Search Results for author:
Found 6 papers, 2 papers with code
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems.
An LSTM-based Plagiarism Detection via Attention Mechanism and a Population-based Approach for Pre-Training Parameters with imbalanced Classes
To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems.
Automated Performance Testing Based on Active Deep Learning
On the other hand, we have a limited test budget to execute tests.
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible.
