# A Memetic Procedure for Global Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2201.11444 · HTML: https://mlsanity.com/paper/2201.11444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

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

- [STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization](https://mlsanity.com/paper/2610.14338/) (2026)

## Abstract (arXiv)

In this paper we consider multi-objective optimization problems over a box. The problem is very relevant and several computational approaches have been proposed in the literature. They broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm (NSMA), besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions.