuantum PhysicsArtificial neural networkComputer Science - Neural and Evolutionary ComputingTheoryofComputation_GENERALAutoencoderAtomic and Molecular Physics and OpticsQuantum technology030104 developmental biologyComputerSystemsOrganization_MISCELLANEOUSQuantum Physics (quant-ph)
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Unknown order process emulation

2002

Approaches the emulation problem using feedforward neural networks of single input single output (SISO) processes, applying a backpropagation method with a higher convergence rate. In this kind of application, difficult problems appear when the system's order is a priori unknown. A search through the SISO processes space is proposed, aiming to find a favorable neural emulator over the training examples set.

Set (abstract data type)EmulationRate of convergenceTime delay neural networkComputer scienceControl theoryComputer Science::Neural and Evolutionary ComputationLinear systemFeedforward neural networkBackpropagationIJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)
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