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Search · four archives
26 papers · ranked by Valyu relevance
Samuel Kienzle, Lisa Junghans, Anja Wittmann, Stefan Wieschalka + 4 more
Applying a single parameter set to describe complex mammalian kinetics often is too simplistic, as it fails to capture sensitive cell-to-environment interactions that may be exploited to optimize production performance. To resolve this time dependency, intra-experimental parameter shifts as part of design of dynamic…
Héctor Fernández, María Alicia Zon, Sabrina Antonella Maccio, Rubén Darío Alaníz + 7 more
'Rubén Darío Alaníz' 'Aylen Di Tocco' 'Roodney Alberto Carrillo Palomino' 'Jose Alberto Cabas Rodríguez' 'Adrian Marcelo Granero' 'Fernando J. Arévalo' 'Sebastian Noel Robledo' 'Gastón Darío Pierini'] We summarize the application of multivariate optimization for the construction of electrochemical biosensors. The…
Shijun Li, Hilaf Hasson, Joydeep Ghosh
Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein multiple agents collaborate and communicate with each other, have exhibited enhanced capabilities in complex tasks, such as high-quality…
Nicola Hallmann, Catalina Guerra-Cornejo, Karl Burgess, Charlotte Merzbacher + 1 more
The optimization of culture media is critical for improving the efficiency and cost of cellular production systems. Traditional approaches often rely on extensive experimental trials or statistical methods, which can be both costly and time-consuming. Here, we present gsMOBO, a computational approach for media design…
Jie Feng, Mingdong He, Lei Jin, Hui Dou + 1 more
Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic…
Pengzhou Chen, Tao Chen, Miqing Li
—Software configuration tuning is essential for optimizing a given performance objective (e.g., minimizing latency). Yet, due to the software's intrinsically complex configuration landscape and expensive measurement, there has been a rather mild success, particularly in preventing the search from being trapped in local…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Kaan Gökcesu, Hakan Gökcesu
—This study presents an effective global optimization technique designed for multivariate functions that are Holder ¨ continuous. Unlike traditional methods that construct lower bounding proxy functions, this algorithm employs a predetermined query creation rule that makes it computationally superior. The algorithm's…
Mohammad Dehghani, Pavel Trojovský, Mikołaj Leszczuk, Szymon Łukasik + 1 more
'Szymon Szott'] With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Xuelei Wang, Jana Zweerings, Michael Lührs, Fengyu Cong + 5 more
Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be…
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…
Carlos Llopis-Albert, Francisco Rubio, Carlos Devece, Shouzhen Zeng
Vehicle handling and stability performance and ride comfort is normally assessed through standard field test procedures, which are time consuming and expensive. However, the rapid development of digital technologies in the automotive industry have enabled to properly model and simulate the full-vehicle dynamics, thus…
Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an…
Hazim Albedran, Shaymaa Alsamia, Edina Koch
This article presents the Flower Fertilization Optimization Algorithm (FFO), a novel bio-inspired optimization technique inspired by the natural fertilization process of flowering plants. The FFO emulates the behavior of pollen grains navigating through the search space to fertilize ovules, effectively balancing…
Zoe Kutulakos, Patrick Slade
Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric…
Zhiqiu Chen, Zong-Gan Chen, Yuncheng Jiang, Zhi‐Hui Zhan
Algorithms in Solving Two Types of Real-World Problems Authors: ['Zhiqiu Chen' 'Zong-Gan Chen' 'Yuncheng Jiang' 'Zhi‐Hui Zhan'] Abstract—In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Ke Cheng, Zhi Wang, Wen Hu, Tiannuo Yang + 2 more
—A service-level objective (SLO) is a target performance metric of service that cloud vendors aim to ensure. Delivering optimized SLOs can enhance user satisfaction and improve the competitiveness of cloud vendors. As large language models (LLMs) are gaining increasing popularity across various fields, it is of great…
Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C. Burkhard + 5 more
A performance metric is a tool to measure the correctness of a trained Machine Learning (ML) model. Numerous performance metrics have been developed for classification problems making it overwhelming to select the appropriate one since each of them represents a particular aspect of the model. Furthermore, selection of…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Zhou He, Biao Tang, Fei Luan, Subhas Mukhopadhyay
According to the characteristics of flexible job shop scheduling problems, a dual-resource constrained flexible job shop scheduling problem (DRCFJSP) model with machine and worker constraints is constructed such that the makespan and total delay are minimized. An improved African vulture optimization algorithm (IAVOA)…
Qingrui Li, Yongquan Zhou, Qifang Luo, Dragan Pamucar
Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two…
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…
Authors not listed
This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and…
Li, Jinbo, Izakian, Hesam + 4 more
Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous…