BOA: the Bayesian optimization algorithm

By Martin Pelikan, David E. Goldberg, Erick Cantú-Paz. 1999

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Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “In general, several hyper-parameter optimization techniques have been based and borrowed ideas from the domains of statistical model selection and traditional optimization techniques [25, 96, 99].”
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018, methodology): “…across multiple objectives, and ﬁnally (5) BOA : We construct and employ a Bayesian Network inspired by the Bayesian Optimization Algorithm (BOA) Pelikan et al. (1999) to fully utilize the promising solutions present in our search history archive and the inherent correlations between the…”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, background): “distribution algorithms (EDAs) [28], compact genetic algorithms [29], Bayesian optimization algorithms (BOAs) [30], and dependency structure matrix (DSM)-driven genetic algo-”
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009, methodology): “Some algorithms such as estimation of distribution algorithms (EDAs) [24, 29, 30, 31, 32] perform an implicit decomposition by approximating a set of joint probability distributions to represent each interaction group.”
A survey on optimization metaheuristics (2013, methodology): “The best known multivariate EDAs are Bayesian Optimization Algorithm (BOA) [202, 203], Estimation of Bayesian Networks Algorithm (EBNA) [82], Factorized Distribution Algorithm (FDA) [189], Extended Compact Genetic Algorithm (EcGA) [124] and Polytree Approximation Distribution Algorithm (PADA) [251].”
The balance between proximity and diversity in multiobjective evolutionary algorithms (2003, background): “on a large variety of problems [25], [26], [27], [28], [29], [30], [31], [32], [7].”
A Survey of Optimization by Building and Using Probabilistic Models (2000, methodology): “Not only does the BOA ll the gap between the FDA and uninformed search methods but also oers a method that is eecient e v en without any prior information (Pelikan et al., 19988 Schwarz & Ocenasek, 19999 Pelikan et al., 1999) and still does not prohibit further improvement b y using this.”
Evolution strategies – A comprehensive introduction (2002, unknown): “Interestingly, only recently Mühlenbein and Mahnig (1999), who propose the so-called factorized distribution algorithm, as well as Pelikan et al. (1999), proposing the so-called Bayesian optimization algorithm, followed this road indicated long before by former ES results.”
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, methodology): “Some of these algorithms, such as BOA, are also used for real-valued optimization [46].”
Hybrid Genetic Algorithms: A Review (2006, methodology): “…(CGA), bivariate marginal distribution algorithms (BMDA), factorized distribution algorithms (FDA) and the Bayesian optimisation algorithm (BOA) [68] are all examples of PMBGA that are reported to have a better search ability, than that of the simple genetic algorithm, in solving a broad class…”
Generalized Inner Loop Meta-Learning (2019, methodology): “…through gradient-based methods (Bengio, 2000; Maclaurin et al., 2015; Luketina et al., 2016; Franceschi et al., 2017), as an alternative to grid/random search (Bergstra & Bengio, 2012) or Bayesian Optimization (Mockus et al., 1978; Pelikan et al., 1999; Bergstra et al., 2011; Snoek et al., 2012).”
The compact genetic algorithm (1998, background): “Recently, a number of algorithms have been suggested that are capable of dealing with pairwise gene interactions [4], [7], [16] and even with order-interactions [11], [17].”
A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice (2005, background): “[24]), learning factorized distribution algorithm (LFDA) (Muhlenbein and Mahnig [25]), and estimation of Bayesian network algorithm (EBNA)”
Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization (2008, methodology): “Some algorithms such as estimation of distribution algorithms (EDAs) [24, 29, 30, 31, 32] perform an implicit decomposition by approximating a set of joint probability distributions to represent each interaction group.”
NSGA-Net: neural architecture search using multi-objective genetic algorithm (2018, unknown): “In this section, we explain the experimental setup and implementation details of NSGA-Net, followed by the empirical results to demonstrate the efﬁcacy of NSGA-Net to automate the NAS process on image classiﬁcation task.”