Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

By Pedro Larraanaga, J. A. Lozano. 2001

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “Related methods include probability distributionbased EAs (Baluja, 1994; Larraanaga & Lozano, 2001; Sałustowicz & Schmidhuber, 1997; Saravanan & Fogel, 1995), Covariance Matrix Estimation Evolution Strategies (CMA-ES) (Hansen, Müller, & Koumoutsakos, 2003; Hansen & Ostermeier, 2001; HeidrichMeisner…”
Performance Evaluation of an EDA-Based Large-Scale Plug-In Hybrid Electric Vehicle Charging Algorithm (2012, background): “EDAs are evolutionary algorithms based on global statistical information extracted from promising solutions [18], [19].”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “[91] based EDA and proposed three hybrid algorithms namely, UMHC, UMSA, and UMEGS by hybridizing decompositionbased EDA with hill climbing, SA, and evolutionary gradient search (EGS), respectively.”
DE/EDA: A new evolutionary algorithm for global optimization (2005, background): “In EDAs for the global continuous optimization problem, the probabilistic model p(x) can be a Gaussian distribution [11], a Gaussian mixture [12][13], a histogram [14], or a Gaussian model with diagonal covariance matrix (GM/DCM) [ 12 ].”
Comparing parameter tuning methods for evolutionary algorithms (2009, background): “with Estimation of Distribution Algorithms (EDA) in mind, it is based on the same general idea [10].”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “algorithm (meta-EDA) [77] since it use the same general idea as EDA [79], that is, estimating the distribution of promising parameter values [80].”
Simulation optimization: a review of algorithms and applications (2014, methodology): “Estimation of distribution algorithms (EDAs) [126] are model-based methods that belong to the evolutionary computation ﬁeld.”
Simulation optimization: a review, new developments, and applications (2005, methodology): “The crudest form of this would utlize all samples generated to a certain point, hence the use of memory, but in practical implementation, parameterization of the distribution is generally employed, and the parameters are updated based on the samples; see Larrañaga and Lozano (2002) for more details.”
Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization (2008, methodology): “1 Introduction Numerous metaheuristic algorithms have been successfully applied to many optimization problems [1, 2, 5, 9, 10, 15, 16, 17, 21, 35, 39].”
A Field Guide to Genetic Programming (2008, background): “Different EDAs use different models for the probability distribution that controls the sampling (see (Larrañaga, 2002; Larrañaga and Lozano, 2002) for more information).”
Evolutionary Computation for Reinforcement Learning (2012, methodology): “…multiobjective methods (Deb, 2001; Coello et al, 2007) diversifying algorithms (Holland, 1975; Goldberg and Richardson, 1987; Mahfoud, 1995; Potter and De Jong, 1995; Darwen and Yao, 1996) and distribution-based methods (Larranaga and Lozano, 2002; Hansen et al, 2003; Rubinstein and Kroese, 2004).”