Results 211 to 220 of about 88,043 (267)

Neutron Reflectometry and Compression of Graded Hydrogel Surfaces

Advanced Materials Interfaces, EarlyView.
Highly‐entangled and covalently‐crosslinked hydrogels with thin and thick surface gel layers are compressed in neutron reflectometry experiments. Surface gel layer thickness dictates the extent to which the polymer network at the surface of a hydrogel collapses, while crosslinking chemistry, and therefore elastic modulus, impacts the pressure at which ...
Kathryn E. Shaffer, Alex Q. Zhang, Mehdi Karimi, Ahmed Al Kindi, Linnaea D. Uliassi, Nemea S. Courelli, Julia J. Ong, Conor D. Pugsley, Andrew R. Rhode, Erik B. Watkins, Rebecca J.L. Welbourn, Alexander Alexeev, Angela A. Pitenis   +12 more
wiley   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

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, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid.

Heliyon
Baghoolizadeh M, Jasim DJ, Sajadi SM, Renani RR, Renani MR, Hekmatifar M.   +5 more
europepmc   +1 more source

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Evolutionary Algorithms

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.
openaire   +3 more sources

Evolutionary Rao algorithm

Journal of Computational Science, 2021
Abstract This paper proposes an evolutionary  Rao algorithm (ERA) to enhance three state-of-the-art metaheuristic Rao algorithms (Rao-1, Rao-2, Rao-3) by introducing two new schemes. Firstly, the population is split into two sub-populations based on their qualities: high and low, with a particular portion. The high-quality sub-population searches for
Suyanto Suyanto, Agung Toto Wibowo, Said al Faraby, Siti Saadah, Rita Rismala   +4 more
openaire   +1 more source

Evolutionary Algorithms

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, Jürgen Branke, Jörn Mehnen, Olaf Mersmann   +3 more
openaire   +1 more source

Evolving evolutionary algorithms using evolutionary algorithms

Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
A 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 Diosan, Mihai Oltean
openaire   +1 more source

Evolutionary design of Evolutionary Algorithms

Genetic Programming and Evolvable Machines, 2009
Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters
Laura Diosan, Mihai Oltean
openaire   +1 more source

Quasirandom evolutionary algorithms

Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010
Motivated 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
Benjamin Doerr, Mahmoud Fouz, Carsten Witt   +2 more
openaire   +2 more sources