Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Convergence curve of unimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

Convergence curve of unimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Hyperparameter settings of the algorithm 1. by Jin Xu (31283)

“…Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). …”

Hypothetical example illustrating how the <i>search</i>.<i>conv</i> function algorithm works. by Silvia Castiglione (5010581)

Phosphorylation-specific motifs identified by the Motif-X algorithm. by Xiaoxiao Qin (9247765)

Represention of the anneal schedules used as a function of the dimensionless anneal parameter <i>s</i>. by John Golden (9946327)

Solid lines present trend curves for normalized rolling algorithmic complexity and dashed lines depict the average rolling algorithmic complexity as a function of experimental conditions. by Mikołaj Biesaga (17816940)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

“…In this work, we introduce a genetic algorithm to identify optimal orbital/site orderings that enhance wave function compactness, thereby enabling the study of larger systems than previously possible. …”

Flowchart of one iteration of the CE-Symm algorithm. by Spencer E. Bliven (6672755)

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”

Parameters of trained algorithms. by Swathi Anil (17382903)

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …”

23 benchmark functions. by Yu Liu (6938)

“…To enhance the algorithm’s global optimization capabilities and stability, an enhanced CTCM (CTCMKT) is proposed, which integrates a joint strategy of Kent chaotic mapping and <i>t</i>- distribution mutation. …”

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

Subjects:

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

Subjects:

Verification of the in-line PS function. by Raul Castañeda (13932971)

Algorithm testing. by Ziad M. Ali (12516532)

“…While the single-diode model (PV_SDM) is commonly used, the double-diode model (PV_DDM) offers improved accuracy at a reasonable level of complexity. However, finding analytical closed-form solutions for the current-voltage (<i>I</i>-<i>U</i>) dependency in PV_DDM circuits has remained a challenge. …”