Browsing by Subject "Comparison"

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    A Comparison of the Genetic Algorithm and the Mixing Genetic Algorithm
    (2020-07) Gulfam, Muhammad
    Genetic Algorithms (GAs) are optimization techniques inspired by the idea of evolution. They can sometimes take a long time to find the solution to a problem, but it is not always obvious when, or how to configure their various parameters. Recently, a new GA was introduced [8] that has a lot of potential for parallelization. This algorithm, called the Mixing Genetic Algorithm, has shown promising results on the well-known Traveling Salesman Problem. In this work, we have compared the effectiveness of the Mixing GA over a traditional GA on three discrete optimization problems: the OneMax problem and two topologies of the Ising Model (Ising Model on Tree and Ising Model on Ring). The comparison has been done for the success rate at the given time, for the given problem size and size of population. The comparison has been done for, both, serial and parallel implementations. Overall, the success rate for the Mixing GA is better than the traditional GA. We have also compared two population selection methods, namely, tournament selection and generational population selection. The tournament selection outperformed generational population selection for all the problems and problem sizes that we experimented with.
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    Kindergarten Program Choices: A Comparison of Full Day to Alternate Day Kindergarten Programs
    (2010) Brenner, Jill; Rauschenfels, Diane; Hyman, Randy
    Parents may have as many as three choices for which type of kindergarten program to enroll their child. These choices include all-day every-day, alternate-day, or half day kindergarten. This paper compares the reasons that parents choose one program over another. Even though parents felt that the consistency of every-day kindergarten would be beneficial, seventy percent of alternate-day parents chose that program because the cost of all-day every-day was too high. Cost was the single most deciding factor for program choice.