A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

TL;DR

This paper introduces a transfer learning framework using hierarchical multi-output Gaussian processes to enhance data-driven evolutionary optimization for dynamic, costly black-box problems, enabling efficient adaptation over time.

Contribution

It proposes a novel transfer learning approach with adaptive source selection and warm-start mechanisms for dynamic environments in expensive optimization problems.

Findings

Outperforms nine state-of-the-art algorithms on benchmarks

Effectively leverages past data for quick adaptation

Reduces computational costs in dynamic optimization

Abstract

Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture the correlation between data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization exercises. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a strictly limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm against nine state-of-the-art peer algorithms.