As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algorithm from [76]. حسب Daniela Huppenkothen (9174507)

"…We note that the middle row is a special case: here, <i>f</i>_target only corresponds to the proportions of the embedded cohort, while “success” for these two panels is defined as recovering maximally diverse cohorts, as this particular algorithm is designed to do. …"

Convergence curves of different algorithms on the benchmark functions. حسب Aihua Guo (13267712)

Comparison of deconvolution and optimization algorithms on a batch of data. حسب 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). …"

Membership function of output for tank 1. حسب Tamiru Demelash Kassie (20678515)

Membership function of output for tank 2. حسب Tamiru Demelash Kassie (20678515)

Framework of the proposed signal control algorithm. حسب Hyosun Lee (1567246)

Prediction performance of different optimization algorithms. حسب Ali-Kemal Aydin (10968731)

"…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …"

Accuracy as a function of the rate of learning. حسب Arun Kumar M. (13871050)

Membership function of error E1 for tank 1. حسب Tamiru Demelash Kassie (20678515)

Membership function of error E2 for tank 2. حسب Tamiru Demelash Kassie (20678515)

Algorithm used in the model to automatically estimate the parameters of LD pharmacodynamics, via minimization of a cost function. حسب Mauro Ursino (144905)

Supporting data for "Personalized Electromyography Based Control Algorithm Development and Training System for Hand Prosthesis" for HKU Libraries حسب Xiaojun Wang (9067289)

Knowledge graph model rating function. حسب Chunjuan Li (7890182)

DE algorithm flow. حسب Ling Zhao (111365)

"…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …"

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... حسب G. Christopher Marais (21492084)

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. حسب Boluwatife Ikwunne (22238697)

Algorithm Parameter Setting. حسب Tong Zhou (3770)

"…This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. …"

Algorithm operation steps. حسب Junyan Wang (4738518)

"…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …"

The benchmark functions. حسب Shuhui Yu (1535500)

Test results of different algorithms. حسب Ling Zhao (111365)

"…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …"