Performance evaluation of an advanced local search evolutionary algorithm

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

312 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6e165f0eedd02414d0c6f75780e3e253845e68e6

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In Computational Theory and Mathematics

Citations in the corpus

Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009 (2010, unknown): “In fact, the true expected runtime of the (truly) restarted algorithm obeys the same formula [2], where b E ( N ) and b E ( N ) are the ex-ha (24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,…”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “In the ES algorithm with covariance matrix adaptation [32], the solution space is transformed into a new space via an adaptive covariance matrix, and the dimension of the new space is reduced by using the PCA technique.”
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, unknown): “…(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.…”
A restart CMA evolution strategy with increasing population size (2005, methodology): “where the population is kept fixed after each restart of the (pw, )-CMA-ES, presented in [1] and referred to as LR-CMA-ES in the following.”
COCO: a platform for comparing continuous optimizers in a black-box setting (2016, methodology): “or of the expected runtime. The estimated expected runtime of the restarted solver, ERT, is often plotted against dimension to indicate scaling with dimension. The ERT, also known as Enes [60] or SP2 [7] or aRT, is computed by dividing the sum of all evaluations before the target was reached (from s”
Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, background): “Moreover, they represent a broad array of optimization techniques, including single search operator (CoEVO, DE, EDA, G-CMA-ES, L-CMA-ES, K-PCX, SPC-PNX), and multimethod memetic (BLX-GL50, BLX-MA, DMS-L-PSO), and self-adaptive (L-SaDE) evolutionary algorithms.”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “The results of the compared algorithms are all derived directly from their corresponding references except that the results of CMA-ES are obtained by our independent experiments on these functions based on the provided source code [58].”