Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE
Kishan Kumar Ganguly, Tim Menzies

TL;DR
This study demonstrates that focused regional search ('zoom') significantly outperforms traditional Pareto and global optimization methods in budget-constrained SBSE, offering faster, more interpretable solutions.
Contribution
The paper introduces EZR, a greedy zoom method that surpasses Pareto-based approaches in efficiency and effectiveness across numerous SE optimization tasks.
Findings
EZR runs three orders of magnitude faster than Pareto and Bayesian methods.
EZR achieves higher statistical ranks and wins or ties in 84-89% of datasets at equal budgets.
Pareto-optimal solutions are concentrated in a small, tight region, making zooming more effective.
Abstract
Traditional Search-Based Software Engineering (SBSE) assumes global search and full Pareto exploration are essential. We offer the following negative result based on a study of over 100 Software Engineering (SE) optimization tasks: "zooming" into promising regions is far more effective than Pareto and global exploration under constrained evaluation budgets. Our minimal greedy zoom method, EZR, runs three orders of magnitude faster than Pareto and global Bayesian methods, achieving higher statistical ranks and winning or tying in 84-89\% of datasets on equal budget. Even at one-fifth the evaluation budget, EZR wins or ties in 79-81\% of datasets. Surprisingly, despite never explicitly seeking a frontier, EZR matches or outperforms Pareto methods on their own coverage metrics (IGD, HV) at equal budgets. The explanation for this widespread failure is structural: across the datasets…
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