# Evaluating MAP-Elites on Constrained Optimization Problems

## TL;DR

This paper assesses MAP-Elites for constrained optimization, highlighting its ability to explore and visualize constraint violations, despite not outperforming specialized algorithms on all benchmarks.

## Contribution

It demonstrates the potential of MAP-Elites to explore constrained search spaces and visualize constraint-objective relationships, offering a new tool for constrained optimization analysis.

## Key findings

- MAP-Elites effectively explores constraint violations and objective trade-offs.
- It provides valuable visualization for understanding constraint interactions.
- Basic MAP-Elites underperforms compared to gradient-based algorithms on some problems.

## Abstract

Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. While some constraints are hard and as such must be satisfied with zero-tolerance, others may be soft, such that non-zero violations are acceptable. Here, we evaluate the applicability of MAP-Elites to "illuminate" constrained search spaces by mapping them into feature spaces where each feature corresponds to a different constraint. On the one hand, MAP-Elites implicitly preserves diversity, thus allowing a good exploration of the search space. On the other hand, it provides an effective visualization that facilitates a better understanding of how constraint violations correlate with the objective function. We demonstrate the feasibility of this approach on a large set of benchmark problems, in various dimensionalities, and with different algorithmic configurations. As expected, numerical results show that a basic version of MAP-Elites cannot compete on all problems (especially those with equality constraints) with state-of-the-art algorithms that use gradient information or advanced constraint handling techniques. Nevertheless, it has a higher potential at finding constraint violations vs. objectives trade-offs and providing new problem information. As such, it could be used in the future as an effective building-block for designing new constrained optimization algorithms.