Search results for " algorithm"

showing 10 items of 2538 documents

On the Use of Prognostics and Health Management to Jointly Schedule Production and Maintenance on a Single Multi-purpose Machine

2020

This paper address the problem of using prognostic information in the decision-making process of a single multi-purpose machine. The prognostics and health management method is compared to condition-based maintenance combined with a genetic algorithm to determine the joint schedule of maintenance and production. The paper presents a methodology to select the adequate strategy while considering several factors that influence the functioning of the machine. The results show that operational and conditions variability influence the choice of the suitable methods. In the presented case, we show configurations where prognostic information is useless or useful.

ScheduleComputer scienceProcess (engineering)Ant colony optimization algorithmsCondition-based maintenanceGenetic algorithmPrognosticsProduction (economics)Reliability engineering2020 Prognostics and Health Management Conference (PHM-Besançon)
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Checkpointing Workflows for Fail-Stop Errors

2017

International audience; We consider the problem of orchestrating the exe- cution of workflow applications structured as Directed Acyclic Graphs (DAGs) on parallel computing platforms that are subject to fail-stop failures. The objective is to minimize expected overall execution time, or makespan. A solution to this problem consists of a schedule of the workflow tasks on the available processors and of a decision of which application data to checkpoint to stable storage, so as to mitigate the impact of processor failures. For general DAGs this problem is hopelessly intractable. In fact, given a solution, computing its expected makespan is still a difficult problem. To address this challenge,…

ScheduleComputer scienceworkflowDistributed computing[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]010103 numerical & computational mathematics02 engineering and technologyParallel computing[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]01 natural sciencesTheoretical Computer Science[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]checkpointfail-stop error0202 electrical engineering electronic engineering information engineeringOverhead (computing)[INFO]Computer Science [cs]0101 mathematicsresilienceClass (computer programming)020203 distributed computingJob shop schedulingProbabilistic logic020206 networking & telecommunications[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationDynamic programmingTask (computing)[INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF]WorkflowComputational Theory and MathematicsHardware and Architecture[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Task analysis[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Software
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An adaptive multimeme algorithm for designing HIV multidrug therapies.

2007

This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimizati…

ScheduleMathematical optimizationComputer scienceAnti-HIV AgentsHIV therapy designAdaptive algorithms; HIV therapy design; Memetic algorithms; Nonlinear integer programming; Algorithms; Anti-HIV Agents; Biomimetics; Computer Simulation; Drug Combinations; Drug Design; Drug Therapy Computer-Assisted; HIV Infections; Humans; Immunity Innate; Models ImmunologicalHIV InfectionsReduction (complexity)Computer-AssistedDrug TherapyModelsBiomimeticsGeneticsInnateHumansComputer SimulationRepresentation (mathematics)MetaheuristicStatistical hypothesis testingFlexibility (engineering)Applied MathematicsNonlinear integer programmingImmunityModels ImmunologicalAdaptive algorithmsImmunity InnateDrug Therapy Computer-AssistedDrug CombinationsImmunologicalDrug DesignMemetic algorithmsMemetic algorithmAlgorithmAlgorithmsBiotechnologyPremature convergenceIEEE/ACM transactions on computational biology and bioinformatics
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A hybrid genetic algorithm for the resource-constrained project scheduling problem

2008

Abstract In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j…

Scheduleeducation.field_of_studyMathematical optimizationInformation Systems and ManagementGeneral Computer ScienceComputer sciencebusiness.industryResource constrainedCrossoverPopulationManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringProject scheduling problemModeling and SimulationGenetic algorithmArtificial intelligencebusinessHeuristicseducationEuropean Journal of Operational Research
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Schema-Backed Visual Queries over Europeana and Other Linked Data Resources

2021

We describe and demonstrate the process of extracting a data-driven schema of the Europeana cultural heritage Linked data resource (with actual data classes, properties and their connections, and cardinalities) and application of the extracted schema to create a visual query environment over Europeana. The extracted schema information allows generating SHACL data shapes describing the actual data endpoint structure. The schema extraction process can be applied also to other data endpoints with a moderate data schema size and a potentially large data triple count, as e.g., British National Bibliography Linked data resource.

Schema (genetic algorithms)Structure (mathematical logic)Information retrievalResource (project management)Computer scienceProcess (engineering)Database schemaSPARQLcomputer.file_formatLinked dataRDFcomputer
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A Dynamic Attribute-Based Authentication Scheme

2015

Attribute-based authentication (ABA) is an approach to authenticate users by their attributes, so that users can get authenticated anonymously and their privacy can be protected. In ABA schemes, required attributes are represented by attribute trees, which can be combined with signature schemes to construct ABA schemes. Most attribute trees are built from top to down and can not change with attribute requirement changes. In this paper, we propose an ABA scheme based on down-to-top built attribute trees or dynamic attribute trees, which can change when attribute requirements change. Therefore, the proposed dynamic ABA scheme is more efficient in a dynamic environment by avoiding regenerating…

Scheme (programming language)AuthenticationComputer scienceComputerApplications_COMPUTERSINOTHERSYSTEMSConstruct (python library)computer.software_genreSignature (logic)Authentication protocolLightweight Extensible Authentication ProtocolData miningChallenge–response authenticationcomputerData Authentication Algorithmcomputer.programming_language
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Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

2012

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_79 The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive wh…

Scheme (programming language)Mathematical optimizationDiscretizationLearning automataComputer sciencebusiness.industryVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422estimator algorithmsBayesian probabilityBayesian reasoninglearning automataEstimatorVDP::Technology: 500::Information and communication technology: 550discretized learningBayesian inferenceAction (physics)Reinforcement learningArtificial intelligencepursuit schemesbusinesscomputercomputer.programming_language
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A General Frame for Building Optimal Multiple SVM Kernels

2012

The aim of this paper is to define a general frame for building optimal multiple SVM kernels. Our scheme follows 5 steps: formal representation of the multiple kernels, structural representation, choice of genetic algorithm, SVM algorithm, and model evaluation. The computation of the optimal parameter values of SVM kernels is performed using an evolutionary method based on the SVM algorithm for evaluation of the quality of chromosomes. After the multiple kernel is found by the genetic algorithm we apply cross validation method for estimating the performance of our predictive model. We implemented and compared many hybrid methods derived from this scheme. Improved co-mutation operators are u…

Scheme (programming language)Multiple kernel learningbusiness.industryComputationPattern recognitionCross-validationSupport vector machineGenetic algorithmArtificial intelligenceGeneral framebusinesscomputerKernel (category theory)Mathematicscomputer.programming_language
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Embedding Evolution in Epidemic-Style Forwarding

2007

International audience; In this work, we introduce a framework to let forwarding schemes evolve in order to adapt to changing and a priori unknown environments. The framework is inspired by genetic algorithms: at each node a genotype describes the forwarding scheme used, a selection process fosters the diffusion of the fittest genotypes in the system and new genotypes are created by combining existing ones or applying random changes. A case study implementation is presented and its performance evaluated via numerical simulations.

Scheme (programming language)Theoretical computer scienceComputer scienceSurvival of the fittestNode (networking)Quality control and genetic algorithmsProcess (computing)Quantitative Biology::Genomics[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]EmbeddingQuantitative Biology::Populations and EvolutioncomputerSelection (genetic algorithm)computer.programming_language
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A Study on scale factor in distributed differential evolution.

2011

This paper proposes the employment of multiple scale factor values within distributed differential evolution structures. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed differential evolution structures and tested on several various test problems. Numerical results show that, on average, the employment of multiple scale factors is beneficial since in most cases it leads to significant improvements in performance with respect to standard distributed alg…

Scheme (programming language)ta113distributed algorithmsMathematical optimizationInformation Systems and ManagementScale (ratio)Computer sciencedifferential evolutionEvolutionary algorithmcomputational intelligence optimizationevolutionary algorithmsstructured populationsScale factorComputer Science ApplicationsTheoretical Computer ScienceArtificial IntelligenceControl and Systems EngineeringSimple (abstract algebra)Distributed algorithmDifferential evolutionoptimization algorithmsscale factorcomputerSoftwarecomputer.programming_language
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