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Item type:Publication, Access status: with files , Can explainability metrics improve genetic programming? Lessons from 2048(Association for Computing Machinery (ACM), 2026-08-13); ; ; Genetic programming (GP) produces solutions that, while often less performant than neural networks, are uniquely amenable to analysis. In domains like game-playing, this explainability could reveal why certain policies succeed or fail-but such insights are rarely leveraged to improve the algorithms themselves. Here, we analyze a GP algorithm for 2048, testing whether structural, behavioral, or semantic metrics of evolved policies correlate with performance. While structural metrics showed no predictive power and behavioral features yielded ambiguous results, we identified a semantic feature that correlated with policy quality. Using this insight, we designed a mutation operator that improved performance. Though modest, this improvement suggests that explainability metrics can guide operator design, even when broader explanatory goals remain unmet. More broadly, our work highlights a potential advantage of GP: its solutions may be analyzable in ways that opaque methods like neural networks are not.Publicationtype: Conference Paper2 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A comparison of repositioning and scheduling algorithms for the ride-hailing problem(Springer, 2026-08); ; ; The Ride-Hailing Problem is an online optimization challenge that coordinates a fleet of vehicles to serve requests revealed over time. The problem is NP-hard, and many solution approaches use stochastic or machine-learning based algorithms, which perform well but are difficult to interpret. We study the trade-offs between performance and explainability. Our results show that greedy assignment consistently outperforms more complex methods, indicating that explainability need not come at the cost of efficiency. We also find that the effectiveness of repositioning strategies strongly depends on demand patterns, highlighting the value of context-aware policies.Publicationtype: Conference Paper7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , EvoAl - codeless domain-optimisation(Association for Computing Machinery (ACM), 2024-07-14); ; ; Applying optimisation techniques such as evolutionary computation to real-world tasks often requires significant adaptation. However, specific application domains do not typically demand major changes to existing optimisation methods. The decisive aspect is the inclusion of domain knowledge and configuration of established techniques to suit the problem. Separating the optimisation technique from the domain knowledge offers several advantages: First, it allows updating domain knowledge without necessitating reimplementation. Second, it improves identification and comparison of the optimisation methods employed. We present EvoAl, an open-source data-science research tool suite that focuses on optimisation research for real-world problems. EvoAl implements the separation of domain-knowledge and detaches implementation from configuration, facilitating optimisation with little programming effort, allowing direct comparability with other approaches (using EvoAl), and ensuring reproducibility. EvoAl also includes options for surrogate models, data models for complex search spaces, data validation, and benchmarking options for optimisation researchers.Publicationtype: Conference Paper15