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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Separate from its utility as an educational, self-learning, or teaching tool for people with diabetes and their relatives / carers, students and health-care professionals — various research groups around the world have found the freeware AIDA diabetes simulator of use as a way of generating large quantities of realistic blood glucose data for ...
Conducting regular self-management tasks such as medication and insulin intake, blood sugar checkup, diet observance, and physical exercise are really demanding. [1] This is why the use of diabetes-related apps for the purposes of recording diet and medication intake or blood glucose level is promising to improve the health condition for the patients. [2]
In 2022, SRBench announced the competition Interpretable Symbolic Regression for Data Science, which was held at the GECCO conference in Boston, MA. The competition pitted nine leading symbolic regression algorithms against each other on a novel set of data problems and considered different evaluation criteria.
A Pareto chart is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The chart is named for the Pareto principle , which, in turn, derives its name from Vilfredo Pareto , a noted Italian economist.
In statistics, the generalized Pareto distribution (GPD) is a family of continuous probability distributions. It is often used to model the tails of another distribution. It is specified by three parameters: location , scale , and shape .
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