Takes two bit strings and creates a new one of the same size by selecting the items from the first string or
the second, based on a given rate (the probability of choosing an element from the first string).

crossover

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

crossover: crossover

Description

Arguments

Value