标题： 遗传AI：用于从头开始动态多目标优化的进化博弈 显示英文标题

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

摘要： 我们引入了遗传AI，这是一种用于多目标优化的新方法，无需外部参数或预定义权重。 该方法可以应用于所有可以用矩阵形式表示的问题，并允许AI模型的无数据训练。 在不使用预定义规则或训练数据的情况下，遗传AI首先将输入数据转换为基因和生物体。 在从基本原理进行的模拟中，这些基因和生物体为了适应性而竞争，它们的行为由普遍的进化策略所支配。 我们提出了四种进化策略：主导型、利他型、平衡型和自私型，并展示了如何在完全自洽的进化博弈中使用线性组合。 研究适应性和进化稳定均衡，遗传AI有助于解决一组预定义的、离散的解决方案并动态变化的优化问题。 我们在两个决策问题上展示了该方法的普遍性。 显示英文摘要

摘要： 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.