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.
Published Version
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