25 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…
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…
Olga Speck, Thomas Speck, Sabine Baur, Michael Herdy + 3 more
'Laith Abualigah' 'Xuewen Xia'] With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic…
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…
Ahmed, Aram M., Hassan, Bryar A. + 10 more
This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multiobjective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these…
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…
Thounaojam Chinglemba, Soujanyo Biswas, Debashish Malakar, Vivek Meena + 2 more
'Vivek Meena' 'Debojyoti Sarkar' 'Anupam Biswas'] > Abstract. With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of…
Pauline Bennet, Denis Langevin, Chaymae Essoual, Abdourahman Khaireh-Walieh + 3 more
'Abdourahman Khaireh-Walieh' 'Olivier Teytaud' 'Peter R. Wiecha' 'A. Moreau'] Numerical optimization for the inverse design of photonic structures is a tool which is providing increasingly convincing results – even though the wave nature of problems in photonics makes them particularly complex. In the meantime, the…
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…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Na Zhang, Ziwei Jiang, Gang Hu, Abdelazim G. Hussien + 1 more
Attraction-Repulsion Optimisation Algorithm (AROA) is a newly proposed metaheuristic algorithm for solving global optimisation problems, which simulates the equilibrium relating to the attraction and repulsion phenomenon that occurs in the natural world, and aims to achieve a good balance between the development…
Authors not listed
We report the development and application of a scalable machine learning optimisation framework for batched multi-objective reaction optimisation. Through experimental data-derived benchmarks, we demonstrate our approach’s capacity to efficiently handle large parallel batches and high-dimensional search spaces…
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…
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…
Julien Diot, Hiroyoshi Iwata
Introduction Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not…
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…
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…
Philippe Preux, Rémi Munos, Michal Valko
We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective function evaluations allowed during the optimization. We consider an algorithm inspired by a continuous version of a multi-armed bandit problem which attacks this…
Serena Landers, Sahil Pontula, Shiekh Zia Uddin, Sachin Vaidya + 2 more
We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this…
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…
Peter L. Bartlett, Chris Junchi Li, Jingfeng Wu, Bin Yu
In the field of optimization, developing accelerated methods for solving minimax and fixed-point problems remains a fundamental challenge. This paper presents a novel family of dual accelerated algorithms that achieve optimal convergence rates for both minimax and fixed-point problems. By exploring new anchoring…
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…
Mengyuan Zhang, Wotao Yin, Mengchang Wang, Yangbin Shen + 6 more
'Youting Wu' 'Liang Zhao' 'Junqiu Pan' 'Jiang Hu' 'KuoLing Huang'] Abstract Numerical software is usually shipped with built-in hyperparameters. By carefully tuning those hyperparameters, significant performance enhancements can be achieved for specific applications. We developed MindOpt Tuner, a new automatic tuning…