  There has been a variety of crossover operators proposed for Real-Coded
Genetic Algorithms (RCGAs), which recombine values from the same location in
pairs of strings. In this article we present a recombination operator for RC-
GAs that selects the locations randomly in both parents, and compare it to
mainstream crossover operators in a set of experiments on a range of standard
multidimensional optimization problems and a clustering problem. We present two
variants of the operator, either selecting both bits uniformly at random in the
strings, or sampling the second bit from a normal distribution centered at the
selected location in the first string. While the operator is biased towards
exploitation of fitness space, the random selection of the second bit for swap-
ping makes it slightly less exploitation-biased. Extensive statistical analysis
using a non-parametric test shows the advantage of the new recombination
operators on a range of test functions.
