相关论文: A Genetic Algorithm Analysis of N* Resonances in $…

The photoproduction of $\gamma p \to K^+ \Sigma^0(1385)$ is investigated based on an effective Lagrangian approach using the tree-level Born approximation, with the purpose of understanding the reaction mechanisms and resonance contents and…

We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by GA. This approach has two improvements compared to KNN: one…

Several computer vision and artificial intelligence projects are nowadays exploiting the manifold data distribution using, e.g., the diffusion process. This approach has produced dramatic improvements on the final performance thanks to the…

We study the reaction mechanism of K*Lambda(1116) photoproduction off the proton target near threshold considering the contributions from nucleon-resonances. Employing the effective Lagrangian method at the tree-level Born approximation, we…

This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The…

Evolutionary algorithms are a type of artificial intelligence that utilize principles of evolution to efficiently determine solutions to defined problems. These algorithms are particularly powerful at finding solutions that are too complex…

Strange multi-baryonic clusters are an exiting possibility to explore the properties of cold dense baryonic matter. Recent results on searches for exotic narrow resonances with $\Lambda$ hyperon subsystems are reviewed. The observation of…

The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct…

We investigate the reaction mechanism of $K^0 \Lambda$ photoproduction off the neutron target, i.e., $\gamma n \to K^0 \Lambda$, in the range of $W\approx 1.6-2.2$ GeV. We employ an effective Lagrangian method at the tree-level Born…

Results of a partial wave analysis of new high-statistics data on $\gamma p\to p\eta$ from MAMI are presented. A fit using known broad resonances and only standard background amplitudes can not describe the relatively narrow peaking…

The two reactions $\gamma p\to K^+\Lambda$ and $\pi^-p\to K^0\Lambda$ are analyzed to determine the leading photoproduction multipoles and the pion-induced partial wave amplitudes in slices of the invariant mass. The multipoles and the…

An innovative strategy for the optimal design of planar frames able to resist to seismic excitations is here proposed. The procedure is based on genetic algorithms (GA) which are performed according to a nested structure suitable to be…

Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient…

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the…

A systematic analysis of the $K^\ast\Sigma$ photoproduction off proton is performed with all the available differential cross section data. We carry out a strategy different from the previous studies of these reactions, where, instead of…

The flawless functioning of a protein is essentially linked to its own three-dimensional structure. Therefore, the prediction of a protein structure from its amino acid sequence is a fundamental problem in many fields that draws researchers…

In this work, we show how a genetic algorithm (GA) can be used to find step-by-step solutions to introductory physics problems. Our perspective is that the underlying task for this is one of finding a sequence of equations that will lead to…

Neural network models of real-world systems, such as industrial processes, made from sensor data must often rely on incomplete data. System states may not all be known, sensor data may be biased or noisy, and it is not often known which…

We propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Precision Reactor Oscillation and Spectrum Experiment…

We give a quasipolynomial time algorithm for the graph matching problem (also known as noisy or robust graph isomorphism) on correlated random graphs. Specifically, for every $\gamma>0$, we give a $n^{O(\log n)}$ time algorithm that given a…