# Chaotic Genetic Algorithm and The Effects of Entropy in Performance Optimization

## TL;DR

This paper introduces a Chaotic Genetic Algorithm that utilizes chaotic maps to modify initial populations and explores how entropy influences optimization performance across various benchmark functions.

## Contribution

It presents a novel approach integrating chaos theory into genetic algorithms and analyzes the impact of population entropy on optimization effectiveness.

## Key findings

- Higher entropy correlates with better optimization performance.
- Chaotic maps effectively modify initial populations for improved results.
- The method performs consistently across multiple benchmark functions.

## Abstract

This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm (GA). The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm.