Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization

TL;DR

ATM is a new robust optimization method for discrete black-box problems that adaptively balances rank-based and model-based approaches to improve performance in noisy, high-dimensional, and unordered factor settings.

Contribution

The paper introduces ATM, a novel method that combines rank- and model-based optimization using marginal tail means, enhancing robustness in discrete black-box optimization.

Findings

ATM outperforms existing methods in simulations.

ATM effectively handles noisy and high-dimensional problems.

Demonstrated success on real-world engineering applications.

Abstract

We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for effective robust optimization of discrete black-box problems. ATM has important applications to many real-world engineering problems (e.g., manufacturing optimization, product design, molecular engineering), where the objective to optimize is black-box and expensive, and the design space is inherently discrete. One weakness of existing methods is that they are not robust: these methods perform well under certain assumptions, but yield poor results when such assumptions (which are difficult to verify in black-box problems) are violated. ATM addresses this via the use of marginal tail means for optimization, which combines both rank-based and model-based methods. The trade-off between rank- and model-based optimization is tuned by first identifying important main effects and interactions, then finding a good compromise which best exploits additive structure. By adaptively tuning this trade-off from data, ATM provides improved robust optimization over existing methods, particularly in problems with (i) a large number of factors, (ii) unordered factors, or (iii) experimental noise. We demonstrate the effectiveness of ATM in simulations and in two real-world engineering problems: the first on robust parameter design of a circular piston, and the second on product family design of a thermistor network.