A more efficient selection sche in iSMS-EMOA
Por:
Menchaca-Mendez A., Montero E., Riff M.-C., Coello C.A.C.
Publicada:
1 ene 2014
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. © Springer International Publishing Switzerland 2014.
Filiaciones:
Menchaca-Mendez A.:
CINVESTAV-IPN, Departamento de Computación, Mexico, DF, Mexico
Montero E.:
Department of Computer Science, Universidad Técnica Federico Santa María, Valparaíso, Chile
Riff M.-C.:
Department of Computer Science, Universidad Técnica Federico Santa María, Valparaíso, Chile
Coello C.A.C.:
CINVESTAV-IPN, Departamento de Computación, Mexico, DF, Mexico
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