Napredna pretraga

Pregled bibliografske jedinice broj: 1021126

Adaptive k-tournament mutation scheme for differential evolution


Bajer, Dražen
Adaptive k-tournament mutation scheme for differential evolution // Applied soft computing, 85C (2019), 105776, 27 doi:10.1016/j.asoc.2019.105776 (međunarodna recenzija, članak, znanstveni)


Naslov
Adaptive k-tournament mutation scheme for differential evolution

Autori
Bajer, Dražen

Izvornik
Applied soft computing (1568-4946) 85C (2019); 105776, 27

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Base vector selection ; Differential evolution ; Exploration and exploitation ; Mutation ; Tournament selection

Sažetak
Mutation in differential evolution (DE) is of considerable importance for the performance of the algorithm. It directly impacts exploration and exploitation. Thus, it represents the driving force for discovering unvisited regions of the search space, whilst also enabling the utilisation of promising points in that space. Since mutation performs search around the base vector, its selection plays a prominent role in directing it. In that regard, a low selection pressure contributes to exploration, whereas a high selection pressure contributes to exploitation. However, a balance between the two is paramount for high and consistent performance. This paper proposes a novel mutation scheme that employs k-tournament selection for choosing the base vector. Each population member is associated with a tournament size that is adapted during the search process with the aim of controlling exploration and exploitation. The mechanism mixes adaptation on an individual and population level. Results of the experimental analysis conducted on a wide range of numerical benchmark problem instances affirm its competitive performance and the benefits of the adaptation of tournament sizes, suggesting it to be a viable measure for increasing DE algorithm performance. Finally, the automatic design of radial basis function networks for classification was tackled. The proposed mutation scheme proved to be effective when dealing with that task as the canonical algorithm incorporating it yielded better fit models than competing approaches.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Autor s matičnim brojem:
Dražen Bajer, (329691)

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati