Could it be that you mean "permutation" instead of "randomization"?

If so, then think that bootstrap is a stochastic approximation of the permutation.

Luis Orlando Duarte don't want to say "randomization", but randomization test or permutation test. Most common resampling methods are Bootstrapping or Monte Carlo Estimation(which approximate to it) and 'Jackknife'. The main practical difference for researchers is that 'the bootstrap' gives different results when repeated on the same datapoints, whereas 'the jackknife' gives exactly the same result each time! The jackknife, originally used for bias reduction in surveys, is more of a specialized method and only estimates the variance of the point estimator.

Randomization tests use a large number of random permutations of given data to determine the probability that the actual empirical test result might have occurred by chance.

Bootstrapping generally refers to statistical approach to quantifying uncertainty by re-using the data, specifically random resampling with replacement. This is useful particularly in cases where you’d like to extract a statistic or apply some computational procedure to your data, and the sampling distribution of that statistic is not available in closed form (e.g., for frequentist error bars). The bootstrap allows us to ask: what is the range of values we expect for this statistic given the degree of variation in our dataset?

Permutation-based analyses resemble the bootstrap in that they rely on randomizations of the observed data. The primary difference is that while bootstrap analyses typically seek to quantify the sampling distribution of some statistic computed from the data, permutation analyses typically seek to quantify the null distribution. That is, they seek to break whatever structure might be preset in a dataset, and quantify the kinds of patterns one expects to see “purely by chance.”