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The performance of modern reinforcement learning algorithms critically relies on tuning ever-increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lead to drastic changes in performance, and different…

Machine Learning · Computer Science 2025-02-05 Jacob Adkins , Michael Bowling , Adam White

This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO,…

Neural and Evolutionary Computing · Computer Science 2024-05-22 Indu Bala , Dikshit Chauhan , Lewis Mitchell

The problem of near-optimal distributed path planning to locally sensed targets is investigated in the context of large swarms. The proposed algorithm uses only information that can be locally queried, and rigorous theoretical results on…

Robotics · Computer Science 2015-03-19 Ishanu Chattopadhyay

We study the percolation properties of graph partitioning on random regular graphs with N vertices of degree $k$. Optimal graph partitioning is directly related to optimal attack and immunization of complex networks. We find that for any…

Statistical Mechanics · Physics 2007-10-07 Gerald Paul , Reuven Cohen , Sameet Sreenivasan , Shlomo Havlin , H. Eugene Stanley

Every realistic instance of a percolation problem is faced with some degree of polydispersity, e.g., the pore-size distribution of an inhomogeneous medium, the size distribution of filler particles in composite materials, or the vertex…

Statistical Mechanics · Physics 2025-06-16 Fabian Coupette , Tanja Schilling

In this paper we contribute a novel algorithm family, which generalizes many unsupervised techniques including unnormalized and energy models, and allows us to infer different statistical modalities (e.g. data likelihood and ratio between…

Machine Learning · Computer Science 2021-01-14 Dmitry Kopitkov , Vadim Indelman

Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for…

Machine Learning · Computer Science 2014-01-10 Yukun Bao , Zhongyi Hu , Tao Xiong

While classical percolation is well understood, percolation effects in randomly packed or jammed structures are much less explored. Here we investigate both experimentally and theoretically the electrical percolation in a binary composite…

Materials Science · Physics 2021-04-20 Shiva Pokhrel , Brendon Waters , Solveig Felton , Zhi-Feng Huang , Boris Nadgorny

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. Existing methods to predict scour depth are mainly based on regression models or black box models in which the first one lacks enough accuracy…

Neural and Evolutionary Computing · Computer Science 2019-06-24 Shahaboddin Shamshirband , Amir Mosavi , Timon Rabczuk

In the presented article, statistical properties regarding the topology and standard percolation on relative neighborhood graphs (RNGs) for planar sets of points, considering the Euclidean metric, are put under scrutiny. RNGs belong to the…

Statistical Mechanics · Physics 2013-04-17 O. Melchert

Bootstrap percolation has been used effectively to model phenomena as diverse as emergence of magnetism in materials, spread of infection, diffusion of software viruses in computer networks, adoption of new technologies, and emergence of…

Probability · Mathematics 2012-06-18 Milan Bradonjić , Iraj Saniee

Using a recently introduced algorithm for simulating percolation in microcanonical (fixed-occupancy) samples, we study the convergence with increasing system size of a number of estimates for the percolation threshold for an open system…

Statistical Mechanics · Physics 2009-11-07 R. M. Ziff , M. E. J. Newman

Choosing a committee with independent members in social networks can be named as a problem in group selection and independence in the committee is considered as the main criterion of this selection. Independence is calculated based on the…

Social and Information Networks · Computer Science 2019-10-07 Parham Hadikhani , Pooria Hadikhani

The satisfiability and optimization of finite-dimensional Boolean formulas are studied using percolation theory, rare region arguments, and boundary effects. In contrast with mean-field results, there is no satisfiability transition, though…

Condensed Matter · Physics 2007-05-23 J. M. Schwarz , A. Alan Middleton

The question of how clustering (non-zero density of triangles) in networks affects their bond percolation threshold has important applications in a variety of disciplines. Recent advances in modelling highly-clustered networks are employed…

Statistical Mechanics · Physics 2013-06-06 James P. Gleeson , Sergey Melnik , Adam Hackett

High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model. The performance of an LFA model heavily rely on its optimization…

Machine Learning · Computer Science 2023-02-24 Jia Chen , Yixian Chun , Yuanyi Liu , Renyu Zhang , Yang Hu

Porous media are often modelled as systems of overlapping obstacles, which leads to the problem of two percolation thresholds in such systems, one for the porous matrix and the other one for the void space. Here we investigate these…

Statistical Mechanics · Physics 2016-06-28 Zbigniew Koza , Grzegorz Kondrat , Karol Suszczyński

Aims. We develop an extended percolation method to allow the comparison of geometrical properties of the real cosmic web with the simulated dark matter web for an ensemble of over- and under-density systems. Methods. We scan density fields…

Cosmology and Nongalactic Astrophysics · Physics 2019-01-30 J. Einasto , I. Suhhonenko , L. J. Liivamägi , M. Einasto

The phenomenon of percolation is one of the core topics in statistical mechanics. It allows one to study the phase transition known in real physical systems only in a purely geometrical way. In this paper, we determine thresholds $p_c$ for…

Statistical Mechanics · Physics 2024-03-08 Krzysztof Malarz

One of the most common methods to train machine learning algorithms today is the stochastic gradient descent (SGD). In a distributed setting, SGD-based algorithms have been shown to converge theoretically under specific circumstances. A…

Machine Learning · Computer Science 2025-08-22 Soumya Sarkar , Shweta Jain