Results 161 to 170 of about 46,799 (193)

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

2008
The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or ...
Hans-Georg Beyer, Bernhard Sendhoff
openaire   +1 more source

Sparse Covariance Matrix Adaptation Techniques for Evolution Strategies

2015
Evolution strategies are variants of evolutionary algorithms. In contrast to genetic algorithms, their search process depends strongly on mutation. Since the search space is often continuous, evolution strategies use a multivariate normal distribution as search distribution. This necessitates the tuning and adaptation of the covariance matrix.
Silja Meyer-Nieberg, Erik Kropat
openaire   +1 more source

Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation

Proceedings of IEEE International Conference on Evolutionary Computation, 2002
A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the fitness function (or the coordinate system).
N. Hansen, A. Ostermeier
openaire   +1 more source

Modified box constraint handling for the covariance matrix adaptation evolution strategy

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
We propose a modified box constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The existing box constraint handling turns the box-constrained optimization problem into an unconstrained optimization by introducing an artificial fitness landscape, where a penalty function is added to the function values at the ...
Naoki Sakamoto, Youhei Akimoto
openaire   +1 more source

PID Controller Tuning Based on the Covariance Matrix Adaptation Evolution Strategy

IEEJ Transactions on Electronics, Information and Systems, 2010
The covariance matrix adaptation evolution strategy (CMA-ES) is a kind of stochastic optimization such as particle swarm optimization (PSO), and has been shown to have a good performance. However, there are few control applications of the CMA-ES except for only one paper.
Yuji Wakasa, Shinji Kanagawa, Kanya Tanaka, Yuki Nishimura   +3 more
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

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
openaire   +1 more source

Covariance Matrix Adaptation Evolution Strategy optimization algorithm

2021
Shirkov, Aleksandr, Volkov, Evgeny
openaire   +1 more source

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
openaire   +1 more source