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人工生命进化模型设计的关键问题是学习与进化之间的关系，在自主体生存期内的学习过程可以通过不同的遗传方式指导个体行为的进化。该文利用进化算法和人工神经网络的研究方法，设计了两种不同的人工生命自主体的进化模型，模型解决了先天的遗传进化和后天的个体神经系统强化学习的有机结合问题，并且得出结论认为，强化学习有助于自主体适应复杂的外部环境，同时学习也可以直接或间接地使该适应性成为自主体遗传基因上的固定成分。
The key issue of artificial life evolutionary model design is the relationship between learning and evolution. The learning process in autonomous life can guide the evolution of individual behavior through different ways of inheritance. This paper uses evolutionary algorithm and artificial neural network research methods, designed two different artificial life autonomy evolution model, the model solves the problem of organic combination of innate genetic evolution and acquired nervous system strengthening learning, and draws The conclusion is that intensive learning helps autonomic adapt to complex external environment, and at the same time, learning can directly or indirectly make the adaptability become a fixed component of autologous genetics.