A Study on the Global Convergence Time Complexity of Estimation of Distribution Algorithms

TL;DR

This paper analyzes the convergence time of Estimation of Distribution Algorithms, providing bounds and exact iteration counts for their global convergence to optimal solutions.

Contribution

It offers the first theoretical analysis of the convergence time complexity of EDAs, including upper bounds and exact iteration numbers.

Findings

Derived upper bounds on EDA convergence time

Calculated exact number of iterations for convergence

Provides theoretical insights into EDA efficiency

Abstract

The Estimation of Distribution Algorithm is a new class of population based search methods in that a probabilistic model of individuals is estimated based on the high quality individuals and used to generate the new individuals. In this paper we compute 1) some upper bounds on the number of iterations required for global convergence of EDA 2) the exact number of iterations needed for EDA to converge to global optima.