Algorithm Portfolios for Noisy Optimization

TL;DR

This paper introduces a portfolio approach for noisy optimization, demonstrating that a dedicated algorithm can nearly match the best solver's performance, with insights on solver comparison timing and resource distribution.

Contribution

It presents a mathematically proven portfolio algorithm tailored for noisy optimization, including a novel method for solver comparison lag and resource allocation.

Findings

Portfolio performs nearly as well as the best individual solver.

Comparing solvers with some lag improves performance.

A principled method for resource distribution among solvers.

Abstract

Noisy optimization is the optimization of objective functions corrupted by noise. A portfolio of solvers is a set of solvers equipped with an algorithm selection tool for distributing the computational power among them. Portfolios are widely and successfully used in combinatorial optimization. In this work, we study portfolios of noisy optimization solvers. We obtain mathematically proved performance (in the sense that the portfolio performs nearly as well as the best of its solvers) by an ad hoc portfolio algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag, i.e., propose the current recommendation of best solver based on their performance earlier in the run. An additional finding is a principled method for distributing the computational power among solvers in the portfolio.