Results 231 to 240 of about 3,655 (256)

Enhancing covariance matrix adaptation evolution strategy through fitness inheritance

2016 IEEE Congress on Evolutionary Computation (CEC), 2016
Evolution strategy (ES) has shown to be effective in many search and optimization problems. In particular, the ES with covariance matrix adaptation (CMAES) achieves great successes and is viewed as a state-of-the-art evolutionary algorithm for complex numerical optimization.
Rung-Tzuo Liaw, Chuan-Kang Ting
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Covariance Matrix Adaptation Evolution Strategy for Convolutional Neural Network in Text Classification

2021
Text classification has become relevant in recent years because of its usefulness in supporting different text mining solutions. Neural networks for this purpose have benefited from the creation of word embedding for learning semantics among words in a corpus.
Orlando Grabiel Toledano-López, Julio Madera, Héctor R. Gonzalez, Alfredo Simón-Cuevas   +3 more
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Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. [PDF]

, 2017
An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating realworld black-box fitness functions is sometimes very timeconsuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations.
Pitra, Z. (Zbyněk), Bajer, L. (Lukáš), Repický, J. (Jakub), Holeňa, M. (Martin)   +3 more
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On the optimization of degree distributions in LT code with covariance matrix adaptation evolution strategy

IEEE Congress on Evolutionary Computation, 2010
Luby Transform code (LT code) has been a popular and practical technique in the field of channel coding since its proposal. One of the key components of LT code is a degree distribution which is used to determine the relationship between source data and codewords.
Chih-Ming Chen, Ying-Ping Chen, Tzu-Ching Shen, John K. Zao   +3 more
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Sample Reuse in the Covariance Matrix Adaptation Evolution Strategy Based on Importance Sampling

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
Recent studies reveal that the covariance matrix adaptation evolution strategy (CMA-ES) updates the parameters based on the natural gradient. The rank-based weight is considered the result of the quantile-based transformation of the objective value and the parameters are adjusted in the direction of the natural gradient estimated by Monte-Carlo with ...
Shinichi Shirakawa, Youhei Akimoto, Kazuki Ouchi, Kouzou Ohara   +3 more
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Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy

Proceedings of the Genetic and Evolutionary Computation Conference, 2019
Gaussian processes modeling technique has been shown as a valuable surrogate model for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in continuous single-objective black-box optimization tasks, where the optimized function is expensive. In this paper, we investigate how different Gaussian process settings influence the error between the ...
Zbynek Pitra, Jakub Repický, Martin Holena   +2 more
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Covariance matrix adaptation evolution strategy based design of centralized PID controller

Expert Systems with Applications, 2010
In this paper, design of centralized PID controller using Covariance Matrix Adaptation Evolution Strategy (CMAES) is presented. Binary distillation column plant described by Wood and Berry (WB) having two inputs and two outputs and by Ogunnike and Ray (OR) having three inputs and three outputs are considered for the design of multivariable PID ...
M. Willjuice Iruthayarajan, S. Baskar 0001   +1 more
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Evolution strategies and CMA-ES (covariance matrix adaptation)

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014
Nikolaus Hansen, Anne Auger
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Scaling Up Covariance Matrix Adaptation Evolution Strategy Using Cooperative Coevolution

2013
Covariance matrix adaptation evolution strategy CMA-ES has demonstrated competitive performance especially on multimodal non-separable problems. However, CMA-ES is not capable of dealing with problems having several hundreds dimensions. Motivated by that cooperative coevolution CC has scaled up many kinds of evolutionary algorithms EAs to high ...
Jinpeng Liu, Ke Tang 0001
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Covariance Matrix Adaptation Evolution Strategy optimization algorithm

2021
Shirkov, Aleksandr, Volkov, Evgeny
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