Scheduling IoT Requests to Minimize Latency in Fog Computing by Alikarar, Mazin Abdelbadea Nasralla

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Exam scheduling algorithms. (c1999) by Mikati, Ziad

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A Parallel Tabu Search Algorithm for Optimizing Multiobjective VLSI Placement by Minhas, Mahmood R.

“…In this paper, we present a parallel tabu search (TS) algorithm for efficient optimization of a constrained multiobjective VLSI standard cell placement problem. …”
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Incremental & classical genetic algorithm. (c2001) by Awad, Mohamad M.

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Genetic algorithms applications in load frequency control by Abdel-Magid, Y.L.

“…This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. …”
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A New Parallel Genetic Algorithm Model by Al-Somani, Turki F.

“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling by ALKETBI, SAIF

“…The results showed the proposed methodology outperformed existing algorithms in optimizing project durations and resources allocation. …”
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Development of an Optimization Scheme for A Fixed-Wing UAV Long Endurance with PEMFC and Battery by Al Rayes, Zaid

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Tensile Test Optimization Using the Design of Experiment and Soft Computing by Mehdi Moayyedian (14880358)

“…This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). …”

Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations by Mansour, Nashat

“…These are simulated annealing, genetic algorithms and neural networks. A number of design choices and the addition of preprocessing and postprocessing steps lead to versions of the algorithms which differ in solution qualities and execution times. …”
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Control of nonlinear dynamical systems using genetic algorithms by Al-Duwaish, H.N.

“…This paper introduces a new method for the control of nonlinear systems using genetic algorithms. The proposed method formulates the nonlinear controller design as an optimization problem and genetic algorithms are used in the optimization process. …”
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Capturing outline of fonts using genetic algorithm and splines by Sarfraz, M.

“…We present a method to convert the original problem into a discrete combinatorial optimization problem and solve it by a genetic algorithm. …”
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Timing driven genetic algorithm for standard-cell placement by Sait, Sadiq M.

“…As the algorithm starts converging toward generations with acceptable delay properties, the objective is dynamically adjusted toward optimizing area and routability. …”
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A new genetic algorithm approach for unit commitment by Mantawy, A.H.

“…This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. …”
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Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models by Asan G. A. Muthalif (16888818)

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A method for test scheduling using an evolutionary approach. (c2000) by Noujaim, Jad

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A Scatter search algorithm for exam scheduling. (c2006) by Isahakian, Vatche Raffi

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Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms by Harmanani, H. M.

“…The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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Genetic Algorithm Based Simultaneous Eigenvalue Placement Of Power Systems by Abdel Magid, Y.L.

“…The task of selecting the output feedback gains is converted to a simple optimization problem with an eigenvaluebased objective function, which is solved by a genetic algorithm. …”
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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

“…Five nature-inspired optimization algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Particle Swarm Optimization (PSO) are compared. …”