Applications of random restart to genetic algorithms
Information Sciences: an International Journal
Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Weight Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria
Advances in Neural Information Processing Systems 5, [NIPS Conference]
The error surface of the simplest xor network has only global minima
Neural Computation
Neural expert networks for faster combined collaborative and content-based recommendation
Journal of Computational Methods in Sciences and Engineering
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Consider an algorithm whose time to convergence is unknown (because of some random element in the algorithm, such as a random initial weight choice for neural network training). Consider the following strategy. Run the algorithm for a specific time T. If it has not converged by time T, cut the run short and rerun it from the start (repeat the same strategy for every run). This so-called restart mechanism has been proposed by Fahlman (1988) in the context of backpropagation training. It is advantageous in problems that are prone to local minima or when there is a large variability in convergence time from run to run, and may lead to a speed-up in such cases. In this article, we analyze theoretically the restart mechanism, and obtain conditions on the probability density of the convergence time for which restart will improve the expected convergence time. We also derive the optimal restart time. We apply the derived formulas to several cases, including steepest-descent algorithms.