City Research Online

Abstract

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.

Publication Type:	Article
Additional Information:	This is a pre-print of an article published in Soft Computing. The final authenticated version is available online at: https://doi.org/10.1007/s00500-016-2170-6
Publisher Keywords:	Travelling Salesman Problem, Crossover Operator, Genetic Operator, Recombination Operator, Gaussian Mutation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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