Sentence examples for stochastic optimizer from inspiring English sources

The model is applied to study the jet-runner interaction in various operation conditions and then to perform numerical design optimization of the bucket shape, using a stochastic optimizer based on evolutionary algorithms.

A stochastic optimizer (Simulated Annealing) has been used to design such a polychromatic field periodic in time.

Variable relevance estimation assuming variable independence was achieved using a kernel perceptron classifier coupled with a probabilistic-model-building stochastic optimizer.

No stochastic optimizer was used, so it may be possible for slightly higher performance (and slightly better agreement) with a exhaustive EP parameter search.

SIMOP (SIMulation for OPtimization) is an application that automatically creates FORTRAN simulation models for the stochastic optimizers MSGA and MSIMPSA.

The introduction of discrete variables benefits the performance of both stochastic optimizers, contrary to what should a priori be expected, since a MINLP formulation promotes an accurate search on lower and upper bounds for several continuous variables.

Such trees arise naturally, for instance, in problems which involve candidate designs at several levels of abstraction and which use stochastic optimizers such as genetic algorithms or simulated annealing to generate a candidate at one level from a parent at the previous level.

Particle swarm optimizer (PSO) is a population-based stochastic optimization technique which has already been successfully applied to the engineering and other scientific fields.

The results of Entropy Based Restricted Stochastic Query Optimizer (ERSQO) are compared with the results of Exhaustive Enumeration Query Optimizer (EAQO), Simple Genetic Query Optimizer (SGQO), Novel Genetic Query Optimizer (NGQO) and Restricted Stochastic Query Optimizer (RSQO).

Finally, in regard to the consistency of stochastic query optimizers, the results of SGQO, NGQO, RSQO and ERSQO are 96.2%, 97.2%, 97.45 and 97.8% consistent respectively.

Stochastic global optimizers on the other side converges rapidly to the vicinity of the global solution, although further refinements are typically costly.