23 papers · ranked by Valyu relevance
Xiaoqiu Ma
The traditional methods deal with large sample data sets of architectural engineering drawings and they have high time complexity and space complexity as well. Their searching time is long and sometimes the results are unsatisfactory. Therefore, this paper proposes an optimization method designed for architectural…
Shashank Shekhar, Adesh Bansode, Asif Salim
—Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right…
Gabriel D. Maher, Stephen Boyd, Mykel J. Kochenderfer, Cristian Matache + 4 more
'Cristian Matache' 'Dylan Reuter' 'Alex Ulitsky' 'Slava Yukhymuk' 'Leonid Kopman'] We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limit…
Farkhanda Abbas, Feng Zhang, Muhammad Ismail, Garee Khan + 6 more
'Javed Iqbal' 'Abdulwahed Fahad Alrefaei' 'Mohammed Fahad Albeshr' 'Waheb Abdullah' 'AbdulRahman Alsewari' 'Mario De Oliveira'] Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to…
Chenyu Wu, Nuozhou Wang, Casey Garner, Kevin Leder + 1 more
In this paper, we introduce a new optimization algorithm that is well suited for solving parameter estimation problems. We call our new method cubic regularized Newton with affine scaling (CRNAS). In contrast to so-called first-order methods which rely solely on the gradient of the objective function, our method…
Owen Madin, Michael Shirts
Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
H. Haddad, Thibault Falque, P. Talbot, Pascal Bouvry
The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires expert knowledge. In this paper, we introduce probe and solve algorithm, a novel two-phase framework…
Fabian Fröhlich, Peter K. Sorger, Hugues Berry
Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using…
Geethu Joy, Christian Huyck, Xin‐She Yang
Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence the behaviour of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure the algorithm used for optimization may perform well and can be…
Nishant Gadde
Gaussian Process (GP) models have also become extremely useful for optimization under uncertainty algorithms, especially where the objective functions are costly to compute. Yet, the more classical methods usually adopt strategies that, in certain circumstances, might be effective but not flexible to be applied to a…
Matt J. Owen, Gary R. Mirams
Ion channel models present many challenging optimisation problems. These include unidentifiable parame- ters, noisy data, unobserved states, and a combination of both fast and slow timescales. This can make it difficult to choose a suitable optimisation routine a priori. Nevertheless, many attempts have been made to…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Azadeh Hassanpour, Johannes Geibel, Henner Simianer, Torsten Pook + 1 more
'G de los Campos'] Title: Abstract In recent years, breeding programs have increased significantly in size and complexity, with various highly interdependent parameters and many contrasting breeding goals. As a result, resource allocation in these programs has become more complex, and deriving an optimal breeding…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Søren Bertelsen, Sigurd Carlsen, Søren Furbo, Morten Bormann Nielsen + 2 more
an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use Authors: ['Søren Bertelsen' 'Sigurd Carlsen' 'Søren Furbo' 'Morten Bormann Nielsen' 'Aksel Obdrup' 'Rolf Taaning'] ProcessOptimizer is a Python package designed to provide…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Priyam Das, Debsurya De, Raju Maiti, Mona Kamal + 4 more
'Katherine A. Hutcheson' 'Clifton D. Fuller' 'Bibhas Chakraborty' 'Christine B. Peterson'] Background In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal…