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Interpretable regression models are important for many application domains,as they allow experts to understand relations between variables from sparsedata. Symbolic regression addresses this issue by searching the space of allpossible free form equations that can be constructed from elementary algebraicfunctions. While explicit mathematical functions can be rediscovered this way,the determination of unknown numerical constants during search has been anoften neglected issue. We propose a new multi-objective memetic algorithm thatexploits a differentiable Cartesian Genetic Programming encoding to learnconstants during evolutionary loops. We show that this approach is competitiveor outperforms machine learned black box regression models or hand-engineeredfits for two applications from space: the Mars express thermal power estimationand the determination of the age of stars by gyrochronology.

Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms