Using Markov Chain Based Estimation of Distribution Algorithm for Model-Based Safety Analysis of Graph Transformation

Pira, Einollah

doi:10.1007/s11390-020-1003-3

Cited by 3 publications

(1 citation statement)

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“…In most EDAs that do not restrict the dependence relationships between variables, the joint probability distribution is estimated by a Bayesian network (Section II-C) learned from data. EDAs have also been developed with the probability distribution estimated from log-linear probability models [39], probabilistic principal component analysis [40], Kikuchi approximations [41], Markov networks [42], [43], Markov chains [44], copulas and vines [45], a reinforcement learningbased method [46], Gaussian adaptive resonance theory neural networks [47], growing neural gas networks [48], restricted Boltzmann machines [49], [50], [51] and in the deep learning area, from autoencoders [52], variational autoencoders [53], [54], and generative adversarial networks [55]. Model selection in EDAs is a more complex problem.…”

Section: Initial Population Of Candidate Solutionsmentioning

confidence: 99%

Estimation of Distribution Algorithms in Machine Learning: A Survey

Larrañaga,

Bielza

2024

IEEE Trans. Evol. Computat.

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The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This paper reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional or probabilistic clustering, obtaining the optimal policy in reinforcement learning and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.

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