A Survey of Optimization by Building and Using Probabilistic Models
By Martin Pelikan, David E. Goldberg, Fernando Graça Lobo. Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
936 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/4925c591ca56638274ed2120521b853848a8e1b3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- J. Urgen Schmidhuber
- Yaochu Jin
- Sergey Levine
- Shimon Whiteson
- Pieter Abbeel
- Ponnuthurai Nagaratnam Suganthan
- Yoshua Bengio
- Carlos Artemio Coello Coello
In Information Systems
- Mark Hauschild
- Martin Pelikan
- José Antonio Lozano
- Pedro Larrañaga
- Henry Shu-Hung Chung
- Jan Peters
- J. Urgen Schmidhuber
- Zhi-hui Zhan
Citations in the corpus
- Genetic algorithms for modelling and optimisation (2005, background): “Recently, there has been considerable research interest in estimation of distribution algorithms (EDAs) [25,26].”
- 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, background): “That is why the overall time complexity is significantly reduced for large problems [204].”
- An introduction and survey of estimation of distribution algorithms (2011, methodology): “In addition, this survey covers many algorithms and methods that have not been covered in previously published surveys on this topic [4,17].”
- Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “In the literature, statistical methods [33], competitive learning techniques [34], Bayesian network [35], and linkage learning techniques [36] are reportedly being used to improve probability models.”
- Natural Evolution Strategies (2008, methodology): “These include the broad class of genetic algorithms (Holland, 1975; Goldberg, 1989), differential evolution (Storn and Price, 1997), estimation of distribution algorithms (Larrañaga, 2002; Pelikan et al., 2000; Bosman and Thierens, 2000; Bosman et al., 2007; Pelikan et al., 2006), particle swarm opt”
- Evolutionary Algorithms for Solving Multi-Objective Problems (2002, background): “Crossover and mutation are replaced in this case by generation of new samples according to the probabilistic model adopted (Pelikan et al., 1999).”
- Linkage Problem, Distribution Estimation, and Bayesian Networks (2000, background): “For a more detailed overview, see Pelikan et al. (1999) .”
- Hybrid Genetic Algorithms: A Review (2006, methodology): “In probabilistic model-building genetic algorithms (PMBGA) or estimation of distribution algorithms (EDA) [66], a probabilistic model is utilized to learn the structure of a problem on the fly.”
- Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms (2006, methodology): “The structural constraints are specified by Bayesian or Markov networks, which use the separation and d-separation concepts [30] to codify statements of probabilistic independence.”