24 papers · ranked by Valyu relevance
Jarne Mathi Decker
This thesis investigates the impact of overcoming this limitation by engineering a comprehensive feature set to explicitly characterize the algorithms themselves. We combine static code metrics, Abstract Syntax Tree properties, behavioral performance landmarks, and high-level conceptual features. We evaluate two…
Alexander David Goldie, Zilin Wang, Jaron Cohen, Jakob Foerster + 1 more
The process of meta-learning algorithms from data, instead of relying on manual design, is growing in popularity as a paradigm for improving the performance of machine learning systems. Meta-learning shows particular promise for reinforcement learning (RL), where algorithms are often adapted from supervised or…
Moncef Garouani
—Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of…
Zeyuan Ma, Hongshu Guo, Yue-Jiao Gong, Jun Zhang + 1 more
Meta-Black-Box-Optimization Authors: ['Zeyuan Ma' 'Hongshu Guo' 'Yue-Jiao Gong' 'Jun Zhang' 'Kay Chen Tan'] Abstract—In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist…
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…
Zechuan Huang, Zhiguang Cao, Hongshu Guo, Yue-Jiao Gong + 1 more
Meta-Black-Box Optimization (MetaBBO) is an emerging avenue within Optimization community, where algorithm design policy could be meta-learned by reinforcement learning to enhance optimization performance. So far, the reward functions in existing MetaBBO works are designed by human experts, introducing certain design…
Yanjiao Wang, Mengjiao Wei, Bilal Alatas
The termite life cycle optimizer algorithm (TLCO) is a new bionic meta-heuristic algorithm that emulates the natural behavior of termites in their natural habitat. This work presents an improved TLCO (ITLCO) to increase the speed and accuracy of convergence. A novel strategy for worker generation is established to…
Jiaru Yang, Yu Zhang, Ting Jin, Zhenyu Lei + 2 more
Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem.…
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…
Yongjie Zou, Kai Liu, Congcong Zhang, Yu Ling + 7 more
Practical application of brain-computer interfaces (BCIs) requires stable mapping between neuronal activity and behavior through various behavioral contexts and for different individuals. Due to neural activity instability, BCIs require frequent recalibration to maintain robust performance. Early approaches to…
Elvis Han Cui, Zizhao Zhang, Weng Kee Wong
Nature-inspired meta-heuristic algorithms are increasingly used in many disciplines to tackle challenging optimization problems. Our focus is to apply a newly proposed nature-inspired meta-heuristics algorithm called CSO-MA to solve challenging design problems in biosciences and demonstrate its flexibility to find…
S. Gopi, Prabhujit Mohapatra
In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of…
Manh Hung Nguyen, Lisheng Sun-Hosoya, Isabelle Guyon
Selection Authors: ['Manh Hung Nguyen' 'Lisheng Sun-Hosoya' 'Isabelle Guyon'] Training a large set of machine learning algorithms to convergence in order to select the best-performing algorithm for a dataset is computationally wasteful. Moreover, in a budget-limited scenario, it is crucial to carefully select an…
Wentao Wang, Hao Liu, Quanqin He
This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search…
Shuai Hou, Yujiao Li, Meijuan Bai, Mengyue Sun + 5 more
'Chao Wang' 'Halil Tetik' 'Dong Lin' 'Sergey V. Zherebtsov'] The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML…
Jaehoon Shin, Jee Hang Lee, Sang Wan Lee
Environmental conditions affect human reward prediction. Stable environments foster accurate prediction but constrain learning opportunities, whereas uncertain environments diminish predictability. This stability-uncertainty dilemma complicates task design. We conceptualize this challenge as a task learning paradigm…
Ch Anwar ul Hassan, Muhammad Sufyan Khan, Rizwana Irfan, Jawaid Iqbal + 4 more
'Jawaid Iqbal' 'Saddam Hussain' 'Syed Sajid Ullah' 'Roobaea Alroobaea' 'Fazlullah Umar'] Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model…
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…
Alexandra R. van den Berg, Pieter R. Roelfsema, Sander M. Bohte
The acquisition of knowledge does not occur in isolation; rather, learning experiences in the same or similar domains amalgamate. This process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While meta-learning can be implemented in recurrent neural networks, these…
Maria H. Rasmussen, Jan H. Jensen
We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions (SciPost Chem. 2021, 1, 003) on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow et al. (Scientific Data 2020, 7, 137). For unimolecular reactions the meta-MD simulations…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
Authors not listed
Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…