Results 21 to 30 of about 5,786,914 (366)
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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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 algorithms and dynamic programming [PDF]
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009 Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion.
Doerr, B. +4 more
openaire +4 more sources
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
doaj +1 more source
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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A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables [PDF]
, 2007 Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates
Bonte, M.H.A. +5 more
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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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TreeCmp: Comparison of Trees in Polynomial Time
Evolutionary Bioinformatics, 2012 When a phylogenetic reconstruction does not result in one tree but in several, tree metrics permit finding out how far the reconstructed trees are from one another.
Damian Bogdanowicz +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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