29 papers · ranked by Valyu relevance
Sanpawat Kantabutra, Mehmet Cunkas
In many combinatorial optimization problems we want a particular set of k out of n items with some certain properties (or constraints). These properties may involve the k items. In the worst case a deterministic algorithm must scan n−k items in the set to verify the k items. If we pick a set of k items randomly and…
Raffaele Marino
This chapter delves into the realm of computational complexity, exploring the world of challenging combinatorial problems and their ties with statistical physics. Our exploration starts by delving deep into the foundations of combinatorial challenges, emphasizing their nature. We will traverse the class P, which…
Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet their direct application to NP-hard combinatorial problems (CPs) remains underexplored. In this work, we systematically investigate the reasoning abilities of LLMs on a variety of NP-hard combinatorial optimization tasks and introduce…
Yongliang Sun, Ismail Alkhouri, Cheng-Han Huang, Alvaro Velasquez + 2 more
Recent studies suggest that gradient-based methods applied to relaxed box-constrained Quadratic Unconstrained Binary Optimization (QUBO) formulations can outperform classical heuristics in some large-scale regimes, often relying on heavy parallelization. However, these methods still underperform heuristics in other…
Sara Moreno-Paz, Rianne van der Hoek, Elif Eliana, Vitor A.P. Martins dos Santos + 2 more
Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex…
Haosen Chen, Shailan Deng, Tian Chen, Xiangdong Zhang
Constrained combinatorial optimization (CCO) problems are prevalent across various fields and represent key challenges in computational science and engineering. Although numerous classical and quantum algorithms have been proposed to tackle these problems, substantial limitations still persist. Classical algorithms…
Kunwei Wu, Liangshun Wang, Mingming Liu, Changsheng Zhang + 1 more
'Haitong Zhao'] High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and…
Javier Alcazar, Mohammad Ghazi Vakili, Can B. Kalayci, Alejandro Perdomo-Ortiz
'Alejandro Perdomo-Ortiz'] Devising an efficient exploration of the search space is one of the key challenges in the design of combinatorial optimization algorithms. Here, we introduce the Generator-Enhanced Optimization (GEO) strategy: a framework that leverages any generative model (classical, quantum, or…
Michael Emmerich, André Deutz
and Case Studies Authors: ['Michael Emmerich' 'André Deutz'] | 1 | | Introduction | | | | | | 4 | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 1.1 | Viewing mulicriteria optimization as a task in system design and | | | | | | | | | | | | | analysis | . | | | | | 5 | | | | | | 1.2…
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou Ammar
This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field. Current MCBO papers often introduce non-diverse or non-standard benchmarks to evaluate their methods, impeding the proper…
Naoya Onizawa, Takahiro Hanyu
This article critically investigates the limitations of the simulated annealing algorithm using probabilistic bits (pSA) in solving large-scale combinatorial optimization problems. The study begins with an in-depth analysis of the pSA process, focusing on the issues resulting from unexpected oscillations among p-bits.…
Md. Rafiqul Islam, Md. Shahidul Islam, Pritam Khan Boni, Aldrin Saurov Sarker + 2 more
'Aldrin Saurov Sarker' 'Md. Asif Anam' 'Ali Safaa Sadiq'] The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning…
Menghao Tang, Zimin Liang, Miqing Li
Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the scalability of multi-objective evolutionary algorithms (MOEAs) has predominantly focussed on continuous problems. However, multi-objective…
Wenbin Liao, Gengrong Gao, Haoyu Wang, Xingcun Fan + 5 more
The rational design of high-performance microbial cell factories remains a central challenge in sustainable biomanufacturing due to the complexity of metabolic networks and the difficulty of predicting synergistic genetic interventions. Despite recent advances in strain design algorithms, predicting combinatorial…
Paul van Lent, Rianne van der Hoek, Sara Moreno Paz, Irsan Kooi + 4 more
Combinatorial pathway optimization is an important tool for industrial metabolic engineering to improve titer, yield, or productivity of strains. Machine learning has been increasingly applied on many aspects of the Design-Build-Test-Learn (DBTL) cycle, an engineering framework that aims to navigate through the large…
Abbas Shah Syed, Daniel Sierra-Sosa, Anup Kumar, Adel Elmaghraby + 4 more
One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage…
Tao Hong, William R. Stauffer
Complex economic decisions are often combinatorial: they require individuals to select from many alternatives under strict constraints on time, resources, and energy. Combinatorial reasoning is the cognitive process that enables decision makers to construct and evaluate multiple potential solutions in the face of these…
Wenbin Liao, Gengrong Gao, Xingcun Fan, Haoyu Wang + 4 more
The design of high-performance microbial cell factories is essential for advancing sustainable biomanufacturing. However, the intricate nature of metabolic networks complicates the prediction of genetic interventions, making strain optimization a challenging combinatorial problem. Here we present a novel computational…
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…
Fatma A. Hashim, Reham R. Mostafa, Ruba Abu Khurma, Raneem Qaddoura + 1 more
based on modified Sea Horse Optimizer Authors: ['Fatma A. Hashim' 'Reham R. Mostafa' 'Ruba Abu Khurma' 'Raneem Qaddoura' 'Pedro Á. Castillo'] 1 Faculty of Engineering, Helwan University, Egypt. Email: fatma_hashim@h-eng.helwan.edu.eg and 2Research Institute of Sciences and Engineering (RISE), University of Sharjah…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Arseny Shur, Ido Tziony, Yaron Orenstein
Minimizers are sampling schemes which are ubiquitous in almost any high-throughput sequencing analysis. Assuming a fixed alphabet of size σ, a minimizer is defined by two positive integers k, w and a linear order ρ on k-mers. A sequence is processed by a sliding window algorithm that chooses in each window of length w…
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…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Eric Smith, Harrison B. Smith, Jakob Lykke Andersen
We consider problems in the functional analysis and evolution of combinatorial chemical reaction networks as rule-based, or three-level systems. The first level consists of rules, realized here as graph-grammar representations of reaction mechanisms. The second level consists of stoichiometric networks of molecules and…
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…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Hozan K. Hamarashid, Bryar A. Hassan, Tarik A. Rashid
Fitness dependent optimizer (FDO) is considered one of the novel swarm intelligent algorithms. Recently, FDO has been enhanced several times to improve its capability. One of the improvements is called improved FDO (IFDO). However, according to the research findings, the variants of FDO are constrained by two primary…
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…