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Proceedings of the 15th International Joint Conference on Computational Intelligence, 2023 The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a ...
Thomaser, A.M. +3 more
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Self-adaptive CMA-ES Algorithm
Mathematical and computer modelling. Series: Physical and mathematical sciences, 2023 У роботі буде розглянуто один із самоадаптивних алгоритмів підбору параметрів складних систем, прикладами яких являються нейронні мережі. Самоадаптивні алгоритми – це алгоритми, які змінюють свою поведінку під час виконання на основі доступної інформації та заздалегідь визначених механізмів винагороди. Ці алгоритми широко використовуються в різних
Y. Litvinchuk
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LB+IC-CMA-ES: Two Simple Modifications of CMA-ES to Handle Mixed-Integer Problems
Parallel Problem Solving from NatureWe present LB+IC-CMA-ES, a variant of CMA-ES that handles mixed-integer problems. The algorithm uses two simple mechanisms to handle integer variables: (i) a lower bound (LB) on the variance of integer variables and (ii) integer centering (IC) of variables to their domain middle depending on their value.
Marty, Tristan +4 more
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Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we can derive the optimal update at each iteration step thanks to Bayesian statistics.
Benhamou Eric +2 more
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This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we can derive the optimal update at each iteration step thanks to Bayesian statistics.
Benhamou Eric +2 more
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Optimizing CMA-ES with CMA-ES - Data and Code
2023Data and Code - Conference Paper at ECTA Optimizing CMA-ES with CMA ...
Thomaser, André +3 more
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Cooperative Coevolutionary CMA-ES With Landscape-Aware Grouping in Noisy Environments
IEEE Transactions on Evolutionary Computation, 2023Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables.
Yapei Wu +4 more
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Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be formulated in a unified and comparatively simple framework.
Nikolaus Hansen, Anne Auger
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Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be formulated in a unified and comparatively simple framework.
Nikolaus Hansen, Anne Auger
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Functionally specialized CMA-ES
Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008This paper aims the design of efficient and effective optimization algorithms for function optimization. This paper presents a new framework of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Recent studies modified the CMA-ES from the viewpoint of covariance matrix adaptation and resulted in drastic reduction of the ...
Youhei Akimoto +3 more
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Benchmarking CMA-ES with Basic Integer Handling on a Mixed-Integer Test Problem Suite
GECCO Companion, 2023We compare the performances of one implementation of CMA-ES (pycma version 3.3.0) for optimizing functions with both continuous and integer variables. The implementation incorporates a lower bound on the variance along the integer coordinates to keep the
Tristan Marty +4 more
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