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Recent Advances of Slip Sensors for Smart Robotics
Advanced Materials Technologies, EarlyView.This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang +8 more
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Fabric‐Based Wearable Robotic Exoskeleton Gloves: Advancements and Challenges
Advanced Materials Technologies, EarlyView.This review highlights interdisciplinary technological advances in fabric‐based robotic gloves, focusing on progress in design, fabrication, actuation, sensing, control, and power and energy requirements. It also addresses performance testing and validation, including biomechanical, strength, functional, user experience, and durability assessments, to ...
Ayse Feyza Yilmaz +2 more
wiley +1 more source
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WIREs Data Mining and Knowledge Discovery, 2014
AbstractEvolutionary algorithm (EA) is an umbrella term used to describe population‐based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming.
Thomas Bartz‐Beielstein +3 more
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AbstractEvolutionary algorithm (EA) is an umbrella term used to describe population‐based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming.
Thomas Bartz‐Beielstein +3 more
+5 more sources
Information Sciences, 2001
This article broadly introduces evolutionary algorithms and discusses the current trends, both in a historical perspective and with respect to practical outcomes. It then quickly surveys theoretical results and main domains of applications.
Michalewicz, Z., Schoenauer, M.
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This article broadly introduces evolutionary algorithms and discusses the current trends, both in a historical perspective and with respect to practical outcomes. It then quickly surveys theoretical results and main domains of applications.
Michalewicz, Z., Schoenauer, M.
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Quasirandom evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010Motivated by recent successful applications of the concept of quasirandomness, we investigate to what extent such ideas can be used in evolutionary computation. To this aim, we propose different variations of the classical (1+1) evolutionary algorithm, all imitating the property that the (1+1) EA over intervals of time touches all bits roughly the same
Doerr, B., Fouz, M., Witt, C.
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Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem.
Laura Silvia Diosan, Mihai Oltean
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2007
Evolutionary computation is an old field of computer science that started in the end of the 1960s nearly simultaneously in different parts of the world. Each paradigm has evolved separately, apparently without knowledge of what was happening elsewhere, until people finally got together and shared their experience.
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Evolutionary computation is an old field of computer science that started in the end of the 1960s nearly simultaneously in different parts of the world. Each paradigm has evolved separately, apparently without knowledge of what was happening elsewhere, until people finally got together and shared their experience.
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ACM SIGBIO Newsletter, 1992
Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the
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Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the
openaire +1 more source