Giovanni Squillero

Edinburgh Napier University, Politecnico di Torino

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About

Giovanni Squillero is a researcher whose work spans evolutionary computation and industrial robotics, with particular expertise in applying computational intelligence to real-world engineering challenges. His contributions to the field of evolutionary computation are reflected in his work on *Applications of Evolutionary Computation* (2017), which has garnered 25 citations and stands as one of his most recognized contributions, helping to consolidate and disseminate knowledge across this broad and impactful domain. Squillero has also made meaningful strides in industrial robotics, specifically addressing the persistent challenge of gear backlash in robotic manipulators. His research on virtual sensing and backlash estimation — spanning a 2020 study on virtual measurement and a 2022 paper that has already accumulated 12 citations — offers practical solutions for predictive maintenance in industrial settings. By enabling accurate backlash estimation without direct measurement, his work helps preserve robot positioning accuracy and prevent costly equipment failures. Squillero's research profile reflects a researcher who bridges theoretical computational methods with tangible industrial applications, making his work valuable both to practitioners seeking operational improvements and to academics advancing the frontiers of evolutionary and intelligent systems research.