Individuals redistribution based on differential evolution for covariance matrix adaptation evolution strategy [PDF]
Scientific Reports, 2022 Among population-based metaheuristics, both Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) perform outstanding for real parameter single objective optimization.
Zhe Chen, Yuanxing Liu
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Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy [PDF]
Sensors, 2022 The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications.
Nouf Alharbi +2 more
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Contextual covariance matrix adaptation evolutionary strategies [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example, due to a change in the situation or context of the task, relearning is required to adapt
Abdolmaleki, Abbas +4 more
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Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy [PDF]
Proceedings of the 14th annual conference on Genetic and evolutionary computation, 2012 This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength
Loshchilov, Ilya +2 more
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Adaptive Exploration through Covariance Matrix Adaptation Enables Developmental Motor Learning
Paladyn, 2012 The “Policy Improvement with Path Integrals” (PI2) [25] and “Covariance Matrix Adaptation - Evolutionary Strategy” [8] are considered to be state-of-the-art in direct reinforcement learning and stochastic optimization respectively. We have recently shown
Stulp Freek, Oudeyer Pierre-Yves
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Parameter Optimization Using Covariance Matrix Adaptation—Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes [PDF]
Frontiers in Neuroinformatics, 2018 Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity.
Zbigniew Jȩdrzejewski-Szmek +5 more
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Covariance Matrix Adaptation MAP-Annealing
Proceedings of the Genetic and Evolutionary Computation Conference, 2023 Accepted to GECCO ...
Matthew Fontaine, Stefanos Nikolaidis
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Covariance matrix adaptation evolution strategy for robust load frequency control of hydro power systems [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2022 Modern power systems are very complex and critical to electrical engineering services. The interconnections between various areas, nonlinear dynamics, and huge inertia of the components, make the problem very complex.
Nitish Katal, Sanjay Kumar, Sanjay Kumar
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Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies [PDF]
Evolutionary Computation, 2020 We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the default CMA-ES with the advantages of separable CMA-ES without inheriting its drawbacks.
Akimoto, Youhei, Hansen, Nikolaus
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A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem
Applied Sciences, 2021 The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a ...
Maria Konieczka +3 more
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