Candidate visualization techniques for use with genetic algorithms
First Claim
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1. A processor-implemented method for generating a plurality of candidate visualizations, the method comprising:
- receiving, by a processor, a scenario description;
collecting a plurality of expert data using a training system based on the received scenario description, wherein the training system is a bootstrap process, wherein the training system is a cloud-based application that generates and presents hypothetical visualization scenarios to subject matter experts through a graphical user interface with which the subject matter experts interact, and wherein the plurality of expert data comprises metric data, and wherein the plurality of metric data comprises skewness of a field and kurtosis of a field;
generating at least one predictive model based on the plurality of collected expert data, wherein the training system utilizes the at least one predictive model to generate one or more subsequent candidate visualizations;
calculating a fitness score for each of a plurality of candidate visualizations by executing the at least one generated predictive model during an application of a plurality of genetic algorithms; and
generating a next generation of candidate visualizations using the plurality of genetic algorithms by mutating or cross-breeding candidate visualizations with a calculated fitness score that satisfies a preconfigured threshold value.
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Abstract
According to one embodiment, a method for generating a plurality of candidate visualizations. The method may include receiving a scenario description. The method may also include collecting a plurality of expert data using a training system based on the received scenario description. The method may further include generating at least one predictive model based on the collected plurality of expert data in order to execute the at least one generated predictive model during an application of a plurality of genetic algorithms.
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9 Claims
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1. A processor-implemented method for generating a plurality of candidate visualizations, the method comprising:
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receiving, by a processor, a scenario description; collecting a plurality of expert data using a training system based on the received scenario description, wherein the training system is a bootstrap process, wherein the training system is a cloud-based application that generates and presents hypothetical visualization scenarios to subject matter experts through a graphical user interface with which the subject matter experts interact, and wherein the plurality of expert data comprises metric data, and wherein the plurality of metric data comprises skewness of a field and kurtosis of a field; generating at least one predictive model based on the plurality of collected expert data, wherein the training system utilizes the at least one predictive model to generate one or more subsequent candidate visualizations; calculating a fitness score for each of a plurality of candidate visualizations by executing the at least one generated predictive model during an application of a plurality of genetic algorithms; and generating a next generation of candidate visualizations using the plurality of genetic algorithms by mutating or cross-breeding candidate visualizations with a calculated fitness score that satisfies a preconfigured threshold value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification