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Journal articles on the topic 'Growing-pruning algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wang, Ruliang, Huanlong Sun, Benbo Zha, and Lei Wang. "Research and Application of Improved AGP Algorithm for Structural Optimization Based on Feedforward Neural Networks." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/481919.

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The adaptive growing and pruning algorithm (AGP) has been improved, and the network pruning is based on the sigmoidal activation value of the node and all the weights of its outgoing connections. The nodes are pruned directly, but those nodes that have internal relation are not removed. The network growing is based on the idea of variance. We directly copy those nodes with high correlation. An improved AGP algorithm (IAGP) is proposed. And it improves the network performance and efficiency. The simulation results show that, compared with the AGP algorithm, the improved method (IAGP) can quickl

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Gelfand, S. B., C. S. Ravishankar, and E. J. Delp. "An iterative growing and pruning algorithm for classification tree design." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 2 (1991): 163–74. http://dx.doi.org/10.1109/34.67645.

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Zhang, Yunong, Ying Wang, Weibing Li, Yao Chou, and Zhijun Zhang. "WASD Algorithm with Pruning-While-Growing and Twice-Pruning Techniques for Multi-Input Euler Polynomial Neural Network." International Journal on Artificial Intelligence Tools 25, no. 02 (2016): 1650007. http://dx.doi.org/10.1142/s021821301650007x.

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Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal con

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Han, Hong-Gui, Shuo Zhang, and Jun-Fei Qiao. "An adaptive growing and pruning algorithm for designing recurrent neural network." Neurocomputing 242 (June 2017): 51–62. http://dx.doi.org/10.1016/j.neucom.2017.02.038.

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Zemouri, Ryad, Nabil Omri, Farhat Fnaiech, Noureddine Zerhouni, and Nader Fnaiech. "A new growing pruning deep learning neural network algorithm (GP-DLNN)." Neural Computing and Applications 32, no. 24 (2019): 18143–59. http://dx.doi.org/10.1007/s00521-019-04196-8.

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Мельниченко, А. В., and К. А. Здор. "INCORPORATING ATTENTION SCORE TO IMPROVE FORESIGHT PRUNING ON TRANSFORMER MODELS." Visnyk of Zaporizhzhya National University Physical and Mathematical Sciences, no. 2 (December 19, 2023): 22–28. http://dx.doi.org/10.26661/2786-6254-2023-2-03.

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With rapid development of technologies and growing number of application of neural networks, the problem of optimization arises. Among other methods to optimize training and inference time, neural network pruning has attracted attention in recent years. The main goal of pruning is to reduce the computational complexity of neural network models while retaining performance metrics on desired level. Among the various approaches to pruning, Single-shot Network Pruning (SNIP) methods was designed as a straightforward and effective approach to optimize number of parameters before training. However,

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Lee, Chien-Cheng, and Cheng-Yuan Shih. "LEARNING PATTERNS OF LIVER MASSES USING IMPROVED RBF NETWORKS." Biomedical Engineering: Applications, Basis and Communications 22, no. 02 (2010): 137–47. http://dx.doi.org/10.4015/s1016237210001852.

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This study proposes a diagnosis system for liver masses based on the improved radial basis function (RBF) neural networks. In this article, RBF networks are improved by sigmoid function and the growing and pruning algorithm. The proposed improved RBF networks adopt the sigmoid function as their kernel due to its increased flexibility over the Gaussian kernel. Furthermore, the growing and pruning algorithm is used to adjust the network size dynamically according to the neuron's significance. This investigation formulates discriminating among cysts, hepatoma, cavernous hemangioma, and normal tis

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Chaurasia, Satvik, and R. Shobana. "An Adaptive Growing Pruning Algorithm to Optimize Dynamic Feed Forward Neural Networks for Nonlinear Dynamic System Identification." International Journal of Microsystems and IoT 3, no. 1 (2025): 1519–25. https://doi.org/10.5281/zenodo.15493712.

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Optimizing the structure is very crucial for effective identification and control of any nonlinear system. Optimization leads to a robust and more generalized structure. In this work, an effective adaptive growing-pruning algorithm scheme is proposed to optimize dynamic feed forward structures. The hidden layer of the static FFNN is made dynamic and the weights of the dynamic FFNN are trained using standard Back propagation algorithm. Firstly, the network is grown only when the MSE is found high and increasing. Likewise, unnecessarily neurons are pruned based on low activation variance. The le

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Liu, Chenyu, Dongliang Zhang, and Wankai Li. "Crown Growth Optimizer: An Efficient Bionic Meta-Heuristic Optimizer and Engineering Applications." Mathematics 12, no. 15 (2024): 2343. http://dx.doi.org/10.3390/math12152343.

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This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig’s law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compa

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Wang, Qitong, Ioana Ileana, and Themis Palpanas. "LeaFi: Data Series Indexes on Steroids with Learned Filters." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3709701.

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The ever-growing collections of data series create a pressing need for efficient similarity search, which serves as the backbone for various analytics pipelines. Recent studies have shown that tree-based series indexes excel in many scenarios. However, we observe a significant waste of effort during search, due to suboptimal pruning. To address this issue, we introduce LeaFi, a novel framework that uses machine learning models to boost pruning effectiveness of tree-based data series indexes. These models act as learned filters, which predict tight node-wise distance lower bounds that are used

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Huang, G. B., P. Saratchandran, and N. Sundararajan. "An Efficient Sequential Learning Algorithm for Growing and Pruning RBF (GAP-RBF) Networks." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (2004): 2284–92. http://dx.doi.org/10.1109/tsmcb.2004.834428.

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Han, Honggui, and Junfei Qiao. "A Self-Organizing Fuzzy Neural Network Based on a Growing-and-Pruning Algorithm." IEEE Transactions on Fuzzy Systems 18, no. 6 (2010): 1129–43. http://dx.doi.org/10.1109/tfuzz.2010.2070841.

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Chai, Zhilei, Wei Song, Qinxin Bao, Feng Ding, and Fei Liu. "Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network." Royal Society Open Science 5, no. 9 (2018): 180529. http://dx.doi.org/10.1098/rsos.180529.

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The growing and pruning radial basis function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bioinspired intelligent algorithm is proposed to optimize GAP-RBF. Specifically, the excellent local convergence of particle swarm optimization (PSO) and the extensive search ability of genetic algorithm (GA) are both considered to optimize the weights and bias term of GAP-RBF. Meanwhile, a competitive mechanism is proposed to m

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Ma, Yuhang, Qingchun Feng, Yuhuan Sun, et al. "Optimized Design of Robotic Arm for Tomato Branch Pruning in Greenhouses." Agriculture 14, no. 3 (2024): 359. http://dx.doi.org/10.3390/agriculture14030359.

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Aiming at the robotic pruning of tomatoes in greenhouses, a new PRRPR configuration robotic arm consisting of two prismatic (P) joints and three revolute (R) joints was designed to locate the end effector to handle randomly growing branches with an appropriate posture. In view of the various spatial posture of the branches, drawing on the skill of manual pruning operation, we propose a description method of the optimal operation posture of the pruning end effector, proposing a method of solving the inverse kinematics of the pruning arm based on the multi-objective optimization algorithm. Accor

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Zhang, Jianwei, Dong Li, Lituan Wang, and Lei Zhang. "One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order." International Journal of Neural Systems 31, no. 07 (2021): 2150029. http://dx.doi.org/10.1142/s0129065721500295.

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Neural Architecture Search (NAS), which aims at automatically designing neural architectures, recently draw a growing research interest. Different from conventional NAS methods, in which a large number of neural architectures need to be trained for evaluation, the one-shot NAS methods only have to train one supernet which synthesizes all the possible candidate architectures. As a result, the search efficiency could be significantly improved by sharing the supernet’s weights during the candidate architectures’ evaluation. This strategy could greatly speed up the search process but suffer a chal

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Fukui, Satoshi, Lihua Wang, and Seiichi Ozawa. "Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning." Applied Sciences 15, no. 10 (2025): 5560. https://doi.org/10.3390/app15105560.

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Cloud computing is widely used by organizations and individuals to outsource computation and data storage. With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. However, operating MLaaS on the cloud raises significant privacy concerns, particularly regarding the leakage of sensitive personal data and proprietary machine learning models. This paper proposes a privacy-preserving decision tree (PPDT) framework that enables secure predictions on sensitive inputs through homomorphic matrix multiplication withi

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Hermawan, Latius, and Maria Bellaniar Ismiati. "Penerapan Augmented reality Berbasis Minimax Algorithm pada Game Papan Cerdas." Jurnal Buana Informatika 13, no. 1 (2022): 21–30. http://dx.doi.org/10.24002/jbi.v13i1.4929.

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Abstract. Application of Augmented reality Based on Minimax-Alpha Beta Pruning Algorithm on Smart Board Games. Augmented reality technology is growing very rapidly making game production more innovative and attractive. The implementation of this technology also has the potential for traditional board games which are starting to be replaced by computer-based digital games. The method used in the digital board is Minimax which is zero-sum based where one point of the opponent's victory will reduce the player's one point. This method underlies the way of thinking to get critical steps in several

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Vuković, Najdan, and Zoran Miljković. "A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation." Neural Networks 46 (October 2013): 210–26. http://dx.doi.org/10.1016/j.neunet.2013.06.004.

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MONGIOVÌ, MISAEL, RAFFAELE DI NATALE, ROSALBA GIUGNO, ALFREDO PULVIRENTI, ALFREDO FERRO, and RODED SHARAN. "SIGMA: A SET-COVER-BASED INEXACT GRAPH MATCHING ALGORITHM." Journal of Bioinformatics and Computational Biology 08, no. 02 (2010): 199–218. http://dx.doi.org/10.1142/s021972001000477x.

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Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching sub-networks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the set-cover problem that

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20

Szabo, Alexandre, and Leandro Nunes de Castro. "A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/459503.

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The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass), is a derivation of a particle swarm clustering algori

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Liang, Yun, Weipeng Jiang, Yunfan Liu, Zihao Wu, and Run Zheng. "Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model." Agriculture 15, no. 3 (2025): 237. https://doi.org/10.3390/agriculture15030237.

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The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus picking-point localization, named as CPPL. The CPPL is achieved based on two stages, namely the detection stage and the segmentation stage. For the detection stage, we define the KD-YOLOP to accurately detect citrus fruits to quickly localize the initial picking region. The KD-YOLOP is define

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WANG, NING, MENG JOO ER, XIAN-YAO MENG, and XIANG LI. "AN ONLINE SELF-ORGANIZING SCHEME FOR PARSIMONIOUS AND ACCURATE FUZZY NEURAL NETWORKS." International Journal of Neural Systems 20, no. 05 (2010): 389–403. http://dx.doi.org/10.1142/s0129065710002486.

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In this paper, an online self-organizing scheme for Parsimonious and Accurate Fuzzy Neural Networks (PAFNN), and a novel structure learning algorithm incorporating a pruning strategy into novel growth criteria are presented. The proposed growing procedure without pruning not only simplifies the online learning process but also facilitates the formation of a more parsimonious fuzzy neural network. By virtue of optimal parameter identification, high performance and accuracy can be obtained. The learning phase of the PAFNN involves two stages, namely structure learning and parameter learning. In

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Sun, Yuhao, Huazhong Zhu, Zhaocheng Liang, Andong Liu, Hongjie Ni, and Ye Wang. "A phase search-enhanced Bi-RRT path planning algorithm for mobile robots." Intelligence & Robotics 5, no. 2 (2025): 404–18. https://doi.org/10.20517/ir.2025.20.

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The proposed improvement to the Rapidly-exploring Random Tree (RRT) path planning algorithm is aimed at addressing the issue of slow convergence speed caused by boundary information in the original algorithm, by introducing a phase search approach. The initial approach involves employing a three-stage search strategy to generate sampling points that are specifically oriented toward real-time sampling failure rate, thereby significantly reducing the number of redundant nodes. Simultaneously, a balanced exploration strategy is introduced, enhancing the algorithmos convergence speed by constructi

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Adak, S., and R. Xu. "Survival Analysis with Time-varying Relative Risks: A Tree-Based Approach." Methods of Information in Medicine 40, no. 02 (2001): 141–47. http://dx.doi.org/10.1055/s-0038-1634477.

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AbstractA tree-based method for estimating time-varying effects of baseline patient characteristics on survival is introduced. A Cox-type model for censored survival data is used in which the time-varying relative risks are modelled as piecewise constants.The tree method consists of three steps: 1. Growing the tree, in which a fast algorithm using maximized score statistics is utilized to determine the optimal change points; 2. A pruning algorithm is applied to obtain more parsimonious models; 3. Selection of a final tree, which may be either via bootstrap resampling or based on a measure of e

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Cribbin, Timothy. "Visualising the Structure of Document Search Results: A Comparison of Graph Theoretic Approaches." Information Visualization 9, no. 2 (2009): 83–97. http://dx.doi.org/10.1057/ivs.2009.3.

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Previous work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally

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Ray, Abhishek, Mario Ventresca, and Karthik Kannan. "A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions." Information Systems Research 32, no. 4 (2021): 1099–114. http://dx.doi.org/10.1287/isre.2021.1031.

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Iterative combinatorial auctions are known to resolve bidder preference elicitation problems. However, winner determination is a known key bottleneck that has prevented widespread adoption of such auctions, and adding a time-bound to winner determination further complicates the mechanism. As a result, heuristic-based methods have enjoyed an increase in applicability. We add to the growing body of work in heuristic-based winner determination by proposing an ant colony metaheuristic–based anytime algorithm that produces optimal or near-optimal winner determination results within specified time.

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Yang, Xi, Tuanjie Gan, Hai Zheng, Quishen Cai, and Yan Chen. "Design of Control System for a new Intelligent Tree-climbing and Pruning Robot." Journal of Physics: Conference Series 2296, no. 1 (2022): 012022. http://dx.doi.org/10.1088/1742-6596/2296/1/012022.

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Abstract This paper designed the control system of a new intelligent tree-climbing and pruning robot which included the control of remote control mobile terminal and intelligent control terminal. Remote control module, power circuit, motor drive, automatic holding detection, height detection, side branch detection and other modules were designed, Then the main program based on STM32 embedded MCU, interrupt program and communication program were designed. The control system could enable the tree climbing robot to climb automatically, detect the side branches in the climbing process, trim the si

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Feng, Jiabo, and Weijun Zhang. "An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots." Applied Sciences 11, no. 22 (2021): 10773. http://dx.doi.org/10.3390/app112210773.

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The application of robots to replace manual work in live-line working scenes can effectively guarantee the safety of personnel. To improve the operation efficiency and reduce the difficulties in operating a live-line working robot, this paper proposes a multi-DOF robot motion planning method based on RRT and extended algorithms. The planning results of traditional RRT and extended algorithms are random, and obtaining sub-optimal results requires a lot of calculations. In this study, a sparse offline tree filling the planning space are generated offline through the growing–withering method. In

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Schroedl, S. "An Improved Search Algorithm for Optimal Multiple-Sequence Alignment." Journal of Artificial Intelligence Research 23 (May 1, 2005): 587–623. http://dx.doi.org/10.1613/jair.1534.

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Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschberg's algorithm, Dynami

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Nassih, Rym, and Abdelaziz Berrado. "A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery." Algorithms 17, no. 12 (2024): 565. https://doi.org/10.3390/a17120565.

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Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the patient rule-induction method (PRIM), a subgroup-discovery algorithm. PRIM finds rules by pe

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Wu, Bin, Wei Zhang, Xiaonan Chi, Di Jiang, Yang Yi, and Yi Lu. "A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold." Sensors 23, no. 17 (2023): 7547. http://dx.doi.org/10.3390/s23177547.

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The efficiency of the rapidly exploring random tree (RRT) falls short when efficiently guiding targets through constricted-passage environments, presenting issues such as sluggish convergence speed and elevated path costs. To overcome these algorithmic limitations, we propose a narrow-channel path-finding algorithm (named NCB-RRT) based on Bi-RRT with the addition of our proposed research failure rate threshold (RFRT) concept. Firstly, a three-stage search strategy is employed to generate sampling points guided by real-time sampling failure rates. By means of the balance strategy, two randomly

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Jin, Liyang, and Qingfeng Wang. "Positioning control of hydraulic cylinder with unknown friction using on/off directional control valve." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 8 (2018): 983–93. http://dx.doi.org/10.1177/0959651818771522.

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In this study, a prediction-based positioning control scheme is proposed for the hydraulic cylinder controlled by a solenoid operated on/off directional control valve. The discrete-valued input, low switching frequency and significant delay of directional control valve make the control problem very complex. Only a discrete-valued control input can be used here; meanwhile, the input has switching frequency constraint and time-delay. Existing methods such as pulse-width modulation control and sliding-mode control are not suitable for this problem, because chattering may arise due to the control

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Shankar, P., and R. K. Yedavalli. "Neural-network-based observer for turbine engine parameter estimation." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223, no. 6 (2009): 821–32. http://dx.doi.org/10.1243/09596518jsce782.

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Accurate estimation of unmeasurable engine parameters such as thrust and turbine inlet temperatures constitutes a significant challenge for the aircraft community. A solution to this problem is to estimate these parameters from the measured outputs using an observer. Currently existing technologies rely on Kalman and extended Kalman filters to achieve this estimation. This paper presents a neural-network-based observer that augments the linear Kalman filter with a neural network to compensate for any non-linearity that is not handled by the linear filter. The implemented neural network is a ra

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Mousavi, Reza, Mahdi Eftekhari, and Mehdi Ghezelbash Haghighi. "A new approach to human microRNA target prediction using ensemble pruning and rotation forest." Journal of Bioinformatics and Computational Biology 13, no. 06 (2015): 1550017. http://dx.doi.org/10.1142/s0219720015500171.

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MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the sel

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Zhu, Jun, Ziwu Pan, Hang Wang, et al. "An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery." Sensors 19, no. 9 (2019): 2087. http://dx.doi.org/10.3390/s19092087.

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As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China’s Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived

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Liu, Jun, Shuang Lai, Ayesha Akram Rai, Abual Hassan, and Ray Tahir Mushtaq. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 3930. http://dx.doi.org/10.3390/ijerph20053930.

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In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 t

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Brafman, R. I. "On Reachability, Relevance, and Resolution in the Planning as Satisfiability Approach." Journal of Artificial Intelligence Research 14 (January 1, 2001): 1–28. http://dx.doi.org/10.1613/jair.737.

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In recent years, there is a growing awareness of the importance of reachability and relevance-based pruning techniques for planning, but little work specifically targets these techniques. In this paper, we compare the ability of two classes of algorithms to propagate and discover reachability and relevance constraints in classical planning problems. The first class of algorithms operates on SAT encoded planning problems obtained using the linear and Graphplan encoding schemes. It applies unit-propagation and more general resolution steps (involving larger clauses) to these plan encodings. The

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Song, Wei, Shiyu Zhang, Zijian Wen, and Junhao Zhou. "A novel adaptive learning deep belief network based on automatic growing and pruning algorithms." Applied Soft Computing 104 (June 2021): 107248. http://dx.doi.org/10.1016/j.asoc.2021.107248.

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Bondarenko, Andrey, Arkady Borisov, and Ludmila Alekseeva. "Neurons vs Weights Pruning in Artificial Neural Networks." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 22. http://dx.doi.org/10.17770/etr2015vol3.166.

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<p class="R-AbstractKeywords">Artificial neural networks (ANN) are well known for their good classification abilities. Recent advances in deep learning imposed second ANN renaissance. But neural networks possesses some problems like choosing hyper parameters such as neuron layers count and sizes which can greatly influence classification rate. Thus pruning techniques were developed that can reduce network sizes, increase its generalization abilities and overcome overfitting. Pruning approaches, in contrast to growing neural networks approach, assume that sufficiently large ANN is already

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Laveglia, Vincenzo, and Edmondo Trentin. "Downward-Growing Neural Networks." Entropy 25, no. 5 (2023): 733. http://dx.doi.org/10.3390/e25050733.

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A major issue in the application of deep learning is the definition of a proper architecture for the learning machine at hand, in such a way that the model is neither excessively large (which results in overfitting the training data) nor too small (which limits the learning and modeling capabilities of the automatic learner). Facing this issue boosted the development of algorithms for automatically growing and pruning the architectures as part of the learning process. The paper introduces a novel approach to growing the architecture of deep neural networks, called downward-growing neural netwo

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Holguin Jimenez, Sofia, Wajdi Trabelsi, and Christophe Sauvey. "Multi-Objective Production Rescheduling: A Systematic Literature Review." Mathematics 12, no. 20 (2024): 3176. http://dx.doi.org/10.3390/math12203176.

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Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRIS

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Trujillano, Javier, Luis Serviá, Mariona Badia, et al. "Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox." Nutrients 17, no. 11 (2025): 1903. https://doi.org/10.3390/nu17111903.

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Background: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to healthcare professionals. This review provides an accessible yet rigorous overview of CT methodology for clinicians, highlighting their utility through a case study addressing the “obesity paradox” in critically ill patients. Methods: We describe key methodological aspects of CTs, including model development, pruning, validation, and classification ty

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Guruswami, Venkatesan, and Chaoping Xing. "Optimal Rate List Decoding over Bounded Alphabets Using Algebraic-geometric Codes." Journal of the ACM 69, no. 2 (2022): 1–48. http://dx.doi.org/10.1145/3506668.

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We give new constructions of two classes of algebraic code families that are efficiently list decodable with small output list size from a fraction 1-R-ε of adversarial errors, where R is the rate of the code, for any desired positive constant ε. The alphabet size depends only ε and is nearly optimal. The first class of codes are obtained by folding algebraic-geometric codes using automorphisms of the underlying function field. The second class of codes are obtained by restricting evaluation points of an algebraic-geometric code to rational points from a subfield . In both cases, we develop a

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Lucchese, Claudio, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, and Salvatore Trani. "X-CLEaVER: Learning Ranking Ensembles by Growing and Pruning Trees." November 15, 2018. https://doi.org/10.5281/zenodo.2668362.

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Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this paper, we propose X-CLEaVER, an iterative meta-algorithm able to build m

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Salahshoor, Karim, and Amin Sabet Kamalabady. "Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach." Chemical Product and Process Modeling 6, no. 1 (2011). http://dx.doi.org/10.2202/1934-2659.1518.

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This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria hav

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Guo, Xin, Wei-Sheng Wang, Jie Zhang, and Li-Shuang Gong. "An Online Growing-and-Pruning Algorithm of a Feedforward Neural Network for Nonlinear Systems Modeling." IEEE Transactions on Automation Science and Engineering, 2024, 1–12. http://dx.doi.org/10.1109/tase.2024.3407518.

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Xiao, Zeqing, and Hui Ou. "Research on Data Generation Based on the Combination of Growing-Pruning Gan and Intelligent Parameter Optimization." International Journal of Cooperative Information Systems, September 29, 2023. http://dx.doi.org/10.1142/s0218843023500235.

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The amount of voltage fault data collection is limited to signal acquisition instruments and simulation software. Generative adversarial networks (GAN) have been successfully applied to the data generation tasks. However, there is no theoretical basis for the selection of the network structure and parameters of generators and discriminators in these GANs. It is difficult to achieve the optimal selection basically by experience or repeated attempts, resulting in high cost and time-consuming deployment of GAN computing in practical applications. The existing methods of neural network optimizatio

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Stender, Mareike, Mattis Hartwig, Tanya Braun, and Ralf Möller. "Increasing State Estimation Accuracy in the Inference Algorithm on a Hybrid Factor Graph Model." International FLAIRS Conference Proceedings 35 (May 4, 2022). http://dx.doi.org/10.32473/flairs.v35i.130682.

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We consider an intelligent agent that receives a continuous input from a signal-generating system and aims for estimating the discrete latent state of that system. The agent includes a switching linear dynamical system modeled as a hybrid factor graph for performing state estimation by determining the inference algorithm. We investigate the agent’s performance in relation to two Gaussian mixture reduction methods restricting Gaussian mixture growing while message passing, namely, the naive pruning implemented in the past and the realization of the Kullback-Leibler (KL) discrimination based app

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Blum, Johannes, Ruoying Li, and Sabine Storandt. "Fission: Practical algorithms for computing minimum balanced node separators." Discrete Mathematics, Algorithms and Applications, January 22, 2022. http://dx.doi.org/10.1142/s1793830922500483.

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Given an undirected graph, a balanced node separator is a set of nodes whose removal splits the graph into connected components of limited size. Balanced node separators are used for graph partitioning, for the construction of graph data structures, and for measuring network reliability. It is NP-hard to decide whether a graph has a balanced node separator of size at most [Formula: see text]. Therefore, practical algorithms typically try to find small separators in a heuristic fashion. In this paper, we present a branching algorithm that for a given value [Formula: see text] either outputs a b

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Kaster, Marvin, Fabian Czappa, Markus Butz-Ostendorf, and Felix Wolf. "Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism." Frontiers in Neuroinformatics 18 (April 19, 2024). http://dx.doi.org/10.3389/fninf.2024.1323203.

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Memory formation is usually associated with Hebbian learning and synaptic plasticity, which changes the synaptic strengths but omits structural changes. A recent study suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this study is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve

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