# Adapting Multi-objectivized Software Configuration Tuning arXiv: https://arxiv.org/abs/2404.04744 ยท HTML: https://mlsanity.com/paper/2404.04744/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 59 references that are in the map. - First posted on arXiv 6 Apr 2024. ## Builds on (most cited first, 18 of 18) - [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014) - [Finding Faster Configurations using FLASH](https://mlsanity.com/paper/1801.02175/) (2018) - [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021) - [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017) - [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023) - [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021) - [FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software](https://mlsanity.com/paper/1608.08933/) (2016) - [An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems](https://mlsanity.com/paper/1606.06543/) (2016) - [Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services](https://mlsanity.com/paper/1608.05917/) (2016) - [How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance](https://mlsanity.com/paper/2002.09040/) (2020) - [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022) - [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024) - [BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction](https://mlsanity.com/paper/2008.13489/) (2020) - [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022) - [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023) - [TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling](https://mlsanity.com/paper/2011.04726/) (2020) - [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022) - [Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes](https://mlsanity.com/paper/2203.15988/) (2022) ## Built on by (most cited first, 13 of 13) - [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025) - [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025) - [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025) - [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024) - [Learning Software Bug Reports: A Systematic Literature Review](https://mlsanity.com/paper/2507.04422/) (2025) - [CoTune: Co-evolutionary Configuration Tuning](https://mlsanity.com/paper/2509.24694/) (2025) - [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025) - [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025) - [Causally Perturbed Fairness Testing](https://mlsanity.com/paper/2510.18719/) (2025) - [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026) - [When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets](https://mlsanity.com/paper/2607.16476/) (2026) - [Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems](https://mlsanity.com/paper/2408.06102/) (2024) - [LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems](https://mlsanity.com/paper/2605.08305/) (2026) ## Abstract (arXiv) When tuning software configuration for better performance (e.g., latency or throughput), an important issue that many optimizers face is the presence of local optimum traps, compounded by a highly rugged configuration landscape and expensive measurements. To mitigate these issues, a recent effort has shifted to focus on the level of optimization model (called meta multi-objectivization or MMO) instead of designing better optimizers as in traditional methods. This is done by using an auxiliary performance objective, together with the target performance objective, to help the search jump out of local optima. While effective, MMO needs a fixed weight to balance the two objectives-a parameter that has been found to be crucial as there is a large deviation of the performance between the best and the other settings. However, given the variety of configurable software systems, the "sweet spot" of the weight can vary dramatically in different cases and it is not possible to find the right setting without time-consuming trial and error. In this paper, we seek to overcome this significant shortcoming of MMO by proposing a weight adaptation method, dubbed AdMMO. Our key idea is to adaptively adjust the weight at the right time during tuning, such that a good proportion of the nondominated configurations can be maintained. Moreover, we design a partial duplicate retention mechanism to handle the issue of too many duplicate configurations without losing the rich information provided by the "good" duplicates. Experiments on several real-world systems, objectives, and budgets show that, for 71% of the cases, AdMMO is considerably superior to MMO and a wide range of state-of-the-art optimizers while achieving generally better efficiency with the best speedup between 2.2x and 20x.