In the past decades, different evolutionary optimization methodologies have been proposed by scholars and exploited by practitioners, in a wide range of applications. Each paradigm shows distinctive features, typical advantages, and characteristic disadvantages; however, one single problem is shared by almost all of them: the “lack of speciation”. While natural selection favors variations toward greater divergence, in artificial evolution candidate solutions do homologize. Many authors argued that promoting diversity would be beneficial in evolutionary optimization processes, and that it could help avoiding premature convergence on sub-optimal solutions. The paper surveys the research in this area up to mid 2010s, it re-orders and re-interprets different methodologies into a single framework, and proposes a novel three-axis taxonomy. Its goal is to provide the reader with a unifying view of the many contributions in this important corpus, allowing comparisons and informed choices. Characteristics of the different techniques are discussed, and similarities are highlighted; practical ways to measure and promote diversity are also suggested.

Divergence of character and premature convergence: A survey of methodologies for promoting diversity in evolutionary optimization / Squillero, Giovanni; Tonda, ALBERTO PAOLO. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - STAMPA. - 329:(2016), pp. 782-799. [10.1016/j.ins.2015.09.056]

Divergence of character and premature convergence: A survey of methodologies for promoting diversity in evolutionary optimization

SQUILLERO, Giovanni;
2016

Abstract

In the past decades, different evolutionary optimization methodologies have been proposed by scholars and exploited by practitioners, in a wide range of applications. Each paradigm shows distinctive features, typical advantages, and characteristic disadvantages; however, one single problem is shared by almost all of them: the “lack of speciation”. While natural selection favors variations toward greater divergence, in artificial evolution candidate solutions do homologize. Many authors argued that promoting diversity would be beneficial in evolutionary optimization processes, and that it could help avoiding premature convergence on sub-optimal solutions. The paper surveys the research in this area up to mid 2010s, it re-orders and re-interprets different methodologies into a single framework, and proposes a novel three-axis taxonomy. Its goal is to provide the reader with a unifying view of the many contributions in this important corpus, allowing comparisons and informed choices. Characteristics of the different techniques are discussed, and similarities are highlighted; practical ways to measure and promote diversity are also suggested.
File in questo prodotto:
File Dimensione Formato  
diversity-draft.pdf

accesso aperto

Descrizione: Post-print draft
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Creative commons
Dimensione 611.72 kB
Formato Adobe PDF
611.72 kB Adobe PDF Visualizza/Apri
1-s2.0-S002002551500729X-main.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.73 MB
Formato Adobe PDF
1.73 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

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/11583/2622368
 Attenzione

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