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Spin Glass Energy Minimization through Learning and Evolution

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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.

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ACKNOWLEDGMENTS

The author thanks Ya.M. Karandashev, B.V. Kryzhanovsky, and M.Yu. Malsagov for their valuable comments and discussions.

Funding

The work was financially supported by State Program of SRISA RAS. Project no. 0065-2019-0003 (AAA-A19-119011590090-2).

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  1. Scientific Research Institute for System Analysis, Russian Academy of Sciences, 117218, Moscow, Russia

    V. G. Red’ko

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  1. V. G. Red’ko
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Correspondence to V. G. Red’ko.

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Red’ko, V.G. Spin Glass Energy Minimization through Learning and Evolution. Opt. Mem. Neural Networks 29, 187–197 (2020). https://doi.org/10.3103/S1060992X20030054

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