A Novel Crossover Operator for Genetic Algorithms: Ring Crossover

TL;DR

This paper introduces a new 'ring crossover' operator for genetic algorithms and demonstrates its superior performance over existing operators across various test functions.

Contribution

The paper proposes a novel ring crossover operator and evaluates its effectiveness compared to other crossover methods in genetic algorithms.

Findings

Ring crossover outperforms traditional operators on multiple test functions

Significant performance differences observed between ring crossover and existing methods

The proposed operator enhances the genetic algorithm's optimization capability

Abstract

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the "fitness" function. In that process, crossover operator plays an important role. To comprehend the GAs as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in GAs. However, how to decide what operator to use for solving a problem? A number of test functions with various levels of difficulty has been selected as a test polygon for determine the performance of crossover operators. In this paper, a novel crossover operator called 'ring crossover' is proposed. In order to evaluate the efficiency and feasibility of the proposed operator, a comparison between the results of this study and results of different crossover operators used in GAs is made through a number of test functions with various levels of difficulty. Results of this study clearly show significant differences between the proposed operator and the other crossover operators.