46 research outputs found
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Quantization is one of the most applied Deep Neural Network (DNN) compression
strategies, when deploying a trained DNN model on an embedded system or a cell
phone. This is owing to its simplicity and adaptability to a wide range of
applications and circumstances, as opposed to specific Artificial Intelligence
(AI) accelerators and compilers that are often designed only for certain
specific hardware (e.g., Google Coral Edge TPU). With the growing demand for
quantization, ensuring the reliability of this strategy is becoming a critical
challenge. Traditional testing methods, which gather more and more genuine data
for better assessment, are often not practical because of the large size of the
input space and the high similarity between the original DNN and its quantized
counterpart. As a result, advanced assessment strategies have become of
paramount importance. In this paper, we present DiverGet, a search-based
testing framework for quantization assessment. DiverGet defines a space of
metamorphic relations that simulate naturally-occurring distortions on the
inputs. Then, it optimally explores these relations to reveal the disagreements
among DNNs of different arithmetic precision. We evaluate the performance of
DiverGet on state-of-the-art DNNs applied to hyperspectral remote sensing
images. We chose the remote sensing DNNs as they're being increasingly deployed
at the edge (e.g., high-lift drones) in critical domains like climate change
research and astronomy. Our results show that DiverGet successfully challenges
the robustness of established quantization techniques against
naturally-occurring shifted data, and outperforms its most recent concurrent,
DiffChaser, with a success rate that is (on average) four times higher.Comment: Accepted for publication in The Empirical Software Engineering
Journal (EMSE