| Nome |
# |
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Applications of Evolutionary Computation, file e384c42e-392e-d4b2-e053-9f05fe0a1d67
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813
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Divergence of character and premature convergence: A survey of methodologies for promoting diversity in evolutionary optimization, file e384c42e-4953-d4b2-e053-9f05fe0a1d67
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665
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Covariance Matrix Adaptation Evolutionary Strategy for Drift Correction of Electronic Nose Data, file e384c42e-116f-d4b2-e053-9f05fe0a1d67
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564
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Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation, file e384c42e-1172-d4b2-e053-9f05fe0a1d67
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486
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Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments, file e384c42e-47c1-d4b2-e053-9f05fe0a1d67
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483
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Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms, file e384c42e-7757-d4b2-e053-9f05fe0a1d67
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471
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Adaptive opponent modelling for the iterated prisoner's dilemma, file e384c42e-1516-d4b2-e053-9f05fe0a1d67
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368
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Accounting for Post-Transcriptional Regulation in Boolean Networks Based Regulatory Models., file e384c42e-2e63-d4b2-e053-9f05fe0a1d67
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363
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Post-silicon failing-test generation through evolutionary computation, file e384c42e-1514-d4b2-e053-9f05fe0a1d67
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336
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Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing, file e384c42e-0fb4-d4b2-e053-9f05fe0a1d67
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334
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Group evolution: Emerging synergy through a coordinated effort, file e384c42e-0b0f-d4b2-e053-9f05fe0a1d67
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298
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Towards Drift Correction in Chemical Sensors Using an Evolutionary Strategy, file e384c42e-12ad-d4b2-e053-9f05fe0a1d67
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281
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Evolution of Test Programs Exploiting a FSM Processor Model, file e384c42e-1515-d4b2-e053-9f05fe0a1d67
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266
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Applications of Evolutionary Computation (Part I), file e384c42e-cae1-d4b2-e053-9f05fe0a1d67
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218
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Applications of Evolutionary Computation (Part II), file e384c42e-cb20-d4b2-e053-9f05fe0a1d67
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192
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Applications of Evolutionary Computation (Part I), file e384c42f-775c-d4b2-e053-9f05fe0a1d67
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158
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Applications of Evolutionary Computation (Part II), file e384c42f-775d-d4b2-e053-9f05fe0a1d67
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123
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Evolutionary Antivirus Signature Optimization, file e384c430-e6d2-d4b2-e053-9f05fe0a1d67
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113
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New Techniques to Reduce the Execution Time of Functional Test Programs, file e384c42f-5d4e-d4b2-e053-9f05fe0a1d67
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103
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Virtual Measurement of the Backlash Gap in Industrial Manipulators, file e384c431-0323-d4b2-e053-9f05fe0a1d67
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73
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Theory and practice of population diversity in evolutionary computation, file e384c432-36ad-d4b2-e053-9f05fe0a1d67
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67
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Simulation-based Equivalence Checking between IEEE 1687 ICL and RTL, file e384c430-e0b7-d4b2-e053-9f05fe0a1d67
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55
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The Rise of Android Banking Trojans, file e384c431-ffd1-d4b2-e053-9f05fe0a1d67
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55
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Identification and Rejuvenation of NBTI-Critical Logic Paths in Nanoscale Circuits, file e384c430-8502-d4b2-e053-9f05fe0a1d67
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53
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A Novel Sequence Generation Approach to Diagnose Faults in Reconfigurable Scan Networks, file e384c430-ff36-d4b2-e053-9f05fe0a1d67
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51
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Evolutionary algorithms and machine learning: Synergies, Challenges and Opportunities, file e384c432-8609-d4b2-e053-9f05fe0a1d67
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47
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Post-Silicon Validation of IEEE 1687 Reconfigurable Scan Networks, file e384c430-c575-d4b2-e053-9f05fe0a1d67
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41
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Countering Android Malware: a Scalable Semi-Supervised Approach for Family-Signature Generation, file e384c431-770e-d4b2-e053-9f05fe0a1d67
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41
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Smart techniques for flying-probe testing, file e384c433-dc40-d4b2-e053-9f05fe0a1d67
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40
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A New Technique to Generate Test Sequences for Reconfigurable Scan Networks, file e384c430-db0d-d4b2-e053-9f05fe0a1d67
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34
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A Benchmark Suite of RT-level Hardware Trojansfor Pipelined Microprocessor Cores, file e384c432-f25d-d4b2-e053-9f05fe0a1d67
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34
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A Semi-Formal Technique to Generate Effective Test Sequences for Reconfigurable Scan Networks, file e384c431-0a89-d4b2-e053-9f05fe0a1d67
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29
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Exploiting Active Learning for Microcontroller Performance Prediction, file e384c433-a77c-d4b2-e053-9f05fe0a1d67
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27
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Evolving assembly programs: how games help microprocessor validation, file 2bf729c9-334f-4948-be09-ab18ed60ebc0
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24
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Evolving Individual Behavior in a Multi-Agent Traffic Simulator, file e384c42e-0fb2-d4b2-e053-9f05fe0a1d67
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23
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Test-Plan Optimization for Flying-Probes In-Circuit Testers, file e384c431-88c3-d4b2-e053-9f05fe0a1d67
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22
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The Maximum Common Subgraph Problem: A Parallel and Multi-Engine Approach, file e384c432-0353-d4b2-e053-9f05fe0a1d67
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21
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Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry, file e384c434-b6f0-d4b2-e053-9f05fe0a1d67
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21
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On NBTI-induced Aging Analysis in IEEE 1687 Reconfigurable Scan Networks, file e384c430-c64b-d4b2-e053-9f05fe0a1d67
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20
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Test, Reliability and Functional Safety trends for Automotive System-on-Chip, file e384c434-7998-d4b2-e053-9f05fe0a1d67
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19
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Test, Reliability and Functional Safety Trends for Automotive System-on-Chip, file 8ca85a99-2ac5-4a5b-962f-9164fb4fea5c
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18
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Exploiting Artificial Swarms for the Virtual Measurement of Backlash in Industrial Robots, file e384c433-9a29-d4b2-e053-9f05fe0a1d67
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18
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An Enhanced Evolutionary Technique for the Generation of Compact Reconfigurable Scan-Network Tests, file e384c431-1604-d4b2-e053-9f05fe0a1d67
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15
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A Virtual Sensor for Backlash in Robotic Manipulators, file e384c434-6cb3-d4b2-e053-9f05fe0a1d67
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15
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An Enhanced Evolutionary Technique for the Generation of Compact Reconfigurable Scan-Network Tests, file e384c431-1603-d4b2-e053-9f05fe0a1d67
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14
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Comparing different approaches to the test of Reconfigurable Scan Networks, file e384c433-2237-d4b2-e053-9f05fe0a1d67
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14
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A Multi-Label Active Learning Framework for Microcontroller Performance Screening, file 2f434b01-35e0-4759-b6cc-5e889c713bd8
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12
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Automated Playtesting in Collectible Card Games using Evolutionary Algorithms: a Case Study in HearthStone, file e384c430-163c-d4b2-e053-9f05fe0a1d67
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9
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Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach, file e384c433-47f5-d4b2-e053-9f05fe0a1d67
|
9
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Evolutionary Antivirus Signature Optimization, file e384c430-e6d1-d4b2-e053-9f05fe0a1d67
|
8
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(Over-)Realism in evolutionary computation: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin, file e384c433-6f61-d4b2-e053-9f05fe0a1d67
|
8
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Semi-Supervised Deep Learning for Microcontroller Performance Screening, file 98c671f9-40b8-4f80-a5f8-9a4de26c9144
|
7
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A dynamic greedy test scheduler for optimizing probe motion in in-circuit testers, file e384c431-2921-d4b2-e053-9f05fe0a1d67
|
6
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|
Generating Neural Archetypes to Instruct Fast and Interpretable Decisions, file e384c431-86a8-d4b2-e053-9f05fe0a1d67
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6
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Microcontroller Performance Screening: Optimizing the Characterization in the Presence of Anomalous and Noisy Data, file 72057a64-5622-443c-afd2-03e97e465782
|
5
|
|
Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation, file e384c42e-0a75-d4b2-e053-9f05fe0a1d67
|
5
|
|
Multi-level diversity promotion strategies for Grammar-guided Genetic Programming, file e384c431-3534-d4b2-e053-9f05fe0a1d67
|
5
|
|
Microcontroller Performance Screening: Optimizing the Characterization in the Presence of Anomalous and Noisy Data, file 69b2b92e-a5a1-4d11-ab4a-4f278bd8804a
|
4
|
|
Identification and Rejuvenation of NBTI-Critical Logic Paths in Nanoscale Circuits, file e384c42f-12c2-d4b2-e053-9f05fe0a1d67
|
4
|
|
(Over-)Realism in evolutionary computation: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin, file e384c42f-626d-d4b2-e053-9f05fe0a1d67
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4
|
|
Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach, file e384c430-3b2f-d4b2-e053-9f05fe0a1d67
|
4
|
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Post-Silicon Validation of IEEE 1687 Reconfigurable Scan Networks, file e384c430-c574-d4b2-e053-9f05fe0a1d67
|
4
|
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Exploiting Artificial Swarms for the Virtual Measurement of Backlash in Industrial Robots, file e384c434-5502-d4b2-e053-9f05fe0a1d67
|
4
|
|
Feature Selection for Cost Reduction in MCU Performance Screening, file 3e7822e8-536a-4281-be9a-03d0a59f3be1
|
3
|
|
A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection, file 5207b782-1b64-4824-8505-979caafb2877
|
3
|
|
HAIT: Heap Analyzer with Input Tracing, file e384c42f-8140-d4b2-e053-9f05fe0a1d67
|
3
|
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VALIS: an evolutionary classification algorithm, file e384c433-9aa8-d4b2-e053-9f05fe0a1d67
|
3
|
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Machine Learning for Hardware Security: Classifier-based Identification of Trojans in Pipelined Microprocessors, file e384c434-3a92-d4b2-e053-9f05fe0a1d67
|
3
|
|
Predictable Features Elimination: An Unsupervised Approach to Feature Selection, file e384c434-af01-d4b2-e053-9f05fe0a1d67
|
3
|
|
Enabling Inter-Product Transfer Learning on MCU Performance Screening, file 97592cf8-c40e-4a7c-887c-2a5764ce29a9
|
2
|
|
Transfer Learning in MCU Performance Screening, file bf798141-bd4c-4ae4-a186-419511808a47
|
2
|
|
Evolving assembly programs: how games help microprocessor validation, file e384c42d-f18e-d4b2-e053-9f05fe0a1d67
|
2
|
|
Approximate Equivalence Verification for Protocol Interface Implementation via Genetic Algorithms, file e384c42d-f601-d4b2-e053-9f05fe0a1d67
|
2
|
|
Artificial evolution in computer aided design: from the optimization of parameters to the creation of assembly programs, file e384c42e-0a91-d4b2-e053-9f05fe0a1d67
|
2
|
|
Industrial Applications of Evolutionary Algorithms, file e384c42e-9e71-d4b2-e053-9f05fe0a1d67
|
2
|
|
VALIS: an evolutionary classification algorithm, file e384c430-befa-d4b2-e053-9f05fe0a1d67
|
2
|
|
A Semi-Formal Technique to Generate Effective Test Sequences for Reconfigurable Scan Networks, file e384c430-dd66-d4b2-e053-9f05fe0a1d67
|
2
|
|
Multi-level diversity promotion strategies for Grammar-guided Genetic Programming, file e384c431-671b-d4b2-e053-9f05fe0a1d67
|
2
|
|
The Rise of Android Banking Trojans, file e384c431-ffd0-d4b2-e053-9f05fe0a1d67
|
2
|
|
Simulation-based Equivalence Checking between IEEE 1687 ICL and RTL, file e384c432-0c1f-d4b2-e053-9f05fe0a1d67
|
2
|
|
Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms, file e384c433-216a-d4b2-e053-9f05fe0a1d67
|
2
|
|
Machine Learning based Performance Prediction of Microcontrollers using Speed Monitors, file e384c433-d339-d4b2-e053-9f05fe0a1d67
|
2
|
|
Predictable Features Elimination: An Unsupervised Approach to Feature Selection, file e384c434-9f14-d4b2-e053-9f05fe0a1d67
|
2
|
|
Social Influence Analysis (SIA) in Online Social Networks, file ef1b6636-495e-40b9-8eaa-12554ab10975
|
2
|
|
Towards Evolutionary Control Laws for Viability Problems, file 01ff305a-f05c-4a70-a1d5-ec352d0623eb
|
1
|
|
Evolutionary discovery of coresets for classification, file 49d11925-68fa-49ea-ad07-8b93293e921b
|
1
|
|
Enabling Inter-Product Transfer Learning on MCU Performance Screening, file 61c94669-ac89-48ee-9b54-d48356800826
|
1
|
|
Beyond coreset discovery: evolutionary archetypes, file 7a766513-7bb7-4d7d-8212-e6cd9893872d
|
1
|
|
Test, Reliability and Functional Safety Trends for Automotive System-on-Chip, file 86aa7109-84fe-4ecf-acc9-c9d1b8069e9d
|
1
|
|
Veni, Vidi, Evolvi (commentary on W. B. Langdon’s "Jaws 30"), file ddd76793-32e0-4d0c-ae8d-709c5ada6ce3
|
1
|
|
Exploiting Evolutionary Computation in an Industrial Flow for the Development of Code-Optimized Microprocessor Test Programs, file e384c42d-c651-d4b2-e053-9f05fe0a1d67
|
1
|
|
A benchmark for cooperative coevolution, file e384c42e-1944-d4b2-e053-9f05fe0a1d67
|
1
|
|
TURAN: Evolving non-deterministic players for the iterated prisoner's dilemma, file e384c42e-321f-d4b2-e053-9f05fe0a1d67
|
1
|
|
Evolutionary Optimization: the µGP toolkit, file e384c42e-a14e-d4b2-e053-9f05fe0a1d67
|
1
|
|
Towards automatic StarCraft strategy generation using genetic programming, file e384c42e-cd8c-d4b2-e053-9f05fe0a1d67
|
1
|
|
Anatomy of a portfolio optimizer under a limited budget constraint, file e384c42f-071a-d4b2-e053-9f05fe0a1d67
|
1
|
|
Observability solutions for in-field functional test of processor-based systems: a survey and quantitative test case evaluation, file e384c42f-08aa-d4b2-e053-9f05fe0a1d67
|
1
|
|
Tutorials at PPSN 2016, file e384c42f-12c1-d4b2-e053-9f05fe0a1d67
|
1
|
|
Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments, file e384c42f-1c73-d4b2-e053-9f05fe0a1d67
|
1
|
|
An Evolutionary Approach to Hardware Encryption and Trojan-Horse Mitigation, file e384c42f-7af5-d4b2-e053-9f05fe0a1d67
|
1
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| Totale |
7.694 |
Archivio Istituzionale della Ricerca