Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing

Tóth, János; Tomán, Henrietta; Hajdú, András

doi:10.1016/j.patcog.2020.107510

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…In conclusion, the paper method significantly improves the denoised effect for CT image of advanced COVID-19 with non-symptom to a certain extent by different noisy density. Aiming at the different kinds of COVID-19 CT imagesadaptive hybrid genetic combined with exponential simulated annealing [41] (AHG+ESA)orthogonal adaptive genetic [42] combined with rapid simulated annealing [43] (OAG+RSA)traditional adaptive genetic [44] combined with exponential simulated annealing (TAG+ESA)adaptive hybrid genetic combined with doppler effect simulated annealing (AHG+DESA, paper method) are used for simulation comparison tests of denoising parameters optimization. The fitness evolution curve for different COVID-19 CT images are shown in Fig.14 .…”

Section: Resultsmentioning

confidence: 99%

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

Guo

Guo-wei

Han

et al. 2022

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

Guo

Guo-wei

Han

et al. 2022

Biomedical Signal Processing and Control

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“…As well as these advances in the machine learning area, which link well into meta-heuristic approaches, there is also scope for research into the combination of meta-heuristics with different optimisation algorithm approaches, leading to an overall more effective algorithm. In terms of algorithms research, the success of ensemble methods within the machine learning area (Abuassba et al, 2021;Shiue et al, 2021) indicates the potential of combining algorithms and models, with their diverse strengths and weaknesses, in optimisation applications (Han et al, 2020;Tóth et al, 2020;Ye et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers

2022

Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies

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Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

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“…Resolution of these problems was however made difficult by the stochastic nature of the simulation outputs. To that end, we adapted a simulated annealing algorithm to the noisy cost function case as described in [8] and [20]. We designed a cost function taking as input a tuple of β Kon and β Kof f values, which executes the simulation of a single gene with those β values for 20 hours and returns the Kantorovich distance between the simulated data (at t=20h) and the initial data (at t=0h).…”

Section: /22mentioning

confidence: 99%

TopoDoE: A Design of Experiment strategy for selection and refinement in ensembles of executable Gene Regulatory Networks

Bouvier

Zreika

Vallin

et al. 2023

Preprint

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Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions.

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Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing

Cited by 15 publications

References 23 publications

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

TopoDoE: A Design of Experiment strategy for selection and refinement in ensembles of executable Gene Regulatory Networks

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