
Franco Castagna enhanced the EvoMaster repository by overhauling its genetic algorithm architecture to improve configurability, reliability, and maintainability. He introduced a configurable workflow and the GASolutionSource component, enabling more reproducible results and consistent scoring within generations. Applying Java and Kotlin, Franco refactored the codebase using the Strategy and Observer design patterns, simplified dependency injection, and modernized operator instantiation. He focused on code clarity by renaming interfaces and methods, updating documentation, and reorganizing packages. Through expanded unit testing and improved test coverage, Franco’s work supported faster experimentation and more robust builds, demonstrating depth in backend development and software engineering.

October 2025: EvoMaster GA improvements focused on configurability, reliability, and maintainability of the genetic algorithm. Delivered a configurable GA workflow, modernized architecture for easier extension, and cleaned up the GA API. These changes enable faster experimentation, more reproducible results, and more robust builds with stronger test coverage.
October 2025: EvoMaster GA improvements focused on configurability, reliability, and maintainability of the genetic algorithm. Delivered a configurable GA workflow, modernized architecture for easier extension, and cleaned up the GA API. These changes enable faster experimentation, more reproducible results, and more robust builds with stronger test coverage.
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