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About

David Naso is a researcher whose work spans evolutionary computation, autonomous robotics, and advanced soft actuation systems. He is perhaps best known for introducing the **Compact Differential Evolution (cDE)** algorithm in 2010, a landmark contribution to the field of evolutionary optimization that replaces traditional population-based processing with statistical representations, achieving remarkable computational efficiency — a paper that has garnered over 240 citations. His robotics research has made significant strides in autonomous navigation and multi-robot coordination, including fuzzy logic-based control for mobile robots (80 citations) and decentralized task assignment frameworks for heterogeneous robot networks, enhancing robustness and scalability in complex multi-agent systems. More recently, Naso has emerged as a notable contributor to soft robotics, particularly in the modeling and sensorless control of dielectric elastomer actuators (DEAs), enabling self-sensing stiffness regulation without external sensors — work that reflects a growing trend toward intelligent, compliant robotic systems. His research on magnetic shape memory actuators further demonstrates his broad expertise in smart materials for precision engineering. Across more than a decade of prolific output, Naso has consistently bridged theoretical innovation with practical robotic applications.