# Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2501.19113 · HTML: https://mlsanity.com/paper/2501.19113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Feature weighting for data analysis via evolutionary simulation](https://mlsanity.com/paper/2511.06454/) (2025)
- [Bias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination](https://mlsanity.com/paper/2610.14015/) (2026)

## Abstract (arXiv)

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.