Each DDQN is able to fit a function on the training data. حسب Andrew Brim (12107047)

Consistency of model-derived predictive patterns as a function of sample diversity. حسب Oualid Benkarim (9707986)

Effects of processing rate and algorithms performance on estimates of biodiversity. حسب Valentine Fleuré (12822137)

Algorithm of the PbGA search for the optimal PbF. حسب Larasmoyo Nugroho (18078260)

"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"

Data_Sheet_1_A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials.PDF حسب Thierry Nieus (165355)

Envelope of the colored noise signal <i>ϵ</i>_(<i>k</i>) from recording <i>2–81731-A-10.wav</i> as a function of sample number <i>k</i>. حسب Martin Dodek (20623832)

An example of the transitions in search for enzymatic reaction networks. حسب Takashi Naka (272391)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9. حسب Yi-Wen Hsiao (13767916)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7. حسب Yi-Wen Hsiao (13767916)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4. حسب Yi-Wen Hsiao (13767916)

Relationship between bistability and resettability. حسب Takashi Naka (272391)

Comparison of the set found when solving IM for <i>k</i> = 19 by the greedy algorithm (left) and the one found by (1 + 1)-WEA for a network with <i>n</i> = 50 vertices. حسب Alexander Andreev (16822333)

Illustration of how (1 + 1)-WEA_v3 improves the target set when solving TSS. حسب Alexander Andreev (16822333)

CEC2018 DF1,3-8 PSAMOO حسب Anonymized (8222226)

Mean crossing time as a function of <i>α</i>. حسب Pratik Mullick (12851011)

Optimization Algorithm Applied to Environmental Control in Broiler Houses حسب MAC Benvenga (10447461)

"…Results indicated that there was a large dispersion of the data collected from the environmental variables. The process suggested that the optimized functions lead to absolute values obtained by the algorithm for each of the environmental factors of the controlled environmental system, representing the optimal condition of the environment found for each broiler age, considering the interactions of the variables. …"

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. حسب Hiroyuki Ichijo (13891153)

Variations of loss functions versus epoch number. حسب Xueliang Guo (4797057)

Swarm intelligence algorithms for width and length on influence games حسب Francisco Muñoz (9455441)

"…<br><br><div>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--excludenodes EXCLUDENODES]<br><br>Calculates the best Influence Spread set on a Weighted Symmetric Graph using PSO<br></div><div><br></div><div><div>positional arguments:</div><div> file</div><div><br></div><div>optional arguments:</div><div> -h, --help: show this help message and exit</div><div> -a: threat file input contents as an Adjacency Matrix</div><div> -r: reverse order of nodes, from (a,b,w) a -> b will be b -> a</div><div> -d D: line separator to use while parsing</div><div> -t T: number of times to execute</div><div> -i I: number of metaheuristic iterations per execution</div><div> -q Q: fixed quota, use 0 = floor(n/2)+1</div><div> --shape SHAPE: shape functions for binarization - list of implemented shape functions: s2,s2_neg,v2,v4</div><div> --sym: consider graph as symmetric instead of directed</div><div> --folder FOLDER: output folder</div><div> --seed SEED: use custom seed for metaheuristic calcs</div><div> --sum SUM: adds a value to all node labels</div><div> --mh MH: metaheuristic to use - list of implemented metaheuristics: {1: 'Swarm', 2: 'Swarm2', 3: 'Swarm_W', 4:</div><div> 'Swarm_L'}</div><div> --tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</div><div> --notags: do not use first row as node tags - tags will be calculated</div><div> --prune: nodes with outdegree = 0 and indegree > 0, and with outdegree = 1 and neighbor's outdegree > 0 will be excluded</div><div> --excludenodes EXCLUDENODES: nodes to skip, comma separated</div></div>…"

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. حسب Laurentius O. Osapoetra (9931759)