20 papers · ranked by Valyu relevance
Luca Baronti, Marco Castellani
Problems Authors: ['Luca Baronti' 'Marco Castellani'] This work proposes a framework of benchmark functions designed to facilitate the creation of test cases for numerical optimisation techniques. The framework, written in Python 3, is designed to be easy to install, use, and expand. The collection includes some of the…
Xin‐She Yang
New algorithms for optimization appear regularly in the current literature and hundreds of papers are being published every year, about new algorithms and their variants as well as their applications. Obviously, new algorithms have to be tested and validated using various known problems with known optimality locations.…
Martin Grambow, Tim Dockenfuß, Trever Schirmer, Nils Japke + 1 more
'David Bermbach'] In this paper, we describe a wrapper approach that enables the Randomized Multiple Interleaved Trials (RMIT) benchmark execution methodology in FaaS environments and use bootstrapping percentile intervals to derive more accurate confidence intervals of detected performance changes. We evaluate our…
M.Z. Naser, Mohammad Khaled al-Bashiti, Arash Teymori Gharah Tapeh, Armin Dadras Eslamlou + 6 more
Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions Authors: ['M.Z. Naser' '\u202c\u202c\u202cMohammad Khaled al-Bashiti' 'Arash Teymori Gharah Tapeh' 'Armin Dadras Eslamlou' 'Ahmed Z. Naser' 'Venkatesh Kodur' 'Rami Hawileeh' 'Jamal A. Abdalla' 'Nima Khodadadi' 'Amir H. Gandomi'] In the…
Jiahao Fan, Ying Li, Tan Wang, Seyedali Mirjalili
Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a…
David Issa Mattos, Lucas Ruud, Jan Bosch, Helena Holmström Olsson
Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many desired qualities for benchmark suites, such as diverse topology, different difficulties, scalability, representativeness of real-world…
Rebecca F. Alford, Jeffrey J. Gray
Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane…
Martin Grambow, Christoph Laaber, Philipp Leitner, David Bermbach + 1 more
'Muhammad Aleem'] Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to…
Francesca Console, Giuseppe D’Aquanno, Giuseppe Antonio Di Luna, Leonardo Querzoni + 1 more
'Leonardo Querzoni' 'Aswani Kumar Cherukuri'] In this article we propose the first multi-task benchmark for evaluating the performances of machine learning models that work on low level assembly functions. While the use of multi-task benchmark is a standard in the natural language processing (NLP) field, such practice…
Xiaoqi Cabiria Liang, Nick Robertson, Marni Torkel, Sanghyun Kim + 3 more
The rapid growth of computational methods for the computational biology field highlights the critical role of benchmarking in guiding method selection. However, there is no standardised data structure that effectively links and stores datasets, performance metrics and available ground truth. Without such a unified and…
Daniel R. Little, Richard M. Shiffrin, Simon M. Laham
Graphical perception is an important part of the scientific endeavour, and the interpretation of graphical information is increasingly important among educated consumers of popular media, who are often presented with graphs of data in support of different policy positions. However, graphs are multidimensional and data…
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…
Izaskun Mallona, Almut Luetge, Ben Carrillo, Daniel Incicau + 4 more
bioinformatics Authors: ['Izaskun Mallona' 'Almut Luetge' 'Ben Carrillo' 'Daniel Incicau' 'Reto Gerber' 'Anthony Sonrel' 'Charlotte Soneson' 'Mark D. Robinson'] We describe an alpha version of a new benchmarking system, Omnibenchmark, to facilitate benchmark formalization and execution in solo and community efforts.…
Shuvendu K. Lahiri, Chao Wang, Priyanka Golia, Subhajit Roy + 1 more
'Kuldeep S. Meel'] Boolean functional synthesis is a fundamental problem in computer science with wide-ranging applications and has witnessed a surge of interest resulting in progressively improved techniques over the past decade. Despite intense algorithmic development, a large number of problems remain beyond the…
Pavan Kumar Behara, Hyesu Jang, Joshua Horton, Trevor Gokey + 6 more
A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient…
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…
Authors not listed
The incredible advances in deep learning architectures have not only led to successful protein structure prediction methods, but also to many interesting new methods related to protein-ligand docking and co-folding. The most recent biomolecular foundation model, Boltz-2, even claimed to approach the performance of…
Ryan Chard, Tyler J. Skluzacek, Zhuozhao Li, Yadu Babuji + 5 more
'Anna Woodard' 'Ben Blaiszik' 'Steven Tuecke' 'Ian Foster' 'Kyle Chard'] Growing data volumes and velocities are driving exciting new methods across the sciences in which data analytics and machine learning are increasingly intertwined with research. These new methods require new approaches for scientific computing in…
Authors not listed
Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based docking approaches has opened up new possibilities for enhancing VS technologies. In this study, we explore the integration of DiffDock-L, a leading ML-based pose…
Fergus Boyles, Charlotte M Deane, Garrett Morris
Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes. We…