An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization
This work addresses the challenge of efficiently approximating Pareto optimal sets in many-objective optimization, which is incremental as it analyzes existing algorithms rather than introducing new ones.
The paper analyzed how three elitist evolutionary algorithms perform in generating high-resolution approximations of the Pareto optimal set for many-objective optimization, defining indicators to track selection dynamics and measure solution retention and discovery under varying population sizes.
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.