Results 21 to 30 of about 382,168 (278)
Autonomous Evolutionary Algorithm [PDF]
, 2010 Evolutionary algorithms (EA) are randomized heuristic search methods based on the principles of natural evolution (Banzhaf et al., 1998; Goldberg, 1989; Holland, 1975; Back, 1996; Koza, 1992). If we know how to describe the problem using the terminology of artificial evolution, the EAs are quite easy to apply.
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Meta-heuristic algorithms in car engine design: a literature survey [PDF]
, 2015 Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy.
Tayarani-N, Mohammad-H. +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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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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Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View [PDF]
, 2013 Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules ...
Chen, Yan +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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Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) [PDF]
, 2020 Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models.
Cheng, Ran +4 more
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IEEE AccessThis paper introduces a novel sliding mode control (SMC) design utilizing a Proportional-Integral-Derivative (PID) Sliding Surface (SS) for frequency regulation in multi-area electrical power systems (EPSs) with hydropower turbines, accounting for random
Dao Trong Tran +3 more
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Experimental study on population-based incremental learning algorithms for dynamic optimization problems [PDF]
, 2005 Copyright @ Springer-Verlag 2005.Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms.
Yang, S, Yao, X
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IEEE AccessThis paper proposes a novel load frequency control (LFC) scheme for multi-area thermal-hydro power systems (MATHPS) subject to multiple communication delays.
Anh-Tuan Tran +4 more
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