Convergence Properties of Two ({\mu} + {\lambda}) Evolutionary Algorithms On OneMax and Royal Roads Test Functions

TL;DR

This paper analyzes the convergence times of two elitist population-based evolutionary algorithms, including a recombination operator and a local search method, on the OneMax and Royal Roads test functions, focusing on the impact of population parameters.

Contribution

It provides new bounds on convergence time for these algorithms and examines how population size and elite distribution influence their performance.

Findings

Bounds on convergence time for the algorithms.

Impact of population size on convergence.

Effect of elite distribution on algorithm efficiency.

Abstract

We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.