**1. Introduction**

This paper investigates Evolutionary Algorithms (EAs) tuning from a multi-objective perspective. In particular, a set of experiments exemplify some of the relevant additional hints that a general multi-objective EA-tuning (Meta-EA) environment can provide, regarding the impact of EAs' parameters on EAs' performance, with respect to the single-objective EA-tuning environment of which it is a very simple extension.

Evolutionary Algorithms [1] have been very successful in solving hard, multi-modal, multi-dimensional problems in many different tasks. Nevertheless, configuring EAs is not simple and implies critical decisions that are taken based, as summarized below, on a number of factors, such as: (i) the nature of the problem(s) under consideration, (ii) the problem's constraints, such as the restrictions imposed by computation time requirements, (iii) an algorithm's ability to generalize results over different problems, and (iv) the quality indices used to assess its performance.
