Kernel hautapen dinamikoa Optimizazio Bayesiarrean

Abstract

In Bayesian Optimization, when using a Gaussian Process prior, some kernels adapt better than others to the objective function. This research evaluates the possibility of dynamically changing the kernel function based on the probability of improvement. Five kernel selection strategies are proposed and tested in well known synthetic functions. According to our preliminary experiments, these methods can improve the e ciency of the search when the best kernel for the problem is unknown.

Optimizazio Bayesiarra Prozesu Gaussiarren bitartez egiten denean, kernel batzuk beste batzuk baino hobeto egokitzen dira helburu-funtziora. Lan honetan, kernel hauek dinamikoki aldatzeko aukera aztertu dugu, hobekuntza-probabilitatean oinarriturik. Kernelen hautaketa aurrera eramateko bost irizpide aurkeztu eta helburu-funtzio ezagunen bidez ebaluatu ditugu. Lortutako emaitzen arabera, irizpide hauek algoritmoaren errendimendua hobetzen dute kernel egokiena aurretiaz ezezaguna denean.