Tartarus örneklerinin zorluklarýnýn tahminlenmesi

Tartarus genetik programlamada sýkça kullanýlan bir kýyaslama problemidir. Fakat zorluk ayarý özelliði henüz tam olarak araþtýrýlmamýþtýr. Literatüre milyonlarca Tartarus örneði çalýþtýrdýðýmýz önceki bir çalýþmanýn verilerini kullanarak zorluklarýný tahmin edebilen bir denklemle katkýda bulunuyoruz. Yaklaþýmýmýz denklemin içinde yer alan dört yeni metrik kullanýyor. Bu metrikler küme sayýlarý ve büyüklüklerine, kutularýn kenarlardan uzaklýðýna, yazýlým etmeninin etrafýndaki kutularýn sayýsýna ve etmenin en büyük kümeye varmasý için gereken hareket sayýsýna baðlýdýr. Metriklerin katsayýlarý veriye genel doðrusal model ile uyarlanmýþ ve ortalama ~0.1 kadar bir hata baþarýsýna ulaþýlmýþtýr. Bu çalýþma Tartarus probleminde bir deðiþiklik yapmadan problemin zorluðunu tahmin edebilen ilk çalýþmadýr.

Estimating the difficulty of Tartarus instances

Tartarus is a commonly used benchmark problem for genetic programming. However, it has never been fully explored for its difficulty tuning property. Using the data from a previous study in which we have executed millions of Tartarus instances, we contribute to the literature with an equation to estimate their difficulty. Our approach uses four metrics that are embedded into the equation. These metrics are related to the number of clusters and clusters sizes, the distances of boxes to the edges of the board grid, the number of boxes around the agent, and the minimum number of actions for the agent to reach the largest cluster. The coefficients of these metrics have been fit to the data using the general linear model and a mean residual error of ~0.1 has been achieved. This is the first study that can estimate the difficulty of a Tartarus board without modifying the problem in any way.