Real-parameter optimization with differential evolution

By Jani Rönkkönen, Saku Kukkonen, K. Price. IEEE Congress on Evolutionary Computation, 2005

603 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/4721b02cc102a06fa2b3546f3866e7bfe0f97372

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Citations in the corpus

Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, background): “For the GA/PCX/PSO/DE algorithms, points generated outside the feasible region are reflected back from the bound by the amount of the violation [33].”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “It has been clear that some trial vector generation strategies are suitable for the global search [4] and some others are useful for rotated problems [5], and that some control parameter settings can speed up the convergence [6] and some other settings are effective for separable functions [7].”
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, background): “Some of the recent publications [7]–[9] indicate that DE may face significant difficulty on certain kind of functions.”
Differential Evolution: A Survey of the State-of-the-Art (2011, background): “Some of the recent publications [85], [S179] indicate that DE faces significant difficulty on functions that are not linearly separable and can be outperformed by CMA-ES.”
Population size reduction for the differential evolution algorithm (2008, background): “This subsection provides some background of the DE algorithm [24, 26, 27] that is important for understanding the idea of this paper.”
Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, background): “Hence, some empirical work focused on the selection of setting parameters to ensure the global convergence [103, 104].”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “2005), DE (Ronkkonen et al. 2005), self-adaptive DE (Quin and Suganthan 2005), restart CMA-ES (Auger and Hansen 2005) and ABC (Akay and Karaboga 2010).”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, methodology): “Here, " DE/rand/1/bin " is chosen because it employs a most commonly used trial vector generation strategy, with three commonly suggested sets of control parameters: 1) ; 2) ; and 3) [15] , [21], [33].”
A comparative study of differential evolution variants for global optimization (2006, methodology): “The modifications from variant to variant are in the recombination operator used (steps 9 to 15 in Figure 1) and also in the way individuals are selected to calculate the mutation vector (step 7 in Figure 1).”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “In Liu and Lampinen (2002b) and Rönkkönen et al. (2005) the setting F = C R = 0.9 is chosen on the basis of discussion in Price and Storn (1997).”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “is suggested as a good compromise between convergence speed and convergence probability in [43].”
Success-history based parameter adaptation for Differential Evolution (2013, neutral): “[1], [8], [9], [10]).”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “However, in [41] it was mentioned that F = 0.”
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): “The algorithms are: BLX- GL50 (García-Martínez and Lozano 2005), BLX-MA (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…”