Improved evolutionary optimization from genetically adaptive multimethod search

By Jasper A. Vrugt, Bruce A. Robinson. Proceedings of the National Academy of Sciences of the United States of America, 2007

626 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7dd60541937bab10ef9b6405859c8473ee14ad7d

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Citations in the corpus

Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…( Beven and Binley, 1992; Freer et al., 1996; Gupta et al., 1998; Kuczera and Parent, 1998; Vrugt et al., 2002; Wagener et al., 2003; Beven, 2006; Vrugt and Robinson, 2007), emerging approaches include state-space ﬁltering (Vrugt et al., 2005; Moradkhani et al., 2005a, b; Slater and Clark, 2006;…”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “A MATLAB toolbox, similar to the one presented herein for DREAM, is1204 available for each of these four algorithms, including multi-criteria optimization with AMALGAM (Vrugt and Robinson, 2007b).1206”
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, methodology): “Parameter sets were populated by AMALGAM, which is a set of sampling-based heuristic optimization algorithms, in the calibration (Vrugt and Robinson, 2007).”
Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction (2010, methodology): “[82] The hybrid strategies above are in line with recent developments in global optimization, which seek to combine multiple methods such as genetic algorithms, simulated annealing, swarms, and ant colonies, etc., for handling complex objective functions [e.g., Vrugt and Robinson, 2007 ].”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “Multi-methods have been also applied to improve multi-objective optimization problems [244,245].”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “In recognition of this fact, a number of self-tuning and selforganising optimisation algorithms have been developed (Hadka and Reed, 2013; McClymont et al., 2013; Vrugt and Robinson, 2007; Vrugt et al., 2009).”
Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, background): “Fourth, it is interesting to observe that similar search operators as selected here for single objective optimization, were selected for population evolution in the multiobjective version of AMALGAM [41].”
Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008, methodology): “…Algorithm (NSGA-II, Deb et al., 2002), the Multiobjective Shuffl ed Complex Evolution Metropolis algorithm (MOSCEM-UA, Vrugt et al., 2003b), and AMALGAM (Vrugt and Robinson, 2007), a recently developed multialgorithm genetically adaptive search method for estimating the Pareto set of solutions.”
Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality (2010, methodology): “The AMALGAM evolutionary search approach developed by Vrugt and Robinson [2007] , and Vrugt et al. [2009a] provides an example of such a method, and has been shown to outperform existing calibration algorithms when confronted with a range of excessively difficult response surfaces.”
Battling Arrow’s Paradox to Discover Robust Water Management Alternatives (2016, methodology): “Recently introduced autoadaptive algorithm technologies (Vrugt and Robinson 2007; Hadka and Reed 2013) seek to alleviate these failure modes by combining multiple algorithms or multiple search operators; the Borg MOEA used in this study is autoadaptive.”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, methodology): “Vrugt and Robinson (2007) and Vrugt et al. (2009) address this issue by introducing an adaptive multi-operator hybrid called AMALGAM.”
Recent advances in selection hyper-heuristics (2020, methodology): “The proposed approach outperforms each MOEA when run on its own as well as the AMALGAM (a multialgorithm, genetically adaptive multiobjective) approach (Vrugt and Robinson, 2007) on the WFG test suite.”