Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

Monteagudo, Ángel; Santos, José

doi:10.1145/2739480.2754640

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In this study, we extend the approach to obtain blending and sampling operators that optimize joint migration inversion (JMI) results. Owing to its ability to manage optimization problems with non-convexity, non-differentiability, the existence of many local minima and large problem space, GAs have successfully been implemented in various application domains (Monteagudo and Santos 2015;Perez-Liebana et al 2015;Bak, Rask and Risi 2016;Davies et al 2016;Scirea et al 2016). However, a standard GA inherently and inevitably needs to evaluate all the solutions to obtain their objective-function values.…”

Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 99%

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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Blended acquisition along with efficient spatial sampling is capable of providing high‐quality seismic data in a cost‐effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio‐inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.

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Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 99%

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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“…Over several decades, the original definition of GAs has gradually evolved, and the technology has been widely adapted to a variety of optimization problems. Numerous successful applications of GAs are easily recognizable in different domains such as biomedicine (Monteagudo and Reyes 2015), arts (Davies et al 2016), architecture (Bak, Rask and Risi 2016), music (Scirea et al 2016), games (Liebana et al 2015) and recently machine learning (Kramer 2016).…”

Section: S U R V E Y D E S I G N W I T H G E N E T I C a L G O R I T mentioning

confidence: 99%

Blended‐acquisition design of irregular geometries towards faster, cheaper, safer and better seismic surveying

Nakayama

Blacquière

Ishiyama

et al. 2018

Geophysical Prospecting

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The application of blended acquisition has drawn considerable attention owing to its ability to improve the operational efficiency as well as the data quality and health, safety and environment performance. Furthermore, the acquisition of less data contributes to the business aspect, while the desired data density is still realizable via subsequent data reconstruction. The use of fewer detectors and sources also minimizes operational risks in the field. Therefore, a combined implementation of these technologies potentially enhances the value of a seismic survey further. One way to encourage this is to minimize any imperfection in deblending and data reconstruction during processing. In addition, one may derive survey parameters that enable a further improvement in these processes as introduced in this study. The proposed survey design workflow iteratively performs the following steps to derive the survey parameters responsible for source blending as well as the spatial sampling of detectors and sources. The first step is the application of blending and sampling operators to unblended and well‐sampled data. We then apply closed‐loop deblending and data reconstruction. The residue for a given design from this step is evaluated and subsequently used by genetic algorithms to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into the next iteration until they satisfy the given termination criteria. We also propose a repeated encoding sequence to form a parameter sequence in genetic algorithms, making the size of problem space manageable. The results of the proposed workflow are outlined using blended dispersed source array data incorporating different scenarios that represent acquisition in marine, transition zone and land environments. Clear differences attributed solely to the parameter design are easily recognizable. Additionally, a comparison among different optimization schemes illustrates the ability of genetic algorithms along with a repeated encoding sequence to find better solutions within a computationally affordable time. The optimized parameters yield a notable enhancement in the deblending and data reconstruction quality and consequently provide optimal acquisition scenarios.

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An Approach for Team Composition in League of Legends using Genetic Algorithm

Costa

Souza

2019

2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

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Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

Cited by 3 publications

References 30 publications

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Blended‐acquisition design of irregular geometries towards faster, cheaper, safer and better seismic surveying

An Approach for Team Composition in League of Legends using Genetic Algorithm

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