English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Software systems continuously evolve due to new functionalities,requirements, or maintenance activities. In the context of software evolution,software refactoring has gained a strategic relevance. The space of possiblesoftware refactoring is usually very large, as it is given by the combinationsof different refactoring actions that can produce software system alternatives.Multi-objective algorithms have shown the ability to discover alternatives bypursuing different objectives simultaneously. Performance of such algorithms inthe context of software model refactoring is of paramount importance.Therefore, in this paper, we conduct a performance analysis of three geneticalgorithms to compare them in terms of performance and quality of solutions.Our results show that there are significant differences in performance amongthe algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II showsthe least memory usage).

Performance of Genetic Algorithms in the Context of Software Model Refactoring