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

arXiv: https://arxiv.org/abs/1409.7478 · HTML: https://mlsanity.com/paper/1409.7478/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [Multiobjective Evolutionary Component Effect on Algorithm behavior](https://mlsanity.com/paper/2308.02527/) (2023)

## Abstract (arXiv)

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.