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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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5
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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6
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). …”
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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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9
Design and implementation of an optimal fuzzy logic controller using egentic algorithm
Published 2011“…Works so far reponed techniques which are on how to oveTCQme or reduce the effects of these issues for ensuring smoother and finely tuned eolltrolling proceM. The devised solution is softwllrebased which employs an algorithmic approach for programming II PICI6F877A microcontroller, thus eliminating allogether the parametric dependence issues while adding the benefits of easier modification to suit a given control system for varying operational conditions_ The approach is first simulated using MATLAB/and the simulated results are verified by programming {he PICI6I'g77A mierocontrolter with {he algorithm and using it on a temperature control system where a fan is regulated in response to variations in the ambient system temperature. …”
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Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
Published 2005“…To facilitate the implementation of these algorithms, a semi-automated computational tool using Matlab® programming facilities is developed. …”
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Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes
Published 2020“…Solutions of MOGA are illustrated using the Pareto fronts. …”
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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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Case driven TLC model checker analysis in energy scenario
Published 2023“…To foster the functional safety of corresponding program-algorithmic solutions, the model checking techniques and related tools are applied to the formal specifications of named solutions. …”
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Optimal HE-PWM inverter switching patterns using differential evolution algorithm
Published 2012“…The DE algorithm needs a relative small amount of generations to reach accurate solutions with appropriate control parameters. …”
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Using genetic algorithms to optimise land use suitability
Published 2012“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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Problem restructuring in interger programming for reduct searching
Published 2003“…Standard Integer Programming / Decision Related Integer Programming (SIP/DRIP) is a reduct searching system that finds the reducts in an information system. …”
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Empirical Evaluation of Mutation Step Size in Automated Evolution of Non-Target-Based 3D Printable Objects
Published 2015“…In this study, an EA in the form of Evolutionary Programming (EP) is used to automatically evolve 3D objects generated by Geilis’s Superformula. …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…Furthermore, the AHP VL-WIDE solutions were more fulfilling to the desire of the decision-maker compared with other algorithm.…”
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Exploring dynamic self-adaptive populations in differential evolution
Published 2006“…Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. …”
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