Results 11 to 20 of about 404,478 (325)
Explicit memory schemes for evolutionary algorithms in dynamic environments [PDF]
, 2007 Copyright @ 2007 Springer-VerlagProblem optimization in dynamic environments has atrracted a growing interest from the evolutionary computation community in reccent years due to its importance in real world optimization problems.
D Dasgupta +13 more
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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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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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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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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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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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IET Collaborative Intelligent Manufacturing, 2021 Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long‐term monitoring of model performance and adaptation of models if surrounding conditions change and the ...
Florian Bachinger +2 more
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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.
openaire +3 more sources
Self-adaptation of mutation distribution in evolutionary algorithms [PDF]
, 2007 This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose,
Tinos, R, Yang, S
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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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