# A study of gene expression programming algorithm for dynamically adjusting the parameters of genetic operators

## TL;DR

This paper introduces a new algorithm called Dynamic Gene Expression Programming (DGEP) that improves the performance of genetic algorithms by maintaining population diversity and avoiding premature convergence.

## Contribution

The novel contribution is the development of DGEP with two unique operators that dynamically adjust genetic parameters to enhance global search and diversity.

## Key findings

- DGEP outperformed standard GEP and other variants in terms of fitness outcomes and R² scores.
- DGEP achieved 15.7% better R² scores and 2.3× higher population diversity on benchmark functions.
- DGEP showed a 35% higher escape rate from local optima compared to standard GEP.

## Abstract

The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handling high-dimensional along with complex optimization problems. This research develops Dynamic Gene Expression Programming (DGEP) as an algorithm to control genetic operators dynamically thus achieving improved global search with increased population diversity. The approach operates with two unique operators which include Adaptive Regeneration Operator (DGEP-R) and Dynamically Adjusted Mutation Operator (DGEP-M) to preserve diversity while maintaining exploration-exploitation balance during evolutionary search. An extensive evaluation of DGEP occurred through symbolic regression problem tests. The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. All key metric evaluations showed that DGEP beat standard GEP along with alternative improved variants. DGEP produced the optimal results for 8 benchmark functions that produced 15.7% better R² scores along with 2.3 × larger population diversity. The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. The DGEP model serves to enhance GEP performance through the effective maintenance of diversity and improved global search functions. The results indicate that adaptive genetic methods strengthen evolutionary procedures for solving complex problems effectively.