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Keywords
(4)
Fitness Function
Genetic Algorithm
Probabilistic Model
Markov Random Field
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Markov random field modeling in computer vision
Updating the probability vector using MRF technique for a Univariate EDA
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Markov Random Field Modelling of Royal Road Genetic Algorithms
Markov Random Field Modelling of Royal Road Genetic Algorithms,10.1007/3540460330_6,Deryck F. Brown,A. Beatriz Garmendiadoval,John A. W. Mccall
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Markov Random Field Modelling of Royal Road Genetic Algorithms
(
Citations: 15
)
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Deryck F. Brown
,
A. Beatriz Garmendiadoval
,
John A. W. Mccall
Markov Random Fields (MRFs) [5] are a class of probabalistic models that have been applied for many years to the analysis of visual patterns or textures. In this paper, our objective is to establish MRFs as an interesting approach to modelling genetic algorithms. Our approach bears strong similarities to recent work on the Bayesian Optimisation Algorithm [9], but there are also some significant differences. We establish a theoretical result that every
genetic algorithm
problem can be characterised in terms of a MRF model. This allows us to construct an explicit
probabilistic model
of the GA fitness function. The model can be used to generate chromosomes, and derive a MRF fitness measure for the population. We then use a specific MRF model to analyse two Royal Road problems, relating our analysis to that of Mitchell et al. [7].
Conference:
Artificial Evolution  AE
, pp. 6576, 2001
DOI:
10.1007/3540460330_6
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Citation Context
(12)
...480 2009 IEEE Congress on Evolutionary Computation (CEC 2009) From which, following relationship between fitness and the energy can be deduced [
4
]...
Siddhartha Shakya
,
et al.
A fully multivariate DEUM algorithm
...From which, following relationship between fitness and the energy can be deduced [
19
] :...
Siddhartha Shakya
,
et al.
Optimization by estimation of distribution with DEUM framework based o...
...The initial theory was published in [
1
] and DEUM was first presented in [11]...
...In [
1
] it was shown that an equation for each individual in a population may be derived from the joint probability distribution shown in (1)...
Alexander E. I. Brownlee
,
et al.
Solving the MAXSAT problem using a multivariate EDA based on Markov ne...
...From which, following relationship between fitness and the energy can be deduced [
19
]...
Siddhartha Shakya
,
et al.
Optimisation by Estimation of Distribution with DEUM framework based o...
...In [
1
], MRF theory was used to provide a formulation of the jpd, p(x), that relates solution fitness, f(X = x) (or simply f(x)), to the energy U(x)...
Siddhartha K. Shakya
,
et al.
Solving the Ising Spin Glass Problem using a Bivariate EDA based on Ma...
References
(8)
A genetic algorithm framework using Haskell
(
Citations: 2
)
Deryck Brown
,
Beatriz Garmendiadoval
,
John A. w. Mccall
Published in 2000.
A functional framework for the implementation of genetic algorithms: Comparing Haskell and Standard ML
(
Citations: 3
)
Deryck F. Brown
,
A. Beatriz Garmendiadoval
,
John A. W. Mccall
Conference:
Scottish Functional Programming Workshops  SFP
, pp. 2738, 2000
Markov Random Field Models in Computer Vision
(
Citations: 46
)
Stan Z. Li
Conference:
European Conference on Computer Vision  ECCV
, pp. 361370, 1994
Equations of state calculations by fast computing machines
(
Citations: 1991
)
A. Rosenbluth
,
M. Rosenbluth
,
A. Teller
,
E. Teller
Published in 1993.
When Will a Genetic Algorithm Outperform Hill Climbing?
(
Citations: 129
)
Melanie Mitchell
,
John H. Holland
,
Stephanie Forrest
Conference:
Neural Information Processing Systems  NIPS
, pp. 64758, 1993
Sort by:
Citations
(15)
Introducing ℓ1regularized logistic regression in Markov Networks based EDAs
Malago Luigi
,
Matteucci Matteo
,
Valentini Gabriele
Published in 2011.
A fully multivariate DEUM algorithm
(
Citations: 3
)
Siddhartha Shakya
,
Alexander E. I. Brownlee
,
John A. W. McCall
,
François Fournier
,
Gilbert Owusu
Conference:
IEEE Congress on Evolutionary Computation  CEC
, pp. 479486, 2009
Optimization by estimation of distribution with DEUM framework based on Markov random fields
(
Citations: 10
)
Siddhartha Shakya
,
John McCall
Journal:
International Journal of Automation and Computing
, vol. 4, no. 3, pp. 262272, 2007
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
(
Citations: 9
)
Alexander E. I. Brownlee
,
John A. W. Mccall
,
Deryck F. Brown
Conference:
Genetic and Evolutionary Computation Conference  GECCO
, pp. 24232428, 2007
Optimisation by Estimation of Distribution with DEUM framework based on Markov Random Fields
(
Citations: 4
)
Siddhartha Shakya
,
John McCall
Published in 2007.