Surrogate-Assisted Tuning for Gaussian Process with Qualitative and Quantitative Factors

Abstract

This thesis mainly focuses on surrogate-assisted tuning procedures for the different types of the variables and responses Basically the surrogate-assisted approach iterates the following two steps until a stop criterion is met First based on the current explored points a surrogate surface is constructed and then due to the surrogate model an infill criterion is adopted to identify the next explored points Here in addition to quantitative variables the qualitative variables are also considered and the responses can be deterministic or contain noise Firstly we study the performance of the heat dissipation fins in electronic components The response is deterministic Since qualitative variables are considered in the study the Gaussian process model with qualitative and quantitative factors (QQGP) is adopted for surrogate construction and a maximum expected improvement criterion is used to identify the next explored points Due to the cost limitation the proposed surrogate-assisted approach does find out the configuration with better heat dissipation effect of the heat dissipation fins in electronic components Secondly we consider the parameter tuning in CNN Since the response involves the random effects the treed Gaussian process (tGP) with an expected improvement criterion is adopted here for the surrogate-assisted tuning procedure Due to the numerical experiments the proposed surrogate-assisted tuning procedure can identify the parameters with better performance

Date of Award	2020
Original language	English
Supervisor	Ray-Bing Chen (Supervisor)