20 papers · ranked by Valyu relevance
Mrinal Kanti Rajak, Rajen Pudur
This paper presents a novel Mitochondrial Energy Production Optimization (MEPO) algorithm for enhancing grid-connected inverter control under weak grid conditions. The proposed bio-inspired approach addresses critical challenges in maintaining power quality and system stability in low Short Circuit Ratio (SCR)…
Qi Fei, Guisheng Yin, Zhian Sun, Xiangjie Kong
Software defect detection is a critical research topic in the field of software engineering, aiming to identify potential defects during the development process to improve software quality and reduce maintenance costs. This study proposes a novel feature selection and defect prediction classification algorithm based on…
Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li + 4 more
'Chua Jia Jim Deryl' 'Mak Lee Onn' 'Gee Wah Ng' 'Kezhi Mao'] Prompt optimization (PO) provides a practical way to improve response quality when users lack the time or expertise to manually craft effective prompts. Existing methods typically rely on advanced, large-scale LLMs like GPT-4 to generate optimized prompts.…
Remi Raman, Shahin John J S, R. Subashini, Subhasree Methirumangalath
'Subhasree Methirumangalath'] We investigate the parameterized complexity of Maximum Exposure Problem (MEP). Given a range space (𝑅, 𝑃) where 𝑅 is the set of ranges containing a set 𝑃 of points, and an integer 𝑘, MEP asks for 𝑘 ranges which on removal results in the maximum number of exposed points. A point 𝑝 is…
Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos + 5 more
A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still…
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…
Yaochang Xu, Ping Guo
The critical node detection problem (CNDP) refers to the identification of one or more nodes that have a significant impact on the entire complex network according to the importance of each node in a complex network. Most methods consider the CNDP as a single-objective optimization problem, which requires more prior…
Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha
The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These…
Hongwei Ding, Yuting Liu, Zongshan Wang, Gushen Jin + 5 more
'Gaurav Dhiman' 'Gang Hu' 'Weiguo Zhao' 'Zhenxing Zhang'] The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local…
Kanak Kalita, Janjhyam Venkata Naga Ramesh, Lenka Cepova, Sundaram B. Pandya + 2 more
'Sundaram B. Pandya' 'Pradeep Jangir' 'Laith Abualigah'] The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its…
Chen Zhang, Ziyun Song, Yufei Yang, Changsheng Zhang + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] The flying foxes optimization (FFO) algorithm stimulated by the strategy used by flying foxes for subsistence in heat wave environments has shown good performance in the single-objective domain. Aiming to explore the effectiveness and benefits of the subsistence strategy…
Jeff Guo, Philippe Schwaller
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased…
Jeff Guo, Philippe Schwaller
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…
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…
Haodong Liu, Pinglu Zhang, Yanming Wei, Qinzhong Tian + 3 more
Partial order alignment (POA) has emerged as a fundamental component in long-read error correction, assembly and pangenomics. However, conventional POA algorithms are limited by high time and memory requirements, making them inefficient for large-scale datasets. Here, we present minipoa, a fast and memory-efficient POA…
Minindu Weerakoon, Christopher T Saunders, Haynes Heaton
Most multiple sequence alignment and string-graph alignment algorithms focus on global alignment, but many applications exist for semi-global and local string-graph alignment. Long reads require enormous amounts of memory and runtime to fill out large dynamic programming tables. Effective algorithms for finding the…
Lucas R. van Dijk, Abigail L. Manson, Ashlee M. Earl, Kiran V Garimella + 1 more
Partial order alignment is a widely used method for computing multiple sequence alignments, with applications in genome assembly and pangenomics, among many others. Current algorithms to compute the optimal, gap-affine partial order alignment do not scale well to larger graphs and sequences. While heuristic approaches…
Amaras Nazarians, Sachin Kumar
—Hyperparameter tuning is a critical yet computationally expensive step in training neural networks, particularly when the search space is high dimensional and nonconvex. Metaheuristic optimization algorithms are often used for this purpose due to their derivative free nature and robustness against local optima. In…
Albert Jiménez-Blanco, Lorién López-Villellas, Juan Carlos Moure, Miquel Moreto + 1 more
Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long…