Evolutionary algorithms (EAs) are a family of optimization algorithms inspired by the Darwinian theory of evolution, and Genetic Algorithm (GA) is a popular technique among EAs. Similar to other EAs, common limitations of GAs have geometrical origins, like premature convergence, where the final population’s convex hull might not include the global optimum. Population diversity maintenance is a central idea to tackle this problem but is often performed through methods that constantly diminish the search space’s area. This work presents a self- adaptive approach, where the non-geometric crossover is strategically employed with geometric crossover to maintain diversity from a geometrical/topological perspective. To evaluate the performance of the proposed method, the experimental phase compares it against well-known diversity maintenance methods over well-known benchmarks. Experimental results clearly demonstrate the suitability of the proposed self-adaptive approach and the possibility of applying it to different types of crossover and EAs.

A Self-Adaptive Approach to Exploit Topological Properties of Different GAs’ Crossover Operators

Manzoni L.;Pietropolli G.
2023-01-01

Abstract

Evolutionary algorithms (EAs) are a family of optimization algorithms inspired by the Darwinian theory of evolution, and Genetic Algorithm (GA) is a popular technique among EAs. Similar to other EAs, common limitations of GAs have geometrical origins, like premature convergence, where the final population’s convex hull might not include the global optimum. Population diversity maintenance is a central idea to tackle this problem but is often performed through methods that constantly diminish the search space’s area. This work presents a self- adaptive approach, where the non-geometric crossover is strategically employed with geometric crossover to maintain diversity from a geometrical/topological perspective. To evaluate the performance of the proposed method, the experimental phase compares it against well-known diversity maintenance methods over well-known benchmarks. Experimental results clearly demonstrate the suitability of the proposed self-adaptive approach and the possibility of applying it to different types of crossover and EAs.
2023
978-3-031-29572-0
978-3-031-29573-7
https://link.springer.com/chapter/10.1007/978-3-031-29573-7_1
File in questo prodotto:
File Dimensione Formato  
Genetic Programming - (Lecture Notes in Computer Science, 13986) Gisele Pappa, Mario Giacobini, Zdenek Vasicek - Genetic Programming_ 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Rep.pdf

Accesso chiuso

Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 2.4 MB
Formato Adobe PDF
2.4 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Genetic+Programming+-+(Lecture+Notes+in+Computer+Science,+13986)+Gisele+Pappa,+Mario+Giacobini,+Zdenek+Vasicek+-+Genetic+Programming_+26th+European+Conference,+EuroGP+2023,+Held+as+Part+of+EvoStar+2023,+Brno,+Czech-Post_print.pdf

Open Access dal 30/03/2024

Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 2.85 MB
Formato Adobe PDF
2.85 MB Adobe PDF Visualizza/Apri
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/11368/3046333
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact