Results 121 to 130 of about 5,249 (165)

Felis Catus Optimization (FCO): A novel nature‑inspired metaheuristic algorithm. [PDF]

PLoS One
Salehi M, Khayami R, Mirmozaffari M.
europepmc   +1 more source

Measuring the health benefits of genome and exome sequencing: a systematic review of economic evaluations. [PDF]

Front Public Health
Riccio M, Rosso A, Siena LM, Baccolini V, Migliara G, Sciurti A, Isonne C, Iera J, Pierri F, Marzuillo C, De Vito C, La Torre G, Villari P.   +12 more
europepmc   +1 more source

Clinical Long-Read Sequencing Test for Genetic Disease Diagnosis.

JAMA Pediatr
Thiffault I, Farrow E, Barrett C, Scott M, Ross A, Means JC, Cheung WA, Johnson AF, Koseva B, McLennan R, Grundberg E, Bi C, Schwendinger-Schreck C, Yoo B, Johnston JJ, Del Viso F, Paolillo V, Herriges J, Zhang L, Gibson M, Cohen ASA, Alaimo J, Saunders CJ, Pastinen T.   +23 more
europepmc   +1 more source

In Vivo Cardiac Biomechanical Model Parameter Estimation from Ultrafast Shear Wave Elastography

Ferrario C, Venet M, Villemain O, Sermesant M.   +3 more
europepmc   +1 more source

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Optimizing CMA-ES with CMA-ES

Proceedings of the 15th International Joint Conference on Computational Intelligence, 2023
The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a ...
Thomaser, A.M., Vogt, M.E., Bäck, T.H.W., Kononova, A.V.   +3 more
openaire   +2 more sources

MODE/CMA-ES: Integrated multi-operator differential evolution technique with CMA-ES

Applied Soft Computing Journal
Sakshi Aggarwal, Sarsij Tripathi
exaly   +2 more sources

Bayesian CMA-ES

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we can derive the optimal update at each iteration step thanks to Bayesian statistics.
Eric Benhamou, David Saltiel, Sébastien Vérel   +2 more
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A2-CMA-ES: a hybrid CMA-ES with adaptive archive for complex engineering design

Expert Systems With Applications
Mao Xi, Longyan Xu, Zaihan He, Jiali Chen, Chenyang Bai, Ren Gao   +5 more
exaly   +2 more sources

CMA-ES

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be formulated in a unified and comparatively simple framework.
Nikolaus Hansen, Anne Auger
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Model complex control CMA-ES

Swarm and Evolutionary Computation, 2019
Abstract Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown great performance on nonseparable optimization problems largely due to its rotation-invariant feature. However, as the computational cost of the self-adaption operation is sensitive to the scale of problems, the performance of CMA-ES heavily suffers from the well-known curse ...
Xin Tong, Bo Yuan 0006, Bin Li 0025
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