28 papers · ranked by Valyu relevance
Shipeng Cen, Ying Tan
The field of automated algorithm design has been advanced by frameworks such as EoH, FunSearch, and Reevo. Yet, their focus on algorithm evolution alone, neglecting the prompts that guide them, limits their effectiveness with LLMs—especially in complex, uncertain environments where they nonetheless implicitly rely on…
Maxime Bouscary, Manxi Wu, Saurabh Amin
Large language models (LLMs) have emerged as powerful tools for automatic algorithm design (AAD). However, existing pipelines remain inefficient. They operate at the granularity of full algorithms, redundantly rewriting recurring substructures and discarding low-fitness candidates that may contain valuable algorithmic…
Chuyang Xiang, Yichen Wei, Jiale Ma, Handing Wang + 1 more
Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic heuristic design. However, most existing LHHs just perform well in optimizing a single function within a pre-defined solver. Their single-layer evolution makes them not effective enough to write a competent complete…
Zhiyao Zhang, Shenghao Wu, Xingyu Wu, Kay Chen Tan
LLM-assisted evolutionary search (LES) has emerged as a promising paradigm for automated algorithm design. However, existing methods usually suffer from two inherent limitations when facing the automated design of real-world complex algorithms that usually consist of multiple components. The first limitation is that…
Jiawei Xu, Fulu Wei, Wei–Neng Chen
Automatic Heuristic Design (AHD) has gained traction as a promising solution for solving combinatorial optimization problems (COPs). Large Language Models (LLMs) have emerged and become a promising approach to achieving AHD, but current LLM-based AHD research often only considers a single role. This paper proposes…
Zhimeng Zhou, Yang Nan, Minjie Mou, Yuntao Qian + 16 more
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug…
Rui Zhang, Jiaming Guo, Shuyao Cheng, Yunji Chen
An overview of fully automated processor chip design, including its research motivations, three key challenges, and the overall framework with core components developed to address these challenges.
Tena Škalec, Marko Đurasević, Heming Jia
The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as…
Wang, Hui, Liu, Yang + 4 more
Automatic Heuristic Design (AHD) is an effective framework for solving complex optimization problems. The development of large language models (LLMs) enables the automated generation of heuristics. Existing LLM-based evolutionary methods rely on population strategies and are prone to local optima. Integrating LLMs with…
Bowen Wu, Renbin Xiao, Jia Zhao
Biological groups exhibit an intrinsic capacity for self-organization, engendering complex and intelligent cooperative behaviors-a capability highly sought after for robotic swarms. This paper proposes an architecture for constructing swarm intelligence in cooperative tasks, termed Cooperation Emergence and Strategy…
Huan Wang, Shu Yang, Zhen Chen, Haoyu Sun + 6 more
Lightweight Unmanned Aerial Vehicles (UAVs) have limited space, low payload capacity, and constrained power supply capabilities. Therefore, their payloads are constrained by size, weight, and power (SWaP). Thus, designing edge-side signal processing architectures for the payloads of UAVs faces severe challenges.…
Luuk Oerlemans, Steven Westerhof, Theo Hofman
This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformative paradigm. We propose computational design synthesis (CDS), a framework utilising deep learning and generative AI to automate the creation of novel systems. Two case…
Mohamed Mamdouh, Ahmed Khalid, Reem Mahmoud, Osama Desouki + 1 more
This paper addresses a critical challenge in Printed Circuit Board (PCB) manufacturing by proposing an AI-driven, fully automated drilling machine that employs sophisticated path-planning techniques. Current methodologies often fail to adequately assess designs with varying hole sizes, diverse component placements, and…
Authors not listed
Bridging AI and self-driving laboratories, we introduce the first fully-automated, closed-loop molecular discovery cycle, exemplified by the identification of novel JAK inhibitors. With minimal human intervention, we combined AI-driven molecular design and retrosynthesis with IBM’s synthesis automation system RoboRXN…
Henry Childs, Allen C. McBride, Bruce R. Donald
The computational design of L-peptides and their mirror-image counterparts, D-peptides, is an active area in drug design. Peptide therapeutics offer exceptional structural diversity and high binding specificity, while D-peptides additionally confer critical advantages such as proteolytic resistance. Progress in de novo…
Matthew Aquilina, Florian Katzmeier, Minke A.D. Nijenhuis, Siyuan Stella Wang + 8 more
Crisscross polymerization enables the assembly of hundreds of unique DNA origami ‘slats’ into micron-sized structures with nanoscale precision. To design these megastructures, thousands of handle sequences from a fixed library must be assigned to individual slats to encode the desired binding architecture. This…
Weize Xu, Erwin Poussi, Quan Zhong, Zehua Zeng + 29 more
The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses.…
Kexin Hao, Jianguang Liu, Hui Tang, Yan Zhang + 6 more
Natural enzymes often fail to meet industrial demands for catalytic efficiency, stability, and substrate specificity, creating a critical bottleneck in biomanufacturing. This review examines how artificial intelligence (AI) and automation are reshaping enzyme engineering from empirical trial-and-error toward…
Anh Phong Tran, Dhruv D. Jatkar, M. Ali Al-Radhawi, Elizabeth A. Ernst + 1 more
Minimal synthesis of Boolean functions is an NP-hard problem, and heuristic approaches typically give suboptimal circuits. However, in the emergent field of synthetic biology, genetic logic designs that use even a single additional Boolean gate can render a circuit unimplementable in a cell. This has led to a renewed…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Benjamin M. David, Paul A. Jensen
Coordinating multiple liquid handling robots is a complex logistical task when designing biological experiments. Protocol designers must consider the capabilities and constraints of each robot to distribute work optimally across multiple instruments. We developed an optimization framework that finds optimal liquid…
Sumesh Kumar, Joseph Zambreno, Ashfaq Khokhar, Shoaib Akram + 1 more
Improving the speed and efficiency of database search algorithms that deduce peptides from mass spectrometry (MS) data has been an active area of research for more than three decades. The significance of the need for faster database search methods has rapidly increased due to the growing interest in studying non-model…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Gina El Nesr, Simon L. Dürr, Irimpan I. Mathews, Qi Wen + 5 more
The de novo design of enzymes remains a central challenge, requiring consideration of catalytic mechanism and optimization across biochemical and biophysical criteria. Here we present dEVA (design by EVolutionary Algorithm), a multi-objective protein design framework built on principles drawn from evolutionary biology.…
Authors not listed
Generating novel, drug-like molecules with realistic synthetic pathways is an essential goal in computer-aided drug discovery, yet generative models often lack synthesis awareness, resulting in compounds that are difficult or impossible to produce. To overcome this limitation, models must optimize not only molecular…
Authors not listed
Molecular generation for pharmaceutical and agrochemical development remains computationally expensive and statistically under-validated. We conducted a comprehensive benchmark of five molecular generation methods—Genetic Algorithm, Proximal Policy Optimization (PPO), REINFORCE, Transformer-based generation, and random…
Wei Yang, Jinyan Liang, Xiaoyu Zhang, Xiting Peng
In the context of smart manufacturing, improving the quality and efficiency of process planning, especially in the processing of complex parts, has become a key factor influencing the level of intelligence in manufacturing systems. However, most current process planning methods still heavily rely on manual expertise…