A novel network-based SIS framework for improved GA performance by Tohme, Rawane

“…Genetic algorithms have long been used to solve complex optimization problems by mimicking natural selection processes. …”
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Practical Multiple Node Failure Recovery in Distributed Storage Systems by Itani, M.

“…We allocate newcomers to nodes with minimal computations and without changing the original optimized plan. The problem is solved using genetic algorithms that search within the feasible solution space. …”
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Final exams scheduling for univeristies. (c2001) by Timany, Mazen Nuhad

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Simulated evolution for timing and low power VLSI standard cell placement by Sait, Sadiq M.

“…Comparison with genetic algorithm is discussed. r 2003 Elsevier Ltd. …”
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Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

“…Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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Impacts of On-Grid Solar PV on Distribution Networks and Potential Solutions: A Case Study in the Region of Zahle by Korkmaz, Jessica

“…The siting and sizing methodology is conducted by considering five different optimization algorithms, namely the single-objective genetic algorithm (SOGA), the combined SOGA and loss sensitivity factor algorithm (SOGA-LSF), the multi-objective genetic algorithm (MOGA), the combined MOGA-LSF and the CAPADD algorithm of OpenDSS. …”
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Data Generation for Path Testing by Mansour, Nashat

“…The two algorithms are: a simulated annealing algorithm (SA), and a genetic algorithm (GA). …”
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Iterative heuristics for multiobjective VLSI standard cellplacement by Sait, Sadiq M.

“…We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. …”
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EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING by Sait, Sadiq M.

“…These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) [sadiq et al., 1999] respectively. …”
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Parallel processing by Mansour, Nashat

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Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids by Shaaban, Mostafa

“…An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting Genetic algorithm (NSGA-II). Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. …”
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Graph Contraction for Mapping Data on Parallel Computers by Mansour, N.

“…We then present experimental results on using contracted graphs as inputs to two physical optimization methods; namely, genetic algorithm and simulated annealing. …”
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Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves by S. Andhale, Yogesh

“…The genetic optimization (GO) method is used to further refine the location of damage within the enclosed area obtained using astroid algorithm. …”
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Scheduling and allocation in high-level synthesis using stochastic techniques by Sait, Sadiq M.

“…Scheduling and allocation can be formulated as an optimization problem. In this work, a unique approach to scheduling and allocation problem using the genetic algorithm (GA) is described. …”
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Nested ensemble selection: An effective hybrid feature selection method by Kamalov, Firuz

“…The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. …”
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Finite state machine state assignment for area and power minimization by El-Maleh, A.

“…A fuzzy-based aggregation function is employed to combine the two objectives. The work employs genetic algorithm for search space exploration. Experimental results demonstrate the effectiveness of the proposed measures.…”
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Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives by Nasir Uddin, M.

“…At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. …”
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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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Enhancement of Frequency Control for Stand-Alone Multi-Microgrids by Kavita Singh (182141)

“…For getting superior outcomes and enhanced steadiness of the microgrid, the controller gains are streamlined utilizing an imperialist competitive algorithm (ICA). To demonstrate the efficiency of ICA, The obtained results are compared with the genetic algorithm and particle swarm optimization algorithm. …”

Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management by Islam Eljilany (14151588)

“…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. …”