A restart CMA evolution strategy with increasing population size

By Anne Auger, Nikolaus Hansen. IEEE Congress on Evolutionary Computation, 2005

1,073 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/36bcaabc15e812df931a8dd8f37c7cfcb0a07d54

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In Artificial Intelligence

Citations in the corpus

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization (2009, neutral): “…(Molina et al. 2005), CoEVO (Pošík 2005), DE (Rônkkônen et al. 2005), DMS-L-PSO (Liang and Suganthan 2005), EDA (Yuan and Gallagher 2005), G-CMA-ES (Auger and Hansen 2005a), K-PCX (Sinha et al. 2005), L-CMA-ES (Auger and Hansen 2005b), L-SaDE (Qin and Suganthan 2005), SPC-PNX (Ballester et al.…”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “CoDE was also compared with three non-DE approaches, namely, CLPSO [21], CMA-ES [22], and GL-25 [23].”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “Auger and Hansen have recently proposed a restart CMA-ES [58], where the population size is increased (IPOP) for each restart.”
How to Maximize the Likelihood Function for a DSGE Model (2010, neutral): “However, the procedure in Auger and Hansen (2005) deviates from our procedure because they gradually increase the population size in each restart and make the number of restarts endogenous.”
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models (2011, background): “Independent restarts with increasing population size are suggested in [4].”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, neutral): “2005 ), self-adaptive DE (Quin and Suganthan 2005 ), restart CMA-ES (Auger and Hansen 2005 ) and ABC (Akay and Karaboga 2010 ).”
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems (2012, methodology): “They are the JADE [53], HRCGA [13], APrMF [30], and G-CMA-ES [2] algorithms.”
Bio-inspired computation: Where we stand and what's next (2019, background): “CMA-ES [8], IPOP-CMA-ES [9] or the more recent SHADE approach [10]).”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “3 In [66], iterated F -Race method was applied to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES) [67] on CEC’05 benchmark set [68].”
Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, methodology): “PERFORMANCE OF THE RESTART CMA-ES (FROM [2]) IS ALSO INCLUDED”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, methodology): “The population restart technique (Auger and Hansen 2004) might be used to remedy the premature convergence of SADE/ BBO.”
Improving the search performance of SHADE using linear population size reduction (2014, methodology): “In addition, we compared LSHADE with NBIPOP-ACMA-ES [19] and iCMAES-ILS [20] which are state-of-the-art restart CMA-ES [12] variants and cowinners of the CEC 2013 competition on real-parameter single objective Optimization.”
Niching the CMA-ES via nearest-better clustering (2010, methodology): “Niching the CMA-ES via Nearest-Better Clustering Mike Preuss Computational Intelligence Group, Chair of Algorithm Engineering Technische Universität Dortmund, Germany mike.preuss@tu-dortmund.de ABSTRACT We investigate how a niching based evolutionary algorithm fares on the BBOB function test set, ”
Automatically improving the anytime behaviour of optimisation algorithms (2014, background): “Traditionally, the analysis is performed in terms of one (or both) classical views, that is, either measuring solution quality over fixed-runtimes [3], or runtime (CPU-time or function evaluations) over fixed qualitytargets [5].”
Self-adaptive learning based particle swarm optimization (2011, methodology): “The ranking of the algorithms is based on the successful performance SP [3], which is defined as follows:”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, methodology): “Moreover, two populations are utilized, one for CMA-ES and the other for the hybrid DE. LaTorre et al. [29] presented a multiple offspring sampling framework to combine a restart CMA-ES [30] with DE.”