Results 11 to 20 of about 46,799 (193)

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Mathematics, 2022
Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects.
Rana Muhammad Adnan Ikram, Leonardo Goliatt, Ozgur Kisi, Slavisa Trajkovic, Shamsuddin Shahid   +4 more
doaj   +3 more sources

A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization Under Linear Constraints [PDF]

IEEE Transactions on Evolutionary Computation, 2019
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The
Patrick Spettel, Hans-Georg Beyer, Michael Hellwig   +2 more
openaire   +4 more sources

An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

Scientific Reports, 2023
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques.
Amala Mary Vincent, P. Jidesh
doaj   +1 more source

CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling

Journal of Harbin University of Science and Technology, 2022
Aiming at the multiobjective cloud resource scheduling problem, with the goal of optimizing the total completion time and total execution cost of the task, a fuzzy cloud resource scheduling model is established using the method of fuzzy mathematics ...
LI Cheng-yan, SONG Yue, MA Jin-tao
doaj   +1 more source

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, Alicja Poturała, Jarosław Arabas, Stanisław Kozdrowski   +3 more
doaj   +1 more source

Robust Covariance Matrix Adaptation Evolution Strategy: Optimal Design of Magnetic Devices Considering Material Variation

IEEE Access, 2023
Uncertainties caused by material variation can significantly impair the characteristics of devices. Therefore, it is important to design devices whose performance is not significantly damaged even when material variations occur. Robust optimization seeks
Akito Maruo, Hajime Igarashi
doaj   +1 more source

Accurate parameters extraction of PEMFC model based on metaheuristics algorithms

Energy Reports, 2021
The electro-chemical proton exchange membrane fuel cell (PEMFC) is an inventing electrical generator from chemical reaction process as a green energy source.
Ahmed A. Zaki Diab, Hamdi Ali, H.I. Abdul-Ghaffar, Hany A. Abdelsalam, Montaser Abd El Sattar   +4 more
doaj   +1 more source

Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization

Algorithms, 2019
The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs).
Wei Li
doaj   +1 more source

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

Energies, 2022
The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions ...
Dharmesh Dabhi, Kartik Pandya, Joao Soares, Fernando Lezama, Zita Vale   +4 more
doaj   +1 more source

A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems [PDF]

, 2016
The multi-tier Covariance Matrix Adaptation Pareto Archived Evolution Strategy (m-CMA-PAES) is an evolutionary multi-objective optimisation (EMO) algorithm for real-valued optimisation problems.
A Hatamlou, A Sfrent, Alex Shenfield, BW Silverman, C Igel, CA Coello Coello, DE Goldberg, E Zitzler, E Zitzler, E Zitzler, F Wilcoxon, I Falco De, J Derrac, J Derrac, JD Knowles, JD Knowles, K Deb, K Deb, K Li, M Epitropakis, N Hansen, N Hansen, N Srinivas, P Civicioglu, PAN Bosman, Q Zhang, Q Zhang, RC Purshouse, S García, S García, Shahin Rostami, Y Wang, Z Michalewicz, Z Yang   +33 more
core   +1 more source