23 papers · ranked by Valyu relevance
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…
Nicholas A. G. Johnson, Liezel Tamon, Xin Liu, Aleksandr B. Sahakyan
Many calculations in computational biology necessitate a use of a probabilistic optimisation protocol to determine a set of parameters that capture the system at a desired state in the configurational space. Here, we developed a flexible optimisation engine in R that can be plugged to any, simple or complex, modelling…
Luke Wolfenden, Katarzyna Bolsewicz, Alice Grady, Sam McCrabb + 25 more
'Melanie Kingsland' 'John Wiggers' 'Adrian Bauman' 'Rebecca Wyse' 'Nicole Nathan' 'Rachel Sutherland' 'Rebecca Kate Hodder' 'Maria Fernandez' 'Cara Lewis' 'Natalie Taylor' 'Heather McKay' 'Jeremy Grimshaw' 'Alix Hall' 'Joanna Moullin' 'Bianca Albers' 'Samantha Batchelor' 'John Attia' 'Andrew Milat' 'Andrew Bailey'…
Johann Sienz, Mauro S. Innocente
The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature. While particle swarm optimizers share such advantages, they outperform evolutionary algorithms in that they require lower computational cost and easier implementation, involving no operator…
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…
Shannon Bonke, Giovanni Trezza, Luca Bergamasco, Hongwei Song + 4 more
The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimisation approaches are poorly suited to multi-variable multi-metric photocatalytic systems because they aim to optimise one performance metric while sacrificing the…
David Palma-Heredia, Marta Verdaguer, Vicenç Puig, Manuel Poch + 2 more
'Miquel Àngel Cugueró-Escofet' 'Ernest W. Tollner'] Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its…
Sam McCrabb, Kaitlin Mooney, Benjamin Elton, Alice Grady + 2 more
'Sze Lin Yoong' 'Luke Wolfenden'] Background Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public…
Kobi Felton, Jan Rittig, Alexei Lapkin
In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past…
Erin Nolan, Luke Wolfenden, Taylor Benn, Elizabeth Holliday + 3 more
'Daniel Barker' 'Christopher Oldmeadow' 'Alix Hall'] Background Optimisation is the iterative process to improve a health intervention or implementation strategy within resource constraints. This review aimed to identify which study designs are being used to evaluate the optimisation of health interventions and…
J. Hadida, S.N. Sotiropoulos, R.G. Abeysuriya, M.W. Woolrich + 1 more
The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local…
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…
Kobi Felton, Jan Rittig, Alexei Lapkin
In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past…
Achim Langenbucher, Nóra Szentmáry, Alan Cayless, Jascha Wendelstein + 2 more
In this context, formula constants were optimised for statistical metrics of the SEQ prediction error PE. For the statistical metrics we used: the sum of squared PE (SoSPE) which minimises the ‘energy’ of the prediction error, the sum of the absolute PE (SoAPE, as typically used in scientific reports on the performance…
Ravi Umadi
Accurate spatial localisation of free-flying echolocating bats is foundational for resolving fine-scale flight behaviour, prey interception, and spatial decision-making in natural environments. Acoustic localisation using microphone arrays is widely employed for this purpose, yet array geometries in field studies are…
Mahyuddin K. M. Nasution
Optimization has been becoming a central of studies in mathematic and has many areas with different applications. However, many themes of optimization came from different area have not ties closing to origin concepts. This paper is to address some variants of optimization problems using ontology in order to building…
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…
Daniel Gaissmaier, Matthias van den Borg, Donato Fantauzzi, Timo Jacob
In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in…
João Paulo Papa, Gustavo Henrique de Rosa, Douglas Rodrigues, Xin‐She Yang
'Xin‐She Yang'] Optimization techniques play an important role in several scientic and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques…
Frank Hutter, Holger H. Hoos, Kevin Leyton‐Brown, T. Stuetzle
The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm's performance on a given class of…
Peng Wang, Gang Xin, Yuwei Jiao
—In this letter, by establishing the Schrodinger equa- ¨ tion of the optimization problem, the optimization problem is transformed into a constrained state quantum problem with the objective function as the potential energy. The mathematical relationship between the objective function and the wave function is…
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…
Marc Claesen, Jaak Simm, Dušan Popović, Yves Moreau + 1 more
Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in…