A more efficient selection sche in iSMS-EMOA

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

Resultado de la investigación: Contribución a una revistaArtículo

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)371-380
Número de páginas10
PublicaciónLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8864
DOI
EstadoPublicada - 1 ene 2014

Áreas temáticas de ASJC Scopus

  • Informática (todo)
  • Ciencia computacional teórica

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