Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology

Barrios, D.; Carrascal, Alberto; Manrique, Daniel; Ríos, José

doi:10.1080/0020716022000005546

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…We pre-set some of the hyperparameters (92) and manually tuned the rest by a stratified 5fold cross-validation (Fig. 7B).…”

Section: Machine Learning Algorithm For Predicting Colonization Successmentioning

confidence: 99%

“…S7B) with binary cross-entropy loss as fitness function. We used proportional roulettewheel selection of seven parents; a morphological crossover operator (MMX) (95) and stochastic mutation to generate two offspring that deterministically replace the two worst solutions. Several isolated populations were randomly and uniformly initialized and then evolved in parallel, allowing migration at certain time points.…”

Section: Algorithm For Predicting Colonization Successmentioning

confidence: 99%

“…Several isolated populations were randomly and uniformly initialized and then evolved in parallel, allowing migration at certain time points. We preset some of the hyperparameters (95) and manually tuned the rest by a stratified fivefold cross-validation (Fig. 7B).…”

Section: Algorithm For Predicting Colonization Successmentioning

confidence: 99%

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Type III secretion system effectors form robust and flexible intracellular virulence networks

Ruano-Gallego

Sanchez‐Garrido

Kozik

et al. 2021

Science

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Infections with many Gram-negative pathogens, including Escherichia coli, Salmonella, Shigella, and Yersinia, rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses.

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“…We pre-set some of the hyperparameters (92) and manually tuned the rest by a stratified 5fold cross-validation (Fig. 7B).…”

Section: Machine Learning Algorithm For Predicting Colonization Successmentioning

confidence: 99%

Section: Algorithm For Predicting Colonization Successmentioning

confidence: 99%

See 1 more Smart Citation

Type III secretion system effectors form robust and flexible intracellular virulence networks

Ruano-Gallego

Sanchez‐Garrido

Kozik

et al. 2021

Science

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“…In this context, there is an urgent need to automate the process of determining the sequence of operators to be applied. The automation process can be performed by several methods; see, for instance, those described in [7,8,34] and, particularly, those involving evolutionary procedures, as the ones proposed in [5,[9][10][11]20,40,41].…”

Section: Introductionmentioning

confidence: 99%

A genetic programming based system for the automatic construction of image filters

Pedrino

Roda

Kato

et al. 2013

ICA

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The manual selection of linear and nonlinear operators for producing image filters is not a trivial task in practice, so new proposals that can automatically improve and speed up the process can be of great help. This paper presents a new proposal for constructing image filters using an evolutionary programming approach, which has been implemented as the IFbyGP software. IFbyGP employs a variation of the Genetic Programming algorithm (GP) and can be applied to binary and gray level image processing. A solution to an image processing problem is represented by IFbyGP as a set of morphological, convolution and logical operators. The method has a wide range of applications, encompassing pattern recognition, emulation filters, edge detection, and image segmentation. The algorithm works with a training set consisting of input images, goal images, and a basic set of instructions supplied by the user, which would be suitable for a given application. By making the choice of operators and operands involved in the process more flexible, IFbyGP searches for the most efficient operator sequence for a given image processing application. Results obtained so far are encouraging and they stress the feasibility of the proposal implemented by IFbyGP. Also, the basic language used by IFbyGP makes its solutions suitable to be directly used for hardware control, in a context of evolutionary hardware. Although the proposal implemented by IFbyGP is general enough for dealing with binary, gray level and color images, only applications using the first two are considered in this paper; as it will become clear in the text, IFbyGP aims at the direct use of induced sequences of operations by hardware devices. Several application examples discussing and comparing IFbyGP results with those obtained by other methods available in the literature are presented and discussed.

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“…Genetic programming (GP) is the most popular technique for automatic programming nowadays and may provide a better context for the automatic generation of morphological procedures [23]. GP is a branch of evolutionary computation and artificial intelligence [24][25][26], based on concepts of genetics and Darwin's principle of natural selection to genetically breed and evolve computer programs to solve problems.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

Pedrino

Saito

Roda

2011

International Journal of Reconfigurable Computing

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Mathematical morphology supplies powerful tools for low-level image analysis. Many applications in computer vision require dedicated hardware for real-time execution. The design of morphological operators for a given application is not a trivial one. Genetic programming is a branch of evolutionary computing, and it is consolidating as a promising method for applications of digital image processing. The main objective of genetic programming is to discover how computers can learn to solve problems without being programmed for that. In this paper, the development of an original reconfigurable architecture using logical, arithmetic, and morphological instructions generated automatically by a genetic programming approach is presented. The developed architecture is based on FPGAs and has among the possible applications, automatic image filtering, pattern recognition and emulation of unknown filter. Binary, gray, and color image practical applications using the developed architecture are presented and the results are compared with similar techniques found in the literature.

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Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology

Cited by 17 publications

References 8 publications

Type III secretion system effectors form robust and flexible intracellular virulence networks

Type III secretion system effectors form robust and flexible intracellular virulence networks

A genetic programming based system for the automatic construction of image filters

A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

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