Sensing Technology in Evolutionary Computation
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 348
Special Issue Editors
Interests: pattern recognition; vehicle routing problem; machine learning; artificial intelligence; artificial neural networks; metaheuristic; TSP; evolutionary algorithms; heuristics
Interests: autonomous intersections; transportation systems; traffic control; urban mobility; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Among intelligent algorithms applied to the huge data provided by sensors, some are related to optimization and follow the Evolutionary Computation (EC) paradigm. They constitute a class of population-based metaheuristics that can successfully address large-scale and difficult real-world optimization problems (NP-hard) modelling sensor data understanding. While classical optimization most often requires objective functions to be differentiable or only apply to small size problems, as with exact methods, EC alleviates this requirement and allows to address a large range of applications with easy-to-implement methods, possibly executed in a parallel and distributed way, and providing high-quality empirical results.
The aim of this Special Issue is to solicit up-to-date contributions on the topics of Evolutionary Computation in the context of sensing technology. Sensors, and their digital data and devices, can include camera sensors, radar sensors, ultrasound and sonar sensors, imaging with probes, Lidar, and UAV sensors. The problematics include, but are not limited to, pattern recognition, perception with uncertain data, feature extraction, tracking and matching, optical-flow computation, speech recognition, and data fusion. Within EC, we include, amongst others, population-based metaheuristics, genetic algorithms, particle swarm optimization, ant colony, memetic algorithms, and to a larger extent, neighborhood search, hyperheuristics, and hybrid methods.
Dr. Jean Charles Créput
Dr. Mahjoub Dridi
Guest Editors
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