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Experiment results are provided to show the performance of the search algorithm.
This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker's preferences within the search process without impairing the performance of the search algorithm.
Thus, for scenarios admitting exact performance evaluation, the assessment shows close-to-optimality performance of the search algorithm.
The normalized mutual information (NMI) was used to assess the performance of the search algorithm.
In D-Tailor, users can manipulate mutational patterns and the selective regime two major parameters of the evolutionary design process to indirectly control sequence diversity, and consequently impact the rate of sequence evolution, as well as the overall performance of the search algorithm (see below).
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To reduce the computational cost and facilitate the good performance of the search algorithms, those intervals had to be constrained.
The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process.
We also see that the performance of the local search algorithm is only slightly worse than that of the exhaustive search.
See Section 3 for the numerical performance of the line search algorithm [21], Algorithm 4.6, and Algorithm 2.1.
Therefore, in the future, strategies to address missing values could increase the general performance of the bidirectional search algorithm.
However, a high mutation probability reduces the searching performance of the random searching algorithm and may go against retaining the better.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com