Data_Sheet_1_Bi-objective goal programming for balancing costs vs. nutritional adequacy.pdf by Melissa F. Koenen (14262764)

“…This method is a bi-objective algorithm based on the NonInferior Set Estimation (NISE) method that finds all efficient trade-offs between two linear objectives.…”

Flowchart of MobiLLe. by Nika Abdollahi (4966120)

S1 Dataset - by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of ACC on the random network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Parameters in the experiment. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of APL on the random network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of ACC on the regular network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of APL on the regular network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

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

“…Structural analyses are performed by using the MASTAN2 software, taking into account geometric nonlinearities and a simplified physical nonlinearity method. The objective function considers the cost of concrete, reinforcement and formwork, and the optimization problems are solved by genetic algorithms. …”

Iteration diagram of genetic algorithm. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Genetic algorithm flow chart. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Results of genetic algorithm tuning parameters. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Wrapper-based feature selection methods. by Xiaohua Zeng (793632)

Decoding results. by Sam Hall-McMaster (10343795)

Block diagram of 2-DOF PIDA controller. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 7. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 10. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

GA-XGBoost optimization process diagram. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Parameter space exploration in comparison to exploitation for the selection of samples during Bayesian Optimization. by John E. Fleming (8533956)

Comparison of methods on the USPS dataset. by Mohammed Shalaby (18297650)