Sökning: "exhaustive algorithm"

Visar resultat 1 - 5 av 24 uppsatser innehållade orden exhaustive algorithm.

  1. 1. Minimization of Model-based Tests in Modbat

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Caroline Borg; [2023]
    Nyckelord :Software testing; Model-based testing; Test minimization; Delta debugging; Modbat; modmin; Mjukvarutestning; Modellbaserad testning; Testminimering; Delta debugging; Modbat; modmin;

    Sammanfattning : Model-based testing (MBT) is a promising testing method with advantages like exhaustive exploration and high maintainability. However, one notable downside is that the generated tests usually contain much unnecessary noise. LÄS MER

  3. 2. Optimization of Physical Uplink Resource Allocation in 5G Cellular Network using Monte Carlo Tree Search

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Gerard Girame Rizzo; [2022]
    Nyckelord :5G NR; PUCCH format; Combinatorial optimization; Physical resource allocation; Monte Carlo Tree Search; 5G NR; PUCCH-format; kombinatorisk optimering; fysisk resursfördelning; Monte Carlo Tree Search;

    Sammanfattning : The Physical Uplink Control Channel (PUCCH), which is mainly used to transmit Uplink Control Information (UCI), is a key component to enable the 5G NR system. Compared to LTE, NR specifies a more flexible PUCCH structure to support various applications and use cases. LÄS MER

  4. 3. Automatic Development of Pharmacokinetic Structural Models

    Master-uppsats, Uppsala universitet/Institutionen för farmaci

    Författare :Alzahra Hamdan; [2022]
    Nyckelord :pharmacometrics; non-linear mixed effects; modelling; pharmacokinetics; NONMEM; automation; exhaustive algorithm; stepwise algorithm; structural models; inter-individual variability; residual error models; model selection;

    Sammanfattning : Introduction: The current development strategy of population pharmacokinetic models is a complex and iterative process that is manually performed by modellers. Such a strategy is time-demanding, subjective, and dependent on the modellers’ experience. LÄS MER

  5. 4. Search methods for Strategy Synthesis for Multi-Agent Games of Imperfect Information

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Axel Jernbäcker; Max Junestrand; [2021]
    Nyckelord :;

    Sammanfattning : Finding strategies for agents in multi-agent systems of imperfect information is a difficult and time-consuming endeavor. It is thus of interest to find ways of doing this more effectively. This study aims to improve the performance of a strategy synthesizer tool by testing two search techniques and evaluating their performance. LÄS MER

  7. 5. Hyperparameter Tuning Using Genetic Algorithms : A study of genetic algorithms impact and performance for optimization of ML algorithms

    Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

    Författare :Franz David Krüger; Mohamad Nabeel; [2021]
    Nyckelord :Machine learning; Data mining; ML algorithm; Genetic algorithms; hyperparameter optimization.;

    Sammanfattning : Maskininlärning har blivit allt vanligare inom näringslivet. Informationsinsamling med Data mining (DM) har expanderats och DM-utövare använder en mängd tumregler för att effektivisera tillvägagångssättet genom att undvika en anständig tid att ställa in hyperparametrarna för en given ML-algoritm för nå bästa träffsäkerhet. LÄS MER