Aging, double helix and small world property in genetic algorithms

TL;DR

This paper explores the dynamics of genetic algorithms using the double helix concept to better understand their convergence and how to optimize their performance.

Contribution

It introduces a novel approach using the double helix analogy to analyze genetic algorithm aging and efficiency, providing insights into tuning parameters.

Findings

Insights into the number of generations needed for optimization

Enhanced understanding of genetic algorithm aging

Guidelines for fine-tuning genetic algorithms

Abstract

Over a quarter of century after the invention of genetic algorithms and miriads of their modifications, as well as successful implementations, we are still lacking many essential details of thorough analysis of it's inner working. One of such fundamental questions is: how many generations do we need to solve the optimization problem? This paper tries to answer this question, albeit in a fuzzy way, making use of the double helix concept. As a byproduct we gain better understanding of the ways, in which the genetic algorithm may be fine tuned.