Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization
We introduce Genetic AI, a novel method for multi-objective optimization without external parameters or predefined weights. The method can be applied to all problems that can be formulated in matrix form and allows for a data-less training of AI models. Without employing predefined rules or training data, Genetic AI first converts the input data into genes and organisms. In a simulation from first principles, these genes and organisms compete for fitness, where their behavior is governed by universal evolutionary strategies. We present four evolutionary strategies: Dominant, Altruistic, Balanced and Selfish and show how a linear combination can be employed in a fully self-consistent evolutionary game. Investigating fitness and evolutionary stable equilibriums, Genetic AI helps solving optimization problems with a set of predefined, discrete solutions that change dynamically. We show the universality of the approach on two decision problems.
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