Stochastic evolution algorithm for technology mapping by Al-Mulhem, A.S.

“…SELF-Map is based on the Stochastic Evolution (SE) algorithm. The state space model of the problem is defined and suitable cost function which allows optimization for area, delay, or area-delay combinations is proposed. …”
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Adaptive bias simulated evolution algorithm for placement by Youssef, H.

“…Simulated Evolution (SE) is a general meta-heuristic for combinatorial optimization problems. …”
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Revolutionizing sustainable supply chain management: A review of metaheuristics by Abualigah, Laith

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Fuzzy simulated evolution algorithm for topology design of campusnetworks by Youssef, H.

“…We present an approach based on the simulated evolution algorithm for the design of campus network topology. …”
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Fuzzy simulated evolution algorithm for VLSI cell placement. by Hussain, Ali Syed

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Fuzzy simulated evolution algorithm for VLSI cell placement by Sait, Sadiq M.

“…In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. Identification of near optimal solutions is achieved through a novel goal-directed fuzzy search approach. …”
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Cooperative Co-evolution using The Brain Storm Optimization Algorithm by El-Abd, Mohammed

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Fuzzy simulated evolution algorithm for multi-objectiveoptimization of VLSI placement by Sait, Sadiq M.

“…A fuzzy simulated evolution algorithm is presented for multi-objective minimization of VLSI cell placement problem. …”
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Simulated evolution algorithm for multiobjective VLSI netlist bi-partitioning by Sait, Sadiq M.

“…In this paper the Simulated Evolution algorithm (SimE) is engineered to solve the optimization problem of multi-objective VLSI netlist bi-partitioning. …”
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A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems by Bilal Khurshid (16715865)

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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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A Novel Two-Level Clustering-Based Differential Evolution Algorithm for Training Neural Networks by El-Abd, Mohammed

“…To address these challenges, we introduce a novel two-level clustering-based differential evolution approach, C2L-DE, to identify the initial seed for a gradient-based algorithm. …”
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Performance Assessment of Foraging Algorithms vs. Evolutionary Algorithms by El-Abd, Mohammed

“…This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. …”
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Enhanced simulated evolution algorithm for digital circuit design yielding faster execution in a larger solution space by Sait, Sadiq M.

“…Evolutionary algorithms have been studied by several researchers for the design of digital circuits. …”
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An evolutionary algorithm for network topology design by Youssef, H.

“…We present an approach based on a simulated evolution algorithm for design of a campus network topology. …”
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Evolutionary Algorithms for VLSIMultiobjective Netlist Partitioning by Sait, Sadiq M.

“…These heuristics are based on Genetic Algorithms (GAs), Tabu Search (TS) and Simulated Evolution (SimE). …”
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Island flower pollination algorithm for global optimization by Al-Betar, Mohammed Azmi

“…Flower pollination algorithm (FPA) is a recent swarm-based evolutionary algorithm that was inspired by the biological evolution of pollination of the flowers. …”
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Machine vision algorithm for MCQ automatic grading – MVAAG by Rababaah, Aaron Rasheed

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Allocating data to distributed-memory multiprocessors by genetic algorithms by Mansour, Nashat

“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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