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  1. 1

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

    Published 1998
    “…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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    article
  2. 2

    Adaptive bias simulated evolution algorithm for placement by Youssef, H.

    Published 2001
    “…Simulated Evolution (SE) is a general meta-heuristic for combinatorial optimization problems. …”
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    article
  3. 3

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

    Published 2000
    “…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 Sait, Sadiq M.

    Published 2003
    “…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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    article
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    Fuzzy simulated evolution algorithm for multi-objectiveoptimization of VLSI placement by Sait, Sadiq M.

    Published 1999
    “…A fuzzy simulated evolution algorithm is presented for multi-objective minimization of VLSI cell placement problem. …”
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    article
  8. 8

    Simulated evolution algorithm for multiobjective VLSI netlist bi-partitioning by Sait, Sadiq M.

    Published 2003
    “…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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  9. 9

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

    Published 2020
    “…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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    article
  10. 10

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

    Published 2020
    “…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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    article
  11. 11

    A Novel Two-Level Clustering-Based Differential Evolution Algorithm for Training Neural Networks by El-Abd, Mohammed

    Published 2024
    “…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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    Parallelization of Stochastic Evolution by Khan, Khawar

    Published 2006
    “…In this work, the development of parallel algorithms for Stochastic Evolution, applied on multi-objective VLSI cell-placement problem is presented. …”
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    masterThesis
  14. 14

    Parallelization of Stochastic Evolution by Khan, Khawar

    Published 2006
    “…In this work, the development of parallel algorithms for Stochastic Evolution, applied on multi-objective VLSI cell-placement problem is presented. …”
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    masterThesis
  15. 15

    Enhanced simulated evolution algorithm for digital circuit design yielding faster execution in a larger solution space by Sait, Sadiq M.

    Published 2004
    “…Evolutionary algorithms have been studied by several researchers for the design of digital circuits. …”
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  16. 16

    Predicting Android Malware Using Evolution Networks by Chahine, Joy

    Published 2025
    “…We combine this model with genetic algorithms to optimize its parameters and return the best state transition probabilities, and we predict future malware accordingly. …”
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    masterThesis
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    Hybrid Cooperative Co-evolution for the CEC15 Benchmarks by El-Abd, Mohammed

    Published 2015
    “…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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  19. 19

    Hybrid Cooperative Co-evolution for Large Scale Optimization by El-Abd, Mohammed

    Published 2016
    “…In this paper, we propose the idea of hybrid cooperative co-evolution (hCC). In CC, multiple instances of the same evolutionary algorithm work in parallel, each optimizes a different subset of the problem in hand. …”
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