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There has been a variety of crossover operators proposed for Real-CodedGenetic Algorithms (RCGAs), which recombine values from the same location inpairs of strings. In this article we present a recombination operator for RC-GAs that selects the locations randomly in both parents, and compare it tomainstream crossover operators in a set of experiments on a range of standardmultidimensional optimization problems and a clustering problem. We present twovariants of the operator, either selecting both bits uniformly at random in thestrings, or sampling the second bit from a normal distribution centered at theselected location in the first string. While the operator is biased towardsexploitation of fitness space, the random selection of the second bit for swap-ping makes it slightly less exploitation-biased. Extensive statistical analysisusing a non-parametric test shows the advantage of the new recombinationoperators on a range of test functions.

K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms