Showing the Quantum Mechanics-based Density Functional Theory optimisation of 3-1-BCMIYPPA where different energy levels are shown in different colours. by Mohammed Ali Alshehri (11707406)

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 algor... by 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. The bottom row shows the same simulations as the middle row, but presents successes and failures when the targets that generated the embedded cohort are applied instead of the balanced targets that are part of the baseline’s objective function. …”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Table_2_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Table_1_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Algorithm. by Daniel Sanchez-Taltavull (11654630)

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

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

Investigate the variation of the cost function in situations where many obstacles and other robots are detected. by Quoc Bao Diep (18452207)

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

Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

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

The benchmark functions. by Shuhui Yu (1535500)

Indel markers generated by WheresWalker for each mutant. by McKenna Feltes (11242085)

Candidate lists generated by WheresWalker for each mutant. by McKenna Feltes (11242085)

Background heterogeneity and sequencing depth improve WheresWalker SNPindex. by McKenna Feltes (11242085)

WheresWalker outperforms homozygosity density mapping for Maize mutant. by McKenna Feltes (11242085)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <pre><code># the main function</code></pre> <pre><code># 1- Instrument the input source, testfunc.…”

Comparison of compression rates of DDQR and Geco3 using simulated data around the region where the performance of DDQR exceeds that of GeCo3. by Yujun Wang (1947196)