Search Results - (( evolution optimization using algorithm ) OR ( using function search algorithm ))
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1
Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. …”
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VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
Published 2012“…A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…This diversifies the search for global optimum. The classical benchmark problems and composite benchmark functions from Congress on Evolutionary Computation (CEC) 2005 special session is used for validate SDAA. …”
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Evaluation of Search Result of Document Search Based GA (DSEGA)
Published 2004“…Although i has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Genetic algorithms describe a set of optimization techniques that given a goal or fitness function are used to search a space for optimal points. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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6
Optimal location and size of distributed generation to reduce power losses and improve voltage profiles using differential evolution optimization method
Published 2016“…In fact, there are several algorithms used for optimizing the size and finding the best location to install DG units in the power system. …”
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7
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…Past studies have revealed that GAs are one of the most prevalently used stochastic search techniques to date. The strength of the algorithm lies in the fact that it assists the evolution of a population of individuals who would thrive in the survival of the fittest towards the next generation. …”
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9
A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…Apart from the best low-level heuristic, EMCQ also gives a chance to low-performing heuristics to search for the optimal threshold values using their probability density function. …”
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Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems
Published 2023“…In this paper, the Manta Ray Foraging Optimization (MRFO) algorithm is applied to solve real parameter constrained optimization problems, using the Gradient-based Mutation MRFO (cMRFO) variant. …”
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Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed
Published 2019“…In both algorithms, global searching is performed by APSO algorithm. …”
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12
A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…Apart from the best low-level heuristic, EMCQ also gives a chance to low-performing heuristics to search for the optimal threshold values using their probability density function. …”
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Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems
Published 2022“…MRFO is proven to solve artificial benchmark-function test by relatively good performance compared to several state-of-the-art algorithm while GbM is a productive approach to repair solution which led to improve the feasibility of the solution throughout the search by using Jacobian approximation in finite differences. …”
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Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices
Published 2017“…In terms of stability, reliability, robustness and computational efficiency, they provide better performance than GA. Multi-objective optimizations considering both objective functions are useful for the designer to find the suitable design parameters of TECs which balance the important roles of cooling rate and COP. © 2016 by the authors. …”
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A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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Two level Differential Evolution algorithms for ARMA parameters estimatio
Published 2013“…The first level searches for the appropriate model order while the second level computes the optimal/sub-optimal corresponding parameters. The performance of the algorithm is evaluated using both simulated ARMA models and practical rotary motion system. …”
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Proceeding Paper -
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Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm
Published 2013“…A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). …”
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Crossover-first differential evolution for improved global optimization in non-uniform search landscapes
Published 2015“…The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. …”
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Differential evolution optimization algorithm based on generation systems reliability assessment integrated with wind energy
Published 2019“…This stuffy proposed a novel optimization method labeled the "Differential Evolution Optimization Algorithm" (DEOA) to assess the reliability of power generation systems (PGS). …”
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