Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization

TL;DR

This paper investigates the balance between diversity and quality in black-box optimization, revealing that simple random sampling often outperforms complex heuristics in generating diverse, high-quality solutions.

Contribution

It provides a novel analysis of the diversity-quality trade-off using existing algorithms, highlighting the need for specialized methods to improve results.

Findings

Naive uniform random sampling is a strong baseline.

Existing heuristics rarely outperform random sampling.

The trade-off depends on problem properties.

Abstract

In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether they have been specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to understand the capability of off-the-shelf algorithms to quantify the trade-off between the minimum pairwise distance within batches of solutions and their average quality. We also analyze how this trade-off depends on the properties of the underlying optimization problem. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality.