Prediction performance of different optimization algorithms. by 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). …”

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

“…Output is given by the vascular response, measured as the change in speed of red blood cells flowing inside a capillary proximal to the recorded neuronal activation (in yellow, right panel). 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). …”

The benchmark functions. by Shuhui Yu (1535500)

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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Algorithm process of BPNN. by Xiaoxiang Cao (12644851)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)

“…<p>Average function evaluation times of the three optimization algorithms.…”

Algorithm. by Daniel Sanchez-Taltavull (11654630)

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]. by Daniela Huppenkothen (9174507)

“…<p>We explore the behaviour of the baseline algorithm both in terms of whether it can recover maximally diverse cohorts (upper four panels), and whether it can recover imbalanced cohorts. …”

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

Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)

The image of the Aekley function. by Zhengyu Xu (8550660)

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

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The IALO algorithm solution flowchart. by Wenjing Wang (181404)

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Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

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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. …”

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Standard functions to test. by Kemal Gökhan Nalbant (18858009)