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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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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Evolutionary algorithm for protein structure prediction by Mansour, Nashat

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

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

“…This parameter has major impact on the algorithm run-time and the quality of the solution subspace searched. …”
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A Review for the Genetic Algorithm and the Red Deer Algorithm Applications by Abu Zitar, Raed

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A Novel Essential Mutation Method for Evolutionary Algorithms by Abou Doush, Iyad

“…The mutation is one of the operators that is used by many Evolutionary Algorithms (EA) to diversify the population (solutions). …”
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Evolutionary algorithms for VLSI multi-objective 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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Revolutionizing sustainable supply chain management: A review of metaheuristics by Abualigah, Laith

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An Evolutionary Algorithm for the Allocation Problem in High-Level Synthesis by Harmanani, Haidar

“…This paper presents an evolutionary algorithm to solve the datapath allocation problem in high-level synthesis. …”
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Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem by Abido, M. A.

“…Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. …”
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Multiobjective evolutionary algorithms for electric power dispatch problem by Abido, M.A.

“…Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. …”
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Evolutionary algorithm for predicting all-atom protein structure by Mansour, Nashat

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Review and analysis for the Red Deer Algorithm by Zitar, Raed

Subjects: “…Red Deer Algorithm…”
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Fuzzy simulated evolution algorithm for topology design of campusnetworks by Youssef, H.

“…Results show that on all test cases the simulated evolution algorithm exhibits more intelligent search of the solution subspace and was able to find better solutions than simulated annealing…”
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Fuzzy simulated evolution algorithm for VLSI cell placement by Sait, Sadiq M.

“…There has not been a consensus on how to accommodate all these (conflicting) requirements in the search for near optimal feasible solutions. In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. …”
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data by Behrouz Ahadzadeh (19757022)

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A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS by Youssef, H.

“…Results show that on all test cases, the Simulated Evolution algorithm exhibits a more intelligent search of the solution subspace and was able to find better solutions than Simulated Annealing and Esau–Williams algorithm. …”
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