Estimation of Distribution Algorithms

By Pedro Larrañaga, J. A. Lozano. Genetic Algorithms and Evolutionary Computation, 2002

835 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b38f06023d26a5734fc4fc304b3f862a55278d6a

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In Artificial Intelligence

Citations in the corpus

Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms (2006, positive): “The work developed in the field since our first edited book (Larrañaga and Lozano (2002)), has motivated us to compile a subset of the great advances on EDAs in this new volume.”
Model-based methods for continuous and discrete global optimization (2017, neutral): “Lozano [14] review different ways of using probabilistic models as EDA instantiations.”
Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion (2006, positive): “The model-based offspring generation method used in this paper is closely related to a large class of search algorithms known as estimation of distribution algorithms (EDAs) [9] in the evolutionary computation community.”
Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification (2005, neutral): “Metaheuristics include, among others, simulated annealing [21], tabu search [18], iterated local search [26], variable neighborhood search [20], and various populationbased models such as evolutionary algorithms [3], scatter search [19], memetic algorithms [28], and various estimation of distributio”
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm (2010, neutral): “We tried to re-produce the conditions of (Larra˜naga & Lozano, 2001), initializing means uniformly over the initial ranges [ − B, B ] d where B is the speciﬁed bound in Table 1, taking N = 2000 points at each iteration and selecting the best µ = 1000 points to compute the updates.”
Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II (2020, positive): “We focus on the use of evolutionary algorithms (EAs) since they generally allow flexible data representations and have shown to combine well with probabilistic modeling approaches [27]—which forms the crux of our learning strategy.”
Evolutionary Computation for Reinforcement Learning (2012, positive): “…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).”
Ant-based and swarm-based clustering (2007, positive): “Estimation of distribution algorithms (EDAs) (Larrañaga and Lozano 2002) are a close relative of genetic algorithms.”