Gabriela Obrzud developed and integrated a gradient-informed Derivative-Based Crossover Operator for real-valued representations within the GENESYS-PK/our_lib repository. Using Python and leveraging expertise in genetic algorithms, numerical methods, and optimization, she designed the operator to approximate the gradient of the fitness function, guiding the crossover process to generate higher-quality offspring in evolutionary algorithms. This approach aimed to improve convergence and optimization performance on real-valued problems, establishing a foundation for further experimentation in the library. Her work focused on engineering depth, with careful integration into the existing Crossover class, and addressed the need for more effective real-valued evolutionary strategies.