Results 11 to 20 of about 88,043 (267)
Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm
Alexandria Engineering Journal, 2021 Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades.
Wali Khan Mashwani +4 more
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A study on general state model of differential drive wheeled mobile robots
Journal of Advanced Engineering and Computation, 2023 This study introduces a novel approach by representing a multi-input-multi-output (MIMO) differential drive wheel mobile robot (DDWMR) using the standard state space representation for the first time.
Anh-Minh Duc Tran, Tri-Vien Vu
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Parallelism and evolutionary algorithms [PDF]
IEEE Transactions on Evolutionary Computation, 2002 This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these ...
Enrique Alba 0001, Marco Tomassini
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New observer-based control design for mismatched uncertain systems with time-delay
Archives of Control Sciences, 2016 In this paper, the state estimation problem for a class of mismatched uncertain time-delay systems is addressed. The estimation uses observer-based control techniques.
Huynh Van Van
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Neural Computing and Applications, 2022 This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e.
Mohd Herwan Sulaiman +4 more
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Hybridization of Evolutionary Algorithms [PDF]
, 2011 Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized.
Fister, Iztok +2 more
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Bias and Variance Analysis of Contemporary Symbolic Regression Methods
Applied SciencesSymbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar ...
Lukas Kammerer +2 more
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Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
Applied SciencesThis paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications.
Philipp Fleck +2 more
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IEEE AccessA speed controller based on an adaptive super-twisting sliding mode control algorithm (AST) is designed for field-oriented control (FOC) of permanent magnet synchronous motor (PMSM) drive.
Hau Huu Vo, Son Hai Hoang, Vo-Tan Phuoc
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Chaos-Based Optimization - A Review
Journal of Advanced Engineering and Computation, 2017 This paper discusses the utilization of the complex chaotic dynamics given by the selected time-continuous chaotic systems as well as by the discrete chaotic maps, as the chaotic pseudo-random number generators and driving maps for the chaos based ...
Roman Senkerik +2 more
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