moncar

Synopsis

Monte Carlo optimization method

Description

The monte carlo optimization method is an implementation of the differential evolution algorithm.

Differential Evolution

A population of fixed size which contains n-dimensional vectors of (where n is the number of free parameters) are randomly initialized. At each iteration, a new n-dimensional vector is generated by combining vectors from the pool of population, the resulting trial vector is selected if it lowers the objective function. For more details see:

Storn, R. and Price, K. "Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces." J. Global Optimization 11, 341-359, 1997.

or http://www.icsi.berkeley.edu/~storn/code.html

Method Options

• ftol - the function tolerance to terminate the search for the minimum; default is sqrt( DBL_EPSILON ) ~ 1.19209289551e-07, where DBL_EPSILON is the smallest number x such that 1.0 != 1.0 + x.
• maxfev - the maximum number of function evaluations; default is 8192 * n (number of free parameters).
• verbose - the amount of information to print about the fit progress. Default is 0 (no output).
• seed - the seed for the random number generator; default value is 2357.
• population_size - the population of potential solutions is allowed to evolve to search for the minimum of the fit statistics. The trial solution is randomly chosen from a combination from the current population, and it is only accepted if it lowers the statistics. The default value for the population size is 16 times the number of free parameters.
• xprob - the crossover probability should be within the range [0.5,1.0]; default value is 0.9. A high value for the crossover probability should result in a faster convergence rate; conversely, a lower value should make the differential evolution method more robust.
• weighting_factor - the weighting factor should be within the range [0.5, 1.0]; default is 0.8. Differential evolution is more sensitive to the weighting_factor then the xprob parameter. A lower value for the weighting_factor, coupled with an increase in the population_size, gives a more robust search at the cost of efficiency.

Example

sherpa> set_method("moncar")
sherpa> get_method_name()
'moncar'

Set the optimization method and then confirm the new value.

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.

See Also

methods

gridsearch, levmar, list_iter_methods, list_methods, neldermead, set_method, set_method_opt

statistics

set_sampler_opt