The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perfect discrimination among peaks underlying Fitness Sharing and, consequently, allows to overcome the drawbacks of the most performing sharing-based methods. This is achieved through an explicit mechanism able to discover the species in the population during the evolution. The method does not require any a priori knowledge, in that it makes no assumption about the location and the shape of the peaks, while it exploits information about the ruggedness of the fitness landscape, dynamically acquired at each generation. The proposed method has been evaluated on a set of standard functions largely adopted in the literature to assess the performance of niching methods. The experimental results show that our method has a better ability to discover and maintain all the peaks with respect to other methods proposed so far.

Speciation in evolutionary algorithms: adaptive species discovery

DELLA CIOPPA, Antonio
;
MARCELLI, Angelo;
2011-01-01

Abstract

The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perfect discrimination among peaks underlying Fitness Sharing and, consequently, allows to overcome the drawbacks of the most performing sharing-based methods. This is achieved through an explicit mechanism able to discover the species in the population during the evolution. The method does not require any a priori knowledge, in that it makes no assumption about the location and the shape of the peaks, while it exploits information about the ruggedness of the fitness landscape, dynamically acquired at each generation. The proposed method has been evaluated on a set of standard functions largely adopted in the literature to assess the performance of niching methods. The experimental results show that our method has a better ability to discover and maintain all the peaks with respect to other methods proposed so far.
2011
9781450305570
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/3029031
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 11
social impact