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

TL;DR

Genetic AI introduces a parameter-free, data-less evolutionary approach for dynamic multi-objective optimization, applicable to problems with matrix-formulated data, using universal strategies to find stable solutions.

Contribution

It presents a novel, parameter-free evolutionary game method for multi-objective optimization that does not require training data or predefined weights.

Findings

Successfully applied to two decision problems.

Demonstrates universal applicability of evolutionary strategies.

Identifies stable solutions in dynamic optimization scenarios.

Abstract

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.