Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

“…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. …”

Experimental Investigation and Comparative Evaluation of Standard Level Shifted Multi-Carrier Modulation Schemes With a Constraint GA Based SHE Techniques for a Seven-Level PUC Inv... by Atif Iqbal (5504636)

“…Different standard multicarrier sinusoidal pulse-width modulation techniques (SPWMs) are adapted for the generation of switching gate signals for the PUC power switches, and these SPWMs are compared with novel optimization-based selective harmonic elimination (SHE) that employs genetic algorithm (GA) for solving nonlinear SHE equation with a constraint that eliminated all third-order harmonics efficiently. …”

A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities by Mahdi Mokhtarzadeh (11593310)

“…To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. …”

A Literature Review on System Dynamics Modeling for Sustainable Management of Water Supply and Demand by Khawar Naeem (17984062)

“…The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). …”

Software defect prediction. (c2019) by Moussa, Rebecca

“…One that focuses on predicting defect in software modules using a hybrid heuristic - a combination of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). We compare our approach to 9 well known machine learning techniques and results show the advantages of our model over the other techniques. …”
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GENETIC SCHEDULING OF TASK GRAPHS by Benten, M. S.

“…The problem of assigning tasks to processing elements as a combinatorital optimization is formulated, and a heuristic based on a genetic algorithm is presented. …”
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Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

“…Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. …”
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Timing driven genetic placement by Sait, Sadiq M.

“…IN this paper we present a timing driven placer for standard cell IC design. The placement algorithm follows the genetic paradigm, with the objective of minimizing both area and path slacks. …”
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A Biologicaly Inspired Decision Model for Multivariable Genetic- Fuzzy-AHP System by Kouatli, Issam

“…This paper describes a hybridized intelligent algorithm as a tuning mechanism for one type of Genetic Fuzzy system termed the Genetic Fuzzimetric Technique (GFT). …”
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Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs by Kouatli, Issam

“…An irregular shape may be required by some systems. Hence, a genetic algorithm was proposed as a methodology to optimize the performance of fuzzy systems by mutating different regular shapes. …”
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Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

“…Looking at the historical data of students, fuzzy logic can be used to develop rules based on these data. Genetic Algorithm would be used to optimize the performance of the system.…”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti (21593819)

“…This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. …”

Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management by Islam, Eljilany

“…As per 10.3% prevalence of genetic variants, 82% bridging, and a calculated 20% optimization in the preparative period of warfarin management, the benefit to cost ratio was 4.0 in favor genotype-guided approach. …”
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Implementation of a Multivariable Modular Structure for Fuzzy Taxi Scheduling System (FTSS) by Kouatli, Issam

“…Fuzzy logic can be utilized to deal with such uncertainty in the information. Genetic algorithm can be utilized for optimization of solution. …”
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Digital circuit design through simulated evolution (SimE) by Sait, Sadiq M.

“…Area, power and delay are considered in the optimization of circuits. Results obtained by SimE algorithm are compared to those obtained by genetic algorithm (GA).…”
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Performance driven standard-cell placement using the geneticalgorithm by Youssef, H.

“…In this paper we present a timing-driven placer for standard-cell IC design. The placement algorithm follows the genetic paradigm. Besides optimizing for area and wire length, the placer minimizes the propagation delays on a predicted set of critical paths. …”
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Simulated evolution for timing and low power VLSI standard cell placement by Sait, Sadiq M.

“…For this hard multiobjective combinatorial optimization problem, no known exact and efficient algorithms exist that guarantee finding a solution of specific or desirable quality. …”
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Data Generation for Path Testing by Mansour, Nashat

“…These algorithms are based on an optimization formulation of the path testing problem which include both integer- and real-value test cases. …”
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Power system output feedback stabilizer design via geneticalgorithms by Abdel-Magid, Y.L.

“…In the second method, the problem of selecting the output feedback gains is converted to a simple optimization problem with an eigenvalue based objective function, which is solved by a genetic algorithm. …”
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Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids by Shaaban, Mostafa

“…The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: (a) deployment and operation costs and (b) associated green house gas emissions, while satisfying the microgrid technical constraints. …”
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