Algorithm of the PbGA search for the optimal PbF. by 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. …”

Details of 23 basic benchmark functions. by Ying Li (38224)

Measures of predictive accuracy in four machine learning algorithms. by Ryan P. Strum (12411239)

Mean relative importance for the top 10 patient characteristic predictors of hospital admission across all machine learning algorithms. by Ryan P. Strum (12411239)

HIV screening algorithm. by Wei Li A. Koay (21094209)

Genetic algorithm meta-parameters. by 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. …”

The entity relationship of DDPG algorithm. by 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. …”

Cross Validation mechanism for an RL case. by 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. …”

Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …”

Table_2_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …”

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS₂‑Based Microparticles in a Breast Cancer Mode... by Maria Kanelli (5494031)

Optimal configuration of RC frames considering ultimate and serviceability limit state constraints by Marcela Alejandra Juliani (10449275)

“…The objective function considers the cost of concrete, reinforcement and formwork, and the optimization problems are solved by genetic algorithms. …”

Calibration of logistic regression. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of lasso logistic regression. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of random forest. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of gradient boosted trees. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Fig 5 - by 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. …”

Monte carlo test ranking from elitism phase. by 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. …”

Individual #5’s action ratio, position states. by 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. …”

RSF Components of the best five individuals. by 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. …”