A novel crossover operator for genetic algorithm: Stas crossover

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, in...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Decision Science Letters 2023-01, Vol.12 (3), p.515-524
Hauptverfasser: Poohoi, Ratchadakorn, Puntusavase, Kanate, Ohmori, Shunichi
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.
ISSN:1929-5804
1929-5812
DOI:10.5267/j.dsl.2023.4.010