{"id":16392,"date":"2026-01-28T16:22:29","date_gmt":"2026-01-28T15:22:29","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=events&p=16392"},"modified":"2026-03-02T13:05:34","modified_gmt":"2026-03-02T12:05:34","slug":"doctoral-defence-hedieh-haddad","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/research-en\/events\/doctoral-defence-hedieh-haddad\/","title":{"rendered":"Doctoral Defence: Hedieh HADDAD"},"content":{"rendered":"\n
<\/div>\n\n\n

The Doctoral School in Science and Engineering<\/a> is happy to invite you to Hedieh HADDAD defence entitled<\/p>\n\n\n\n

\nHYPERPARAMETER OPTIMIZATION OF CONSTRAINT PROGRAMMING SOLVERS<\/h2>\n\n\n\n

Supervisor<\/strong>: Prof Pascal BOUVRY<\/a><\/p>\n\n\n\n

The performance of constraint programming solvers is highly sensitive to the configuration of search heuristics, restart policies, and propagation levels. Manually tuning these hyperparameters is inefficient, expertise-dependent, and rarely generalizes across diverse problem instances. This thesis addresses this fundamental bottleneck by automating solver configuration through formalized hyperparameter optimization, aiming to create adaptive, reproducible, and self-configuring constraint solvers.<\/p>\n\n\n\n

In the first part, we establish the theoretical and practical foundations. We formalize solver configuration as a hyperparameter optimization problem over discrete, categorical, and often conditional parameter spaces. We then conduct a comprehensive evaluation of optimization strategies, including grid search, random search, Hamming-distance local search, Bayesian optimization, and Hyperband, within the context of constraint programming. This analysis reveals that model-based methods, particularly Bayesian optimization, offer superior sample efficiency and robustness under strict time budgets, providing a principled basis for automated configuration.<\/p>\n\n\n\n

Building on these insights, the core contribution of this thesis is the introduction of the probe and solve algorithm, a novel, solver-agnostic framework for time-budgeted hyperparameter optimization. The framework operates in two adaptive phases: a probing phase for exploring the configuration space and a solving phase that executes with the most promising configuration. It incorporates key mechanisms such as dynamic timeout scheduling, stagnation-aware early stopping, and memory-based caching to maximize efficiency without requiring internal solver modifications. This design enables robust, near-parameterless operation across varying computational constraints.<\/p>\n\n\n\n

Next, we present an extensive empirical validation of the framework. Through large-scale benchmarking on the ACE and Choco solvers using XCSP3 and MiniZinc problem sets, we demonstrate that the probe and solve algorithm consistently outperforms default solver configurations. We perform a full-factorial analysis of its modular components, time allocation, timeout evolution, and stopping conditions, to identify optimal configurations and validate the framework\u2019s generalization across solver architectures and modeling environments.<\/p>\n\n\n\n

In the final part, we transition from solver-centric optimization to real-world application within the architecture, engineering, and construction domain. We develop an integrated pipeline that transforms building information models and textual engineering standards, such as ISO 10077-1 for thermal performance and ISO 717-1 for acoustic insulation, into executable constraint programming models. This enables automated, multi-objective design optimization with guaranteed regulatory compliance. We further enhance this pipeline with a hierarchical search strategy, combining the probe and solve algorithm for learned intensification with large neighborhood search for structural exploration, resulting in a comprehensive method for generating diverse, high-quality Pareto-optimal design solutions. This thesis bridges algorithmic innovation with practical application, advancing both automated solver configuration and standards-aware optimization in combinatorial problem solving.<\/p>\n\n\n\n