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…
Authors not listed
The recent global health crisis also known as the COVID-19 or coronavirus pandemic has attracted the researchers’ attentions to a treatment approach called immune plasma or convalescent plasma once more again. The main idea lying behind the immune plasma treatment is transferring the antibody rich part of the blood…
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…
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…
Tarsicio Lucas, Teresa B. Ludermir, Ricardo B. C. Prudêncio, Carlos Soares
'Carlos Soares'] Abstract. The optimization of Artificial Neural Networks (ANNs) is an important task to the success of using these models in real-world applications. The solutions adopted to this task are expensive in general, involving trial-and-error procedures or expert knowledge which are not always available. In…
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…
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…
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…
Hua-Dong Xiong, Li Ji-An, Robert C. Wilson, Marcelo G. Mattar
A hallmark of intelligence is the ability to adapt behavior to changing environments, which requires adapting one’s own learning strategies. This phenomenon is known as learning to learn or meta-learning. Although well established in humans and animals, a computational framework that characterizes how biological agents…
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…
Mohammed Baragilly, Brian H Willis
Meta-analysis may be used to summarise a test’s accuracy. Often the sensitivity and specificity are the measures of interest and as these are correlated a bivariate random effects model is commonly used to fit the data. This model has five parameters and it may be optimised using a Newton-Raphson based algorithm…
Maria Harris Rasmussen, Mads Madsen, Jan H. Jensen
We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem.…
Maria Harris Rasmussen, Mads Madsen, Jan H. Jensen
We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem.…
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…
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…