Hybrid Cooperative Co-evolution for the CEC15 Benchmarks by El-Abd, Mohammed

“…In its second stage, the method adopts different algorithms within the cooperative co-evolution (CC) framework to simultaneously optimize the generated groups. …”
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Digital circuit design through simulated evolution (SimE) by Sait, Sadiq M.

“…In this paper, the use of simulated evolution (SimE) algorithm in the design of digital logic circuits is proposed. …”
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Evaluating Parallel Simulated Evolution Strategies for VLSI Cell Placement by Sait, Sadiq M.

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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy by Haitao Xu (435549)

“…Coupling the developed MLPNN and differential evolution optimization algorithm shows that temperature = 263 K and pressure = 6.92 MPa are the optimum condition for minimizing the MeOH loss in the gas hydrate prevention unit. …”

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

“…Abstract This paper presents a Fuzzy Simulated Evolution algorithm for VLSI standard cell placement with the objective of minimizing power, delay and area. …”
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Cooperative Co-evolution using LSHADE with Restarts For The CEC15 Benchmarks by El-Abd, Mohammed

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Fuzzy simulated evolution for power and performance optimization ofVLSI placement by Sait, Sadiq M.

“…In this paper, an algorithm for VLSI standard cell placement for low power and high performance design is presented. …”
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A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments by Barada, Hassan

“…The performance of SE is compared with a genetic algorithm approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms. r 2003 Elsevier Science Ltd. …”
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Fast force-directed/simulated evolution hybrid for multiobjective VLSI cell placement by Sait, Sadiq M.

“…In this work, a fast hybrid algorithm is designed to address this problem. The algorithm employs simulated evolution (SE), an iterative search heuristic that comprises three steps: evaluation, selection and allocation. …”
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Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review by Chakraborty, Sanjoy

“…Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. …”
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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. …”

FAST FUZZY FORCE-DIRECTED/SIMULATED EVOLUTION METAHEURISTIC FOR MULTIOBJECTIVE VLSI CELL PLACEMENT by Sait, Sadiq M.

“…SE is hybridized with force directed algorithm to speed-up the search. The proposed schemes are compared with previously presented SE based heuristics. …”
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Centroid-Based Differential Evolution with Composite Trial Vector Generation Strategies for Neural Network Training by El-Abd, Mohammed

“…Differential evolution (DE), a popular population-based metaheuristic algorithm, is an interesting alternative for tackling challenging optimisation problems. …”
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A new method for broad‐scale modeling and projection of plant assemblages under climatic, biotic, and environmental cofiltering by Alessandro Ferrarini (3951098)

Human digital thought clones: the <i>Holy Grail</i> of artificial intelligence for big data by Jon Truby (14153022)

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends by Abdellatif M. Sadeq (16931841)

“…<p dir="ltr">Studying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. …”

Performance Modeling of Rooftop PV Systems in Arid Climate, a Case Study for Qatar: Impact of Soiling Losses and Albedo Using PVsyst and SAM by Sachin Jain (19161721)

“…To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. Additionally, the study utilized dynamic albedo inputs to better account for ground-reflectance effects in energy yield simulations. …”

Video Streaming over D2D Networks by Ahmed, Ibtihal Mohamed Taha

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Parallel processing by Mansour, Nashat

“…The parallel genetic algorithm is based on a model of natural evolution. …”
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2022 IEEE Congress on Evolutionary Computation (CEC) by El-Abd, Mohammed

“…The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. …”
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