Search Results - (( parallel classification problem algorithm ) OR ( evolution optimization strategy algorithm ))
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1
A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…DE is developed based on an improved Genetic Algorithm and come with different strategies for faster optimization. …”
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2
Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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3
Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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4
Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
Published 2014“…This method resulted around 99% of classification rate. To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate.…”
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5
A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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6
Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…An improved version of Differential Evolution (DE) namely Backtracking Search Algorithm (BSA) is applied to several fed batch fermentation problems and its performance is compared with recent emerging metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and DE. …”
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7
Resource allocation in coordinated multipoint long term evolution-advanced networks
Published 2015“…The resource allocation algorithm is developed through three phases, namely Low-Complexity Resource Allocation (LRA), Optimized Resource Allocation (ORA) and Cross-Layer Design of ORA (CLD-ORA). …”
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8
Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem
Published 2023“…Other metaheuristic approaches such as genetic algorithm, differential evolution algorithm, particle swarm optimization, and ant colony optimization are still preferable to address combinatorial optimization problems. …”
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Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization
Published 2025“…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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10
A holistic review on artificial intelligence techniques for well placement optimization problem
Published 2020“…Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. …”
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11
The forecasting of poverty using the ensemble learning classification methods
Published 2023“…The results of the algorithms showed the poverty trend, which helped to determine the poverty classification. …”
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12
Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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13
Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm
Published 2018“…However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. …”
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14
Text classification using Naive Bayes: An experiment to conference paper
Published 2005“…In the problem of document text classification for conference paper, various papers themes will normally be classified manually by the conference management.Once the classification of the papers is ready, the parallel sessions for presentation according to the themes will be scheduled. …”
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15
Optimized differential evolution algorithm for linear frequency modulation radar signal denoising
Published 2013“…The main contention of this thesis is to investigate the development of new optimization technique based on Differential Evolution algorithm (DE), applied for radar signal denoising application. …”
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16
Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization
Published 2025“…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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17
Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…The proposed EC methods are Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), and Pareto-based Differential Evolution (PDE). …”
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18
Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking
Published 2024“…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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19
Hybrid harmony search algorithm integrating differential evolution and lévy flight for engineering optimization
Published 2025“…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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20
A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
Published 2013“…The Pareto Archived Evolution Strategy (PAES) algorithm is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing game scores and minimizing neural network complexity. …”
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