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

TL;DR

This paper introduces K-Bit-Swap, a novel crossover operator for real-coded genetic algorithms that randomly selects swap locations, demonstrating improved performance over traditional operators through extensive experiments.

Contribution

The paper presents a new recombination operator for RC-GAs that uses random location selection, offering a different bias towards exploitation and showing superior results.

Findings

Outperforms mainstream crossover operators on standard benchmarks

Two variants of the operator show consistent advantages

Statistical analysis confirms significance of results

Abstract

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.