An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization

TL;DR

This paper investigates how three elitist multi- and many-objective evolutionary algorithms perform in approximating the Pareto set at high resolution, analyzing their dynamics and effectiveness under various population sizes.

Contribution

It introduces new search-assessment indicators to evaluate the survival selection process and the algorithms' ability to maintain and discover optimal solutions.

Findings

Algorithms effectively balance preservation and discovery of solutions.

Population size influences the diversity and convergence behavior.

High-resolution approximation requires careful selection dynamics.

Abstract

This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.