Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

TL;DR

This paper introduces a hybrid optimization method combining Gaussian Process Regression and genetic algorithms to efficiently optimize electromagnetic device performance while accounting for uncertainty.

Contribution

It presents a novel hybrid approach that integrates surrogate modeling with multi-objective genetic algorithms for robust electromagnetic device optimization.

Findings

The hybrid method outperforms classic approaches on a dielectric waveguide benchmark.

The approach effectively balances performance and robustness under uncertainty.

It reduces computational effort compared to traditional Monte Carlo analysis.

Abstract

Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.