K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms
This is an incremental improvement for researchers and practitioners in evolutionary computation, offering a slightly less exploitation-biased operator for real-coded optimization.
The authors tackled the problem of recombination in Real-Coded Genetic Algorithms by introducing K-Bit-Swap, a new operator that randomly selects swap locations in parents, and found it advantageous over mainstream operators in experiments on optimization and clustering problems, with extensive statistical analysis showing its benefits.
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.