Optimizing initial guesses to improve global minimization

In this paper, we envision global optimization as finding, for a given calculation complexity, a suitable initial guess of a considered optimization algorithm. One can imagine that this possibility clearly improve the capacity of existing optimization algorithms, including stochastic ones. This approach is validated on several large dimension nonlinear minimization problems. Results are compared with those obtained by a geneti algorithm

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