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A Boltzmann Multivariate Estimation of Distribution Algorithm for Continuous Optimization
2014
Proceedings of the International Conference on Evolutionary Computation Theory and Applications
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A Boltzmann Multivariate Estimation of Distribution Algorithm for Continuous Optimization. ...
This paper introduces an approach for continuous optimization using an Estimation of Distribution Algorithm (EDA), based on the Boltzmann distribution. ...
This is a general framework for Boltzmann distribution based estimation of distribution algorithms, where practical EDAs have been derived from. ...
doi:10.5220/0005079902510258
dblp:conf/ijcci/DominguezVA14
fatcat:3jhhvoo2qrebpbstz6polzanz4
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
2006
Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06
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The difficulty of estimating the exact Boltzmann distribution in continuous state space is circumvented by adopting the multivariate Gaussian model, which is popular in continuous EDA, to approximate only ...
This paper extends the Boltzmann Selection, a method in EDA with theoretical importance, from discrete domain to the continuous one. ...
To make use of the Boltzmann distribution, Mühlenbein et.al. proposed the Boltzmann Selection and the Boltzmann Estimation of Distribution Algorithm(BEDA) [18] for combinatory optimization. ...
doi:10.1145/1143997.1144070
dblp:conf/gecco/YunpengXJ06
fatcat:4gak5wgoqrebrner3aoi754jbi
Symmetric-approximation Energy-based Estimation of Distribution (SEED): a continuous optimization algorithm
2019
IEEE Access
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Estimation of Distribution Algorithms (EDAs) maintain and iteratively update a probabilistic model to tackle optimization problems. ...
INDEX TERMS Boltzmann selection, estimation of distribution algorithms, Kullback-Leibler divergence, J-divergence. ...
Some examples of this type of EDAs are: Boltzmann-EDA (BEDA) [44] , Boltzmann-Gaussian Univariate Marginal Distribution Algorithm (BG-UMDA) [45] , Estimation of Multivariate Normal Algorithm with Boltzmann ...
doi:10.1109/access.2019.2948199
fatcat:kztqipe6tbcn7h6v3yr3kolaja
Experience in Using Stochastic Optimization Methods for Determining Numerical Parameters of Models in Materials Structurization Management Systems
2018
International Journal of Engineering & Technology
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The program implements ten modifications of the simulation algorithm for annealing, allowing for a finite number of steps to make an estimate of the optimal value of the input elements of the function ...
In particular, modification of A, B and B algorithm schemes using the Boltzmann and Cauchy distribution functions, as well as the superfast annealing algorithm and the Xin Yao algorithm are implemented ...
In the random search algorithm for simulated annealing of optimal parameters Ξ by the Boltzmann scheme, it was customary to modify the Gibbs distribution function. ...
doi:10.14419/ijet.v7i3.5.15196
fatcat:awh4w6dytbgp3h3z7zmnzmspcq
A Boltzmann based estimation of distribution algorithm
2013
Information Sciences
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The Elitist Convergent Estimation of Distribution Algorithm (ECEDA), is a definition of a class of EDA which guarantees convergence to the optimum. ...
This paper introduces the conceptual ECEDA and a practical approach derived from it, called the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). ...
Introduction The Estimation of Distribution Algorithms (EDAs) were first introduced for global optimization in discrete spaces [12] [1] , then several approaches were extended to continuous domains ...
doi:10.1016/j.ins.2013.02.040
fatcat:ijegtm375jcpra72fkf7hb3vkm
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
[article]
2016
arXiv
pre-print
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Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. ...
We integrate a DBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. ...
Introduction Estimation of Distribution Algorithms (EDAs) [12, 8] are metaheuristics for combinatorial and continuous non-linear optimization. ...
arXiv:1509.06535v2
fatcat:2r3jlxutfvcxzcl4avnjvhz5gi
Efficient Estimation of Distribution Algorithms by Using the Empirical Selection Distribution
[chapter]
2010
New Achievements in Evolutionary Computation
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Continuous variables For the continuous case, consider a univariate search space with domain in the interval [a,b] , then a set of points i x for i=1,2,...,|X| define partitions. ...
Introduction Estimation of Distribution Algorithms (EDAs) (Mühlenbein et al., 1996; Mühlenbein & PaaB, 1996) are a promising area of research in evolutionary computation. ...
from: http://www.intechopen.com/books/newachievements-in-evolutionary-computation/efficient-estimation-of-distribution-algorithms-by-using-theempirical-selection-distribution © 2010 The Author(s). ...
doi:10.5772/8056
fatcat:e2f3hj6ambgvboa3ydpnt4vvxe
A Survey of Some Model-Based Methods for Global Optimization
[chapter]
2012
Optimization, Control, and Applications of Stochastic Systems
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Abstract We review some recent developments of a class of random search methods: model-based methods for global optimization problems. ...
We have developed various frameworks for model-based algorithms to guide the updating of probabilistic models and to facilitate convergence proofs. ...
(a) In continuous optimization when multivariate normal distributions with mean vector µ and covariance matrix Σ are used as the parameterized family, then it is easy to show that Theorem 1 implies lim ...
doi:10.1007/978-0-8176-8337-5_10
fatcat:2ngptgrckvhkdez2bamxssyf4e
Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly
2014
Journal of Geochemical Exploration
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In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial neural network, was used to recognize the mineral potential area in Korit 1:100000 sheet, ...
For this purpose, 470 geochemical stream sediment samples were collected from the study area and analyzed for 36 elements. ...
Also special thanks go to Industry, Mine & Trade Organization of South Khorasan for providing the required data. ...
doi:10.1016/j.gexplo.2014.02.013
fatcat:ll7zrqysone3tpn2qhvse4t4wm
Detection of Voltage Anomalies in Spacecraft Storage Batteries Based on a Deep Belief Network
2019
Sensors
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detection algorithm for spacecraft storage batteries based on a deep belief network (DBN) is proposed. ...
For a spacecraft, its power system is vital to its normal operation and capacity to complete flight missions. The storage battery is an essential component of a power system. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s19214702
pmid:31671886
pmcid:PMC6864756
fatcat:46elsftf7nfyppwi6fieiruf3a
Mathematical Analysis of Evolutionary Algorithms
[chapter]
2002
Operations Research/Computer Science Interfaces Series
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We present a mathematical theory based on probability distributions. ...
Today evolutionary algorithms have been successfully used in a number of applications. ...
to define the
(Boltzmann Estimated
Distribution Algorithm). ...
doi:10.1007/978-1-4615-1507-4_24
fatcat:m5womgzhzzbftjhe3dpmpsy4ii
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
[article]
2016
arXiv
pre-print
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Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. ...
We integrate a GAN into an EDA and evaluate the performance of this system when solving combinatorial optimization problems with a single objective. ...
Introduction Estimation of Distribution Algorithms (EDA) [7, 6] are metaheuristics for combinatorial and continuous non-linear optimization. ...
arXiv:1509.09235v2
fatcat:yhneqaql7jg2lfuobemcif5uzy
Bayesian Optimization for Adaptive MCMC
[article]
2011
arXiv
pre-print
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This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. ...
We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust ...
This algorithm is restricted to the adaptation of the multivariate random walk Metropolis algorithm with Gaussian proposals. ...
arXiv:1110.6497v1
fatcat:swkhgdjzjzbblkzb7z4poblwzy
Simulated annealing: Practice versus theory
1993
Mathematical and computer modelling
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Acknowledgements Many of the authors cited here generously responded to my electronic mail requests for (p)reprints on current work in this field; quite a few read earlier drafts and contributed their ...
Graphs were produced using XVGR (graphics for exploratory data analysis), a public domain software package running under UNIX and X11, developed by Paul Turner at the Oregon Graduate Institute. ...
These estimates are used to estimate an optimal ensemble size. ...
doi:10.1016/0895-7177(93)90204-c
fatcat:jpooy3cutbaujmdhpxf5yaybsm
The Nonnegative Boltzmann Machine
1999
Neural Information Processing Systems
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Application of maximum likelihood estimation to this model gives a learning rule that is analogous to the binary Boltzmann machine. ...
We illustrate learning of the NNBM on a transiationally invariant distribution, as well as on a generative model for images of human faces. ...
interpretation of the Boltzmann machine. ...
dblp:conf/nips/DownsML99
fatcat:ebespnqfnfdlnpzf65eg6bnuma
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