Spin Glass Energy Minimization through Learning and Evolution

• Published in: Optical memory & neural networks Vol. 29; no. 3; pp. 187 - 197
• Main Author: Red’ko, V. G.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01-07-2020
• Subjects: Complexity; Computer Science; Information Storage and Retrieval; genotypes; learning and evolution of agents; phenotypes; spin glass energy minimization; autonomous agents
• ISSN: 1060-992X; 1934-7898
• DOI: 10.3103/S1060992X20030054

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.