A more efficient selection sche in iSMS-EMOA

Adriana Menchaca-Mendez, Elizabeth Montero, María Cristina Riff, Carlos A Coello Coello

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this paper, we study iSMS-EMOA, a recently proposed approach that improves the well-known S metric selection Evolutionary Multi-Objective Algorithm (SMS-EMOA). These two indicator-based multi-objective evolutionary algorithms rely on hypervolume contributions to select individuals. Here, we propose to define a probability of using a randomly selected individual within the iSMS-EMOA’s selection scheme. In order to calibrate the value of such probability, we use the EVOCA tuner. Our preliminary results indicate that we are able to save up to 33% of computations of the contribution to hypervolume with respect to the original iSMS-EMOA, without any significant quality degradation in the solutions obtained. In fact, in some cases, the approach proposed here was even able to improve the quality of the solutions obtained by the original iSMS-EMOA.

Original languageEnglish
Pages (from-to)371-380
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8864
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Hypervolume contribution
  • Multi-objective evolutionary algorithms
  • Tuning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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