24 papers · ranked by Valyu relevance
Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann + 2 more
'Dimo Brockhoff'] COCO is a platform for Comparing Continuous Optimizers in a black-box setting. It aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. We present the rationals behind the development of the platform as a general…
Mohd Nadhir Ab Wahab, Samia Nefti-Meziani, Adham Atyabi, Catalin Buiu
'Catalin Buiu'] Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known…
Vahid Beiranvand, Warren Hare, Yves Lucet
Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide…
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer + 1 more
Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested 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…
Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng + 13 more
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a benchmark framework for Comprehensive Automated Research Performance Studies allowing to evaluate N optimizers on M benchmark tasks. In this…
Clemens Kreutz
Insufficient performance of optimization approaches for fitting of mathematical models is still a major bottleneck in systems biology. In this manuscript, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for increasing evidence of outcomes of…
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…
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…
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…
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…
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…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Jacob O. Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander
Optimization Authors: ['Jacob O. Tørring' 'Carl Hvarfner' 'Luigi Nardi' 'Magnus Själander'] Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of…
Tong Yue, Tao Li, Heming Jia, Laith Abualigah + 1 more
Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a…
Clemens Kreutz
Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an increased level of…
Zhe Zhang, Xiaobiao Huang, Minghao Song
Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and…
Helge Hass, Carolin Loos, Elba Raimundez Alvarez, Jens Timmer + 2 more
Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A…
Thanh Sang-To, Minh Hoang-Le, Magd Abdel Wahab, Thanh Cuong-Le
In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search…
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…
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…
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…
Kyriakos Georgiou, Zbigniew Chamski, Andrés Amaya García, David May + 1 more
'Kerstin Eder'] Abstract. Existing iterative compilation and machine-learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler's optimizer as part of…
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.…