19 papers · ranked by Valyu relevance
Broderick Crawford, Álex Paz, Ricardo Soto, Álvaro Peña Fritz + 7 more
Metaheuristics are a fundament pillar of Industry 4.0, as they allow for complex optimization problems to be solved by finding good solutions in a reasonable amount of computational time. One category of important problems in modern industry is that of binary problems, where decision variables can take values of zero…
Abdelmonem M. Ibrahim, Doaa A. Fakhry, Fares Al-Shargie, Jan Cornelis
Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method…
Reham Kamal, Eman Amin, Diaa Salama AbdElminaam, Rasha Ismail
Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization…
Łukasz Grodzki, Mateusz Slysz, Grzegorz Waligóra, Zhian Jia + 1 more
Quantum computing offers new possibilities for solving combinatorial optimization problems with rapidly growing search spaces. Among emerging hardware platforms, photonic quantum computers based on boson sampling provide a promising approach for sampling-based optimization methods. In this work, we investigate the…
Michael J. Bommarito
Sequence models for binary analysis are bottlenecked by byte-level tokenization: raw bytes waste precious context window capacity for transformers and other neural network architectures, and many existing text-oriented tokenizers fail on arbitrary 0x00–0xFF sequences. To address this issue, we introduce the Binary BPE…
Yifu Ding, Xianglong Liu, Shenghao Jin, Jinyang Guo + 1 more
Ultra low-bit quantization brings substantial efficiency for Transformer-based models, but the accuracy degradation and limited GPU support hinder its wide usage. In this paper, we analyze zero-point distortion in binarization and propose a Binary Weights & Ternary Activations (BWTA) quantization scheme, which projects…
Yanji Qu, Yaxuan Wang, Yan Wang, Haoru Tang + 1 more
The precision of CRISPR/Cas systems is fundamental to their application in plant and animal biotechnology. However, comprehensive off-target assessment remains a bottleneck, particularly in large, complex genomes where existing tools often suffer from prohibitive computational costs, poor search-space convergence, and…
Federico Bruzzone, Walter Cazzola
Quantifying the marginal impact of individual optimization passes underpins phase ordering, pass selection, optimization design, and analysis of pass/hardware interactions. In LLVM -- the standard backend for C/C++, Rust, and ML stacks via MLIR -- interactions among optimization passes, measurement noise, and pipeline…
Blaž Pšeničnik, Borko Bošković, Jan Popić, Janez Brest
Low autocorrelation binary sequences problem (LABS) is a hard combinatorial optimization challenge with important applications in communications, signal processing, and satellite navigation. This paper proposes a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to adaptively…
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…
Veronika Hendrychová, Karel Břinda
One important question in bacterial genomics is how to represent and search modern million-genome collections at scale. Phylogenetic compression effectively addresses this by guiding compression and search via evolutionary history, and many related methods similarly rely on tree- and ordering-based heuristics that…
Authors not listed
Designing efficient photoreactors remains challenging due to the complex interplay of light transport phenomena, shaped by reflection, scattering and absorption processes. Here, we introduce a workflow that integrates ray-tracing digital twins with multi-objective Bayesian optimization to autonomously design…
Guilherme E. Kundlatsch, Almiro P. S. Neto, Gabriela B. de Paiva, Elibio Rech + 2 more
Intrinsic transcription terminators are biological parts critical for controlling gene expression in natural genomes and are fundamental to the modularity and predictability of synthetic gene circuits. Despite their simplicity of structure and function, we have not yet been able to rationally engineer synthetic…
Shengnan Li, Taiju Yin, Heming Jia
As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary…
Clyde Meli, Vitezslav Nezval, Zuzana Komínková Oplatková, Victor Buttigieg + 1 more
Different bitstring representations offer different performance computations. This work describes three different bitstring representations: i) std::bitset, ii) Boost::dynamic\_bitset, and iii) a custom direct implementation, written in the C++ programming language. Their performance is benchmarked in the context of…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Binhe Chen, Yaodan Chen, Li Cao, Changzu Chen + 3 more
The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…