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Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
[article]
2018
arXiv
pre-print
We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks. ESGD combines SGD and gradient-free evolutionary algorithms as complementary algorithms in one framework in which the optimization alternates between the SGD step and evolution step to improve the average fitness of the population. With a back-off strategy in the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population
arXiv:1810.06773v1
fatcat:4vyjnr3acfd3fosu2rije64ubu