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Found 12 papers, 0 papers with code
An Adaptive Metaheuristic Framework for Changing Environments
The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time.
Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
However, the testing approaches of input data are not as systematic and are few compared to model testing.
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems.
A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier's performance per domain region.
Quality Assurance in MLOps Setting: An Industrial Perspective
The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software.
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors
These methods utilize the predictive model's performance degradation to signal substantial changes in the systems.
Overview of Test Coverage Criteria for Test Case Generation from Finite State Machines Modelled as Directed Graphs
Test Coverage criteria are an essential concept for test engineers when generating the test cases from a System Under Test model.
Prioritized Variable-length Test Cases Generation for Finite State Machines
Depending on the application of the FSM, the strategy and evaluation presented in this paper are applicable both in testing functional and non-functional software requirements.
Using Deep Reinforcement Learning for Zero Defect Smart Forging
Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment.
Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems
This paper discusses a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO).
Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites
The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems.
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem
Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level.
