Graph based adaptive evolutionary algorithm for continuous optimization

TL;DR

This paper introduces a graph-based adaptive evolutionary algorithm designed to maintain population diversity and simplify parameter tuning in continuous optimization tasks.

Contribution

It proposes a novel evolutionary algorithm that dynamically adapts search operators using graph modeling to prevent premature convergence and reduce complexity.

Findings

Effective in maintaining diversity during optimization

Reduces parameter tuning complexity

Improves convergence performance

Abstract

he greatest weakness of evolutionary algorithms, widely used today, is the premature convergence due to the loss of population diversity over generations. To overcome this problem, several algorithms have been proposed, such as the Graph-based Evolutionary Algorithm (GEA) \cite{1} which uses graphs to model the structure of the population, but also memetic or differential evolution algorithms \cite{2,3}, or diversity-based ones \cite{4,5} have been designed. These algorithms are based on multi-populations, or often rather focus on the self-tuning parameters, however, they become complex to tune because of their high number of parameters. In this paper, our approach consists of an evolutionary algorithm that allows a dynamic adaptation of the search operators based on a graph in order to limit the loss of diversity and reduce the design complexity.