Soft Computing

, Volume 21, Issue 20, pp 6133–6142 | Cite as

K-Bit-Swap: a new operator for real-coded evolutionary algorithms

  • Aram Ter-Sarkisov
  • Stephen Marsland
Methodologies and Application


There have been a variety of crossover operators proposed for real-coded genetic algorithms (RCGAs). Such operators recombine values from pairs of strings to generate new solutions. In this article, we present a recombination operator for RCGAs that selects the string locations for change separately randomly in the parent and offspring, enabling solution parts to move within a string, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a real-world clustering problem. We present two variants of the operator, either selecting bits uniformly at random in both strings or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased toward exploitation of fitness space, the random selection of the second bit for swapping reduces this bias slightly. Statistical analysis of the experimental results using a nonparametric test shows the advantage of the new recombination operators on our test optimization functions.


Travelling Salesman Problem Crossover Operator Genetic Operator Recombination Operator Gaussian Mutation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of ComputingDublin Institute of TechnologyDublinIreland
  2. 2.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

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