Abstract

The research considers the minimization of spin glass energy via learning and evolution. The Sherrington-Kirkpatrick spin-glass model is used. A population of autonomous agents is considered. The genotype and phenotype of each agent are chains consisting of a great number of spins. The energy of spin glasses is minimized through learning and evolution of agents. The genotypes of agents are optimized by evolution; the phenotypes are optimized by learning. The evolution of a population of agents is analyzed. In the evolution the fitness of agents is determined by the energy of the spin glass of final phenotypes resulted from learning: the lower the energy is, the higher the fitness of the agent is. In the next generation agents are selected with probabilities corresponding to their fitnesses. Agents-descendants get mutationally modified genotypes of agents-ancestors. The interaction between learning and evolution during the spin glass energy minimization is investigated. The research involves the computer simulation.

中文翻译：

---

通过学习和进化使自旋玻璃能量最小化

摘要

该研究考虑了通过学习和进化使自旋玻璃能量最小化。使用Sherrington-Kirkpatrick旋转玻璃模型。考虑了一群自治代理。每种药物的基因型和表型是由大量自旋组成的链。通过学习和发展代理，旋转玻璃的能量被最小化。药物的基因型通过进化得到优化。通过学习优化表型。分析了代理商群体的演变。在进化过程中，主体的适应度取决于学习产生的最终表型的旋转玻璃杯的能量：能量越低，主体的适应度越高。在下一代中，选择具有与其适应度相对应的概率的代理。药剂后代得到突变的基因型的祖先。研究了自旋玻璃能量最小化过程中学习与进化之间的相互作用。研究涉及计算机仿真。