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EvoAl — Codeless Domain-Optimisation

Bernhard J. Berger; Christina Plump; Lauren Paul; Rolf Drechsler
In: The Genetic and Evolutionary Computation Conference (GECCO). Genetic and Evolutionary Computation Conference (GECCO-2024), July 14-18, Melbourne, Australia, 2024.


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 opensource 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.