Modified Soft Brood Crossover in Genetic Programming

TL;DR

This paper proposes a modified soft brood crossover operator for genetic programming to enhance population diversity and mitigate premature convergence, validated through experiments on symbolic regression problems.

Contribution

It introduces a novel modification to the soft brood crossover operator, improving genetic programming performance by reducing premature convergence.

Findings

The modified operator outperforms existing crossover methods.

Enhanced population diversity observed in experiments.

Improved accuracy in symbolic regression tasks.

Abstract

Premature convergence is one of the important issues while using Genetic Programming for data modeling. It can be avoided by improving population diversity. Intelligent genetic operators can help to improve the population diversity. Crossover is an important operator in Genetic Programming. So, we have analyzed number of intelligent crossover operators and proposed an algorithm with the modification of soft brood crossover operator. It will help to improve the population diversity and reduce the premature convergence. We have performed experiments on three different symbolic regression problems. Then we made the performance comparison of our proposed crossover (Modified Soft Brood Crossover) with the existing soft brood crossover and subtree crossover operators.