Applying the concepts and formalism from Evolutionary Game Theory to the data regime, the fundamental paradigms of Evolutionary Data Theory are introduced. It is shown that essential definitions and results such as replicator equations, evolutionary strategies, the Bishop-Cannings theorem and the analogy to Lotka-Volterra systems can be mapped to the data interpretation. Understanding data in matrix form as evolutionary entities, input data is mapped to genes and organisms. Steered by genetic fitness and two evolutionary strategies, Dominant-Balanced and Altruistic-Selfish, data records and features conduct an evolutionary game. It is shown that this evolutionary interpretation remains universally meaningful, by proving convergence to a unique rest point, where all data features persist in the population. A basic example of multi-objective optimization is shown as well as a related distribution problem and machine learning applications.

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