Improving Multi-Objective Evolutionary Algorithms Using Grammatical Evolution

35 Pages Posted: 3 Mar 2023

See all articles by Amín Vanya Bernabé Rodríguez

Amín Vanya Bernabé Rodríguez

affiliation not provided to SSRN

Braulio Israel Alejo-Cerezo

affiliation not provided to SSRN

Carlos Artemio Coello Coello

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN)

Abstract

Multi-objective evolutionary algorithms (MOEAs) have become an effective choice to solve multi-objective optimization problems (MOPs). However, it is well known that Pareto dominance-based MOEAs struggle in MOPs with four or more objective functions due to a lack of selection pressure in high dimensional spaces. The main choices for dealing with such problems are decomposition-based and indicator-based MOEAs. In this work, we propose the use of Grammatical Evolution, an evolutionary computation search technique, to generate functions that can improve decomposition-based and indicator-based MOEAs. Namely, we propose a methodology to generate new scalarizing functions, which are known to have a great impact in the performance of decomposition-based MOEAs and in some indicator-based MOEAs. Additionally, we propose two methodologies to generate hypervolume approximations, since the hypervolume is a popular performance indicator used not only in indicator-based MOEAs but also to assess performance of MOEAs. 

    Using these methodologies, we generate two new scalarizing functions and provide their corresponding experimental validation to show that they outperform the well-known scalarizing function called ASF in a variety of test problems. We also propose two different schemes for producing hypervolume approximations and compare them with respect to the Monte Carlo method.

Keywords: Grammatical evolution, genetic programming, evolutionary algorithms, Multi-objective optimization

Suggested Citation

Bernabé Rodríguez, Amín Vanya and Alejo-Cerezo, Braulio Israel and Coello Coello, Carlos Artemio, Improving Multi-Objective Evolutionary Algorithms Using Grammatical Evolution. Available at SSRN: https://ssrn.com/abstract=4377537 or http://dx.doi.org/10.2139/ssrn.4377537

Amín Vanya Bernabé Rodríguez (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Braulio Israel Alejo-Cerezo

affiliation not provided to SSRN ( email )

No Address Available

Carlos Artemio Coello Coello

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN) ( email )

07360 Mexico, D.F.
Mexico

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
17
Abstract Views
88
PlumX Metrics