Next Article in Journal
Regular Ordered Ternary Semigroups in Terms of Bipolar Fuzzy Ideals
Previous Article in Journal
Switching Point Solution of Second-Order Fuzzy Differential Equations Using Differential Transformation Method
Previous Article in Special Issue
Dynamic Horizontal Union Algorithm for Multiple Interval Concept Lattices
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Mathematics 2019, 7(3), 232; https://doi.org/10.3390/math7030232

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

1
Camlin Italy, via Budellungo 2, 43123 Parma, Italy
2
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124 Parma, Italy
*
Author to whom correspondence should be addressed.
Received: 7 January 2019 / Revised: 21 February 2019 / Accepted: 28 February 2019 / Published: 4 March 2019
(This article belongs to the Special Issue Evolutionary Algorithms in Intelligent Systems)
Full-Text   |   PDF [2493 KB, uploaded 4 March 2019]   |  

Abstract

Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones. View Full-Text
Keywords: evolutionary algorithms; multi-objective optimization; parameter puning; parameter analysis; particle swarm optimization; differential evolution; global continuous optimization evolutionary algorithms; multi-objective optimization; parameter puning; parameter analysis; particle swarm optimization; differential evolution; global continuous optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Ugolotti, R.; Sani, L.; Cagnoni, S. What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains? Mathematics 2019, 7, 232.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Mathematics EISSN 2227-7390 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top