8 results on '"DIFFERENTIAL evolution"'
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2. Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model
- Author
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Noman, Nasimul, Palafox, Leon, Iba, Hitoshi, Suzuki, Yasuhiro, editor, and Nakagaki, Toshiyuki, editor
- Published
- 2013
- Full Text
- View/download PDF
3. Approximate Bayesian Inference.
- Author
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Alquier, Pierre and Alquier, Pierre
- Subjects
Mathematics & science ,Research & information: general ,Approximate Bayesian Computation ,Bayesian inference ,Bayesian sampling ,Bayesian statistics ,Bethe free energy ,Edward-Sokal coupling ,Gaussian ,Gibbs posterior ,Hamilton Monte Carlo ,Kullback-Leibler divergence ,Langevin Monte Carlo ,Langevin dynamics ,Laplace approximations ,MCMC ,MCMC-SAEM ,Markov Chain Monte Carlo ,Markov chain ,Markov chain Monte Carlo ,Markov kernels ,Monte Carlo integration ,PAC-Bayes ,PAC-Bayes theory ,Riemann Manifold Hamiltonian Monte Carlo ,Sequential Monte Carlo ,Stiefel manifold ,approximate Bayesian computation ,approximate Bayesian computation (ABC) ,bifurcation ,complex systems ,control variates ,data imputation ,data streams ,deep learning ,differential evolution ,differential privacy (DP) ,discrete state space ,dynamical systems ,entropy ,ergodicity ,expectation-propagation ,factor graphs ,fixed-form variational Bayes ,generalisation bounds ,gradient descent ,greedy algorithm ,hyperparameters ,integrated nested laplace approximation ,machine learning ,mean-field ,message passing ,meta-learning ,network modeling ,network variability ,neural networks ,no free lunch theorems ,non-reversible dynamics ,online learning ,online optimization ,particle flow ,principal curves ,priors ,probably approximately correct ,regret bounds ,robustness ,sequential Monte Carlo ,sequential learning ,sleeping experts ,sparse vector technique (SVT) ,statistical learning theory ,statistical mechanics ,stochastic gradients ,stochastic volatility ,thinning ,variable flow ,variational Bayes ,variational approximations ,variational free energy ,variational inference ,variational message passing - Abstract
Summary: Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis-Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC-Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.
4. Applied Mathematics to Mechanisms and Machines.
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Rubio Alonso, Higinio, Caballero, Alejandro, Meneses Alonso, Jesus, Rubio Alonso, Higinio, and Soriano-Heras, Enrique
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History of engineering & technology ,Technology: general issues ,COVID-19 ,FEM analysis ,Frenet trihedral ,GRU ,Levenberg-Marquardt algorithm ,Miura-ori pattern ,NMR reconstruction ,Pareto front ,adaptive rejection control ,aeroengine ,air flow medical sensor ,approximate entropy ,autoencoder ,biomechanics ,compliance ,compliant joint ,compliant mechanism ,convergence ,coupled motion ,decoupled motion ,delayed feedback ,denoising ,deployable mechanism ,differential evolution ,dimensional synthesis ,dispersion relation ,distance to a singularity ,dynamic balancing ,effort mathematical models ,elbow joint ,elliptic integrals ,emergency air flow sensor ,fault classification ,flexible rotor ,flexure ,flexure-based mechanism ,fractal ,fully Cartesian coordinates ,geometric algebra ,helical spring ,heteroclinic bifurcation ,hybrid compliant mechanisms ,hybrid optimization ,hydrodynamic journal bearing ,jump ,kinematics ,land vehicle ,lateral dynamics ,local minima ,low-cost air flow sensor ,multiobjective optimization ,nanostructures ,natural frequency ,neural networks ,non-linear systems ,numerical methods ,optimization ,origami inspired design ,path analysis ,path generation ,phase space reconstruction ,predictive suspension ,random forest ,relative rotation ,reverse engineering ,rotor group ,rotordynamic ,safe basin ,safety vehicle index ,serial robotic manipulators ,singularity identification ,sinusoidal disturbance ,six-bar mechanism ,slider-crank mechanism ,smooth sheet attachment ,sprung mass ,stiffness ,support vector machines ,suspension algorithm ,three-legged parallel mechanism ,track parameters ,vehicle mathematical modeling ,vibration ,zipper-coupled tubes - Abstract
Summary: This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section "Engineering Mathematics". The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics.
5. Mathematical Modeling with Differential Equations in Physics, Chemistry, Biology, and Economics.
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Palestini, Arsen and Palestini, Arsen
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Mathematics & science ,Research & information: general ,COVID-19 transmission ,Hopfield artificial neural networks ,Kepler-type orbits ,Lie symmetries ,Runge-Kutta ,SEIR ODE model ,border collision bifurcation ,commuting operator ,convalescent plasma transfusion (CPT) ,degeneracy ,differential equation ,differential equations with discontinuous right-hand sides ,differential evolution ,dynamical systems ,economics ,eigenvalues ,elliptic PDE ,financial markets ,fundamental analysis ,gauss hypergeometric functions ,initial value problem ,investment style ,ladder operator ,market maker ,mixing process ,n/a ,networks ,oscillatory problems ,q-Hermite polynomials ,relationships ,simulated annealing ,simultaneous differential equations ,splitted separation ,stability ,technical analysis ,variable production rate ,zeros of q-Hermite polynomials - Abstract
Summary: This volume was conceived as a Special Issue of the MDPI journal Mathematics to illustrate and show relevant applications of differential equations in different fields, coherently with the latest trends in applied mathematics research. All the articles that were submitted for publication are valuable, interesting, and original. The readers will certainly appreciate the heterogeneity of the 10 papers included in this book and will discover how helpful all the kinds of differential equations are in a wide range of disciplines. We are confident that this book will be inspirational for young scholars as well.
6. Kinematics and Robot Design IV, KaRD2021.
- Author
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Di Gregorio, Raffaele and Di Gregorio, Raffaele
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History of engineering & technology ,Technology: general issues ,Chebyshev and least-square approximations ,EP control ,Hill's model ,MATLAB ,RoboMech ,SimScape ,SimScape Multibody ,Simulink ,Stephenson III ,Watt II ,autonomous underwater vehicles ,bio-inspired exoskeleton ,biomimetics ,cable-driven system ,collaborative robots ,computational modelling ,design optimization ,dexterity ,differential evolution ,dimensional synthesis ,dynamic modelling ,elderly ,ergonomics ,finger grip ,functional electrical stimulation ,indeterminate linkages ,inspection ,kinematic synthesis and analysis ,kinematic synthesis of robots ,kinematics ,kinetostatics ,machine design ,mechanism optimization ,mixed-position synthesis ,multibody systems ,over-actuation ,parallel manipulator ,performance index ,performance tricycle ,pinch assistant ,pinch force ,planar linkages ,rehabilitation robotics ,robot design ,robot modeling and simulation ,robotics ,screw theory ,simulation ,six-bar linkage ,skill-based programming ,small-scale production ,topology ,torque adjusting mechanism ,transmission systems ,twist systems ,underwater robots ,upper limb rehabilitation ,usability ,useful workspace - Abstract
Summary: This volume collects the papers published on the special issue "Kinematics and Robot Design IV, KaRD2021" (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal "MDPI Robotics". KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on "mechanisms and robotics". KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects.
7. Evolutionary Computation 2020.
- Author
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Wang, Gai-Ge, Alavi, Amir, and Wang, Gai-Ge
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Technology: general issues ,0-1 knapsack problem ,Pareto optimality ,Pareto-front ,Q-learning ,WOA ,ant colony optimization ,assortative mating ,bWOA-S ,bWOA-V ,binary whale optimization algorithm ,bug detection ,citation ,classification ,coevolution ,constrained optimization ,cuckoo search algorithm ,decomposition-based multi-objective optimisation ,differential evolution ,dimensionality reduction ,discrete artificial bee colony algorithm ,diversity preservation ,dominance ,dynamic learning ,elephant herding optimization ,engineering optimization ,evolutionary algorithm ,evolutionary algorithms (EAs) ,evolutionary computation ,feature selection ,fuzzing ,fuzzy hybrid flow shop scheduling ,game feature ,game simulation ,game trees ,geoelectric model ,global optimization ,green shop scheduling ,grey wolf optimizer ,h-index ,iterated local search ,knapsack problem ,knowledge transfer ,krill herd ,magnetotelluric ,many-objective optimization ,memetic algorithm ,menu planning problem ,metaheuristic ,minimize makespan ,minimize total energy consumption ,multi-indicators ,multi-metric ,multi-objective optimization ,multi-resources ,multi-task evolutionary computation ,multi-task optimization ,mutation ,one-dimensional inversions ,opposite path ,opposition-based learning ,optimization problem ,particle swarm optimization ,path discovery ,performance indicators ,playtesting ,playtesting metric ,premature convergence ,quantum ,quantum computing ,ranking ,seed schedule ,self-adaptive step size ,simulated annealing ,single objective optimization ,single-objective optimization ,success-history ,swarm intelligence ,traveling salesman problems ,travelling salesman problem ,turning-based mutation ,unified search space ,universities ranking ,validation ,whale optimization algorithm - Abstract
Summary: Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.
8. Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb.
- Author
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Coello, Carlos, Coello, Carlos, Goodman, Erik, Miettinen, Kaisa, Saxena, Dhish, Schütze, Oliver, and Thiele, Lothar
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Computer science ,Information technology industries ,COVID-19 data ,MOO ,NSGA-II ,RBDO ,archiving ,association rule mining ,auto-configuration and auto-design of metaheuristics ,causality measures ,chaos control theory ,constraint handling ,convergence ,data mining ,differential evolution ,evolutionary algorithms ,evolutionary multi-objective optimization ,genetic algorithm ,grouping genetic algorithm ,grouping mutation operator ,grouping problem ,hypervolume indicator ,hypervolume scalarization ,importance sampling ,impulse response ,interactive optimization ,knowledge discovery ,large-scale multi-objective optimization ,many objectives ,mass-damper-spring termination ,multi-criteria decision making ,multi-objective evolutionary algorithm ,multi-objective optimization ,multi-objective reliability-based design optimization ,n/a ,newton method ,objectives reduction ,particle filter ,real-world problems optimization ,reconfigurable manufacturing system ,reliability ,reliability analysis ,rod vibration ,scarce data ,shifting vector approach ,simple cell mapping ,simulation ,surrogate ,transfer learning ,unrelated parallel-machine scheduling - Abstract
Summary: This volume is a reprint of the Honorary Special Issue dedicated to the 60th birthday of Professor Dr. Kalyanmoy Deb, published in the journal Mathematical and Computational Applications (MCA). Kalyanmoy Deb has been a pioneer and highly impactful and influential proponent of Evolutionary Multi-objective Optimization (EMO) since 1994. He is currently a Koenig Endowed Chair Professor and University Distinguished Professor in the Department of Electrical and Computer Engineering at Michigan State University, USA, and holds additional appointments in Mechanical Engineering and in Computer Science and Engineering. Professor Deb's research interests are in evolutionary optimization and its application in multi-objective optimization, modeling, machine learning, and in multi-objective decision making. He has been a visiting professor at various universities across the world, including IITs in India, Aalto University in Finland, the University of Skovde in Sweden, and Nanyang Technological University in Singapore. He was awarded the IEEE Evolutionary Computation Pioneer Award, the Infosys Prize, the TWAS Prize in Engineering Sciences, the CajAstur Mamdani Prize, the Distinguished Alumni Award from IIT Kharagpur, the Edgeworth Pareto Award, the Bhatnagar Prize in Engineering Sciences, and the Bessel Research Award from Germany. He is a fellow of IEEE, ASME, and three Indian science and engineering academies.
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