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Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms
2015
Mathematical Problems in Engineering
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The new evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. ...
Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal ...
Gaussian Mixture Modelling In this section, the modeling of image histograms through Gaussian mixture models is presented. ...
doi:10.1155/2015/805357
fatcat:qb5zask36fhslbrq6trakdd2za
Determining the optimal number of clusters using a new evolutionary algorithm
2005
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)
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Empirical evaluations using the synthetic dataset and an existing benchmark show that the proposed evolutionary algorithm can exactly estimate the optimal number of clusters for a set of data. ...
An improper pre-selection for the number of clusters might easily lead to bad clustering outcome. In this paper, we propose a new evolutionary algorithm to address this issue. ...
Another popular clustering approach sensitive to this problem is based on Gaussian mixture model (GMM). ...
doi:10.1109/ictai.2005.57
dblp:conf/ictai/LuT05
fatcat:bp3iywu6mbcxpcpheztlfoa7by
Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model
[chapter]
2004
Lecture Notes in Computer Science
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In evolutionary continuous optimization by building and using probabilistic models, the multivariate Gaussian distribution and their variants or extensions such as the mixture of Gaussians have been used ...
In this paper, we propose a new continuous estimation of distribution algorithms (EDAs) with the variational Bayesian independent component analyzers mixture model (vbICA-MM) for allowing any distribution ...
Acknowledgments This research was supported by the Ministry of Commerce, Industry and Energy through the MEC project, the Ministry of Science and Technology through National Research Lab (NRL), and the ...
doi:10.1007/978-3-540-30217-9_22
fatcat:cmui6p66unfsjjsil3lvbzaahe
E-Means: An Evolutionary Clustering Algorithm
[chapter]
2008
Lecture Notes in Computer Science
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E-means is an Evolutionary extension of k-means algorithm that is composed by a revised k-means algorithm and an evolutionary approach to Gaussian mixture model, which estimates automatically the number ...
In this paper we propose a new evolutionary clustering algorithm named E-means. ...
A popular clustering approach sensitive to this problem is based on Gaussian mixture model (GMM). ...
doi:10.1007/978-3-540-92137-0_59
fatcat:wdoiyhckajcypfc7ltkyib2duy
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering
[article]
2020
arXiv
pre-print
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Gaussian mixture model. ...
An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. ...
for a Gaussian mixture model. ...
arXiv:1811.00097v2
fatcat:dwaxgig4ybbppeucu2hubrfn6m
Estimation of distribution algorithms
2011
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11
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GACR 102/02/0132 entitled "Use of Genetic Principles in Evolutionary Algorithms", and was supervised by Jiří Lažanský. ...
Acknowledgements The project was supported by the Grant agency of the Czech Rep. with the grant No. ...
Figure 2 : 2 Two Peaks function -Evolution of bin boundaries for equi-height and max-diff histogram models and evolution of component centers for mixture of Gaussians model. ...
doi:10.1145/2001858.2002067
dblp:conf/gecco/Santana11
fatcat:jsiyhfqz5jeo7bjdexafgi4swu
Research on Evolutionary Level Set Method and Gaussian Mixture Model based Target Shape Design Optimization Problem
2019
IEEE Access
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INDEX TERMS Target shape design optimization, level set method, Gaussian mixture model, evolutionary algorithms. ...
Therefore, in this paper, we first propose a level-set method integrated with a Gaussian mixture model (GMMLSM) as a shape representation method to overcome the fixed topology and loop problem in the existing ...
mixture Gaussian functions, namely Gaussian mixture model level set method (GMMLSM). ...
doi:10.1109/access.2019.2928686
fatcat:amongqdwfbgp7mffmwd56nvtiy
Paired comparison-based Interactive Differential Evolution
2009
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)
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Evolutionary Algorithms; Differential Evolution; Interactive Evolutionary Computation, Paired Comparison, Gaussian Mixture Model I. ...
We propose a system of Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms ...
the 3-D Gaussian Mixture Model. ...
doi:10.1109/nabic.2009.5393359
dblp:conf/nabic/TakagiP09
fatcat:prxs5qubnrat3lvwlyalchtzba
A review on probabilistic graphical models in evolutionary computation
2012
Journal of Heuristics
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Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these ...
This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. ...
Network
Mixture of hyperplanes
Mixture of Gaussian Kernels
Marginal Product Model
Mixture of Gaussian Markov
Networks
Hierarchical Dependency Tree
Gaussian Bayesian Network
•
ernels ...
doi:10.1007/s10732-012-9208-4
fatcat:54ipbzsryfbt5nqmaczgurb2he
Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures
2020
Scientific Reports
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However, although fitting of Gaussian mixture models (GMM) is often aimed at obtaining the separate modes composing the mixture, current technical implementations, often using the Expectation Maximization ...
Gaussian mixtures play an important role in the multimodal distribution of one-dimensional data. ...
The funders had no role in the decision to publish or in the preparation of the manuscript. ...
doi:10.1038/s41598-020-57432-w
pmid:31959878
pmcid:PMC6971287
fatcat:vjfngldumrb5fmt3lx5s273o3a
NichingEDA: Utilizing the diversity inside a population of EDAs for continuous optimization
2008
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
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Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. ...
Gaussian mixture model based EDAs have been developed to remedy this disadvantage of single Gaussian based EDAs. ...
This work is supported by a grant from the Chinese Academy of Sciences for Outstanding Young Scholars from Overseas (No. 2F03B01) and an EPSRC grant (EP/C520696/1). ...
doi:10.1109/cec.2008.4630958
dblp:conf/cec/DongY08
fatcat:cosvcnxiwralbhiaajr6nhrhga
Clonal Selection Algorithm for Gaussian Mixture Model Based Segmentation of 3D Brain MR Images
[chapter]
2012
Lecture Notes in Computer Science
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Among them, Gaussian Mixture Model (GMM) based segmentation is one of the most commonly used techniques. • In this study, we incorporate the prior anatomical information embedded in the probabilistic brain ...
IEEE Transactions on Evolutionary Computation 6, (2002)
[2] Tohka, J., Krestyannikov, E., Dinov, I.D., Graham, A.M., Shattuck, D.W.,
Ruotsalainen, U., Toga, A.W.: Genetic Algorithms for Finite Mixture ...
doi:10.1007/978-3-642-31919-8_38
fatcat:z6rqabooc5e4rbdilz3cqs3n7a
Estimation of Distribution Algorithms
[chapter]
2016
Search and Optimization by Metaheuristics
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GACR 102/02/0132 entitled "Use of Genetic Principles in Evolutionary Algorithms", and was supervised by Jiří Lažanský. ...
Acknowledgements The project was supported by the Grant agency of the Czech Rep. with the grant No. ...
Figure 2 : 2 Two Peaks function -Evolution of bin boundaries for equi-height and max-diff histogram models and evolution of component centers for mixture of Gaussians model. ...
doi:10.1007/978-3-319-41192-7_7
fatcat:dk3lax3axfctjd7ehyayxia4xu
Estimation of Distribution Algorithms
[chapter]
2015
Springer Handbook of Computational Intelligence
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GACR 102/02/0132 entitled "Use of Genetic Principles in Evolutionary Algorithms", and was supervised by Jiří Lažanský. ...
Acknowledgements The project was supported by the Grant agency of the Czech Rep. with the grant No. ...
Figure 2 : 2 Two Peaks function -Evolution of bin boundaries for equi-height and max-diff histogram models and evolution of component centers for mixture of Gaussians model. ...
doi:10.1007/978-3-662-43505-2_45
fatcat:wdnzqegmcnfirc3hkaw62gjwva
Gene regulatory network reverse engineering using population based incremental learning and K-means
2012
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12
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Finding interactions among genes is one of the main problems in molecular biology. In this paper, we use a novel approach to model the gene's regulations, or Gene Regulatory Networks (GRNs). ...
We use a Recursive Neural Network (RNN) to model the networks, and then use Population Based Incremental Learning (PBIL) enhanced with K-means to find the optimum parameters of the Neural Network. ...
We model the candidates as a mixture of K Gaussian distributions, to have at a set of N ×K clusters modeling the best candidates of the problem for each of the N dimensions. ...
doi:10.1145/2330784.2330965
dblp:conf/gecco/PalafoxH12
fatcat:uragqv7iqbhnfhmb4we24fuvje
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