The Use of Evolutionary Algorithms in Robotics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 115
Special Issue Editors
Interests: computational intelligence; intelligent control; computational learning; machine learning; fuzzy systems; neural networks; optimization; modeling; simulation; estimation; prediction; control; big data; robotics; mobile robotics and intelligent vehicles; robot manipulators control; sensing; soft sensors; automation; industrial systems; embedded systems; real-time systems; general architectures and systems for controlling robot manipulators; mobile robots
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Special Issue Information
Dear Colleagues,
This study explores evolutionary algorithms (EAs) within the field of robotics, addressing aspects such as robot design optimization, control mechanisms, path navigation, and swarm behavior. EAs like genetic algorithms (GAs), genetic programming (GP), and evolutionary strategies (ESs) emulate biological evolution via techniques of the selection, mutation, and recombination of solutions. Due to their capability to search extensive solution spaces, they are particularly well suited for use in robotics, where manual adjustments may be challenging. The significance of the research area includes adaptability, design innovation, efficiency, and scalability.
Dr. Rui Araújo
Dr. Roland Szabó
Guest Editors
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Keywords
- evolutionary algorithms (EA)
- genetic algorithm (GA)
- memetic algorithms (MA)
- particle swarm optimization (PSO)
- ant colony optimization (ACO)
- differential evolution (DE)
- shuffled frog leaping algorithm (SFLA)
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