screen_nsga2_parameters

A user-defined function that NSGA2 seeks to
minimise

function_to_evaluate

An object containing the emulator input
and output names, the input parameters for function to evaluate, minimum
and maximum values for emulator inputs. These should be set using the
function that creates that object prior to running this method,
nsga2_set_user_params

nsga2_user_set_parameters

an object containing the minimum and
maximum values of generation number, crossover probability and mutation
probability to be assessed

nsga_sensitivity_parameters

An object containing the population size, number
of generations, crossover probability and mutation probability to be
assessed

nsga2_settings

This method performs a sensitvity analysis of key settings for the nsga2
algorithm. Different values of generation number, crossover and mutation
rate are assessed and the values for each objective, along with the
variance of the parameter inputs are written out to file in .csv format
so the user can assess which settings are best suited to the chosen
application. Values for the crossover and mutation distribution indices,
used in simulated binary crossover, are left at their default settings,
but can be overwritten when running the emulator_parameter_evolution method.

Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. 'spartan', first described in our 2013 publication in PLoS Computational Biology, provided code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 added support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions. Version 3.0 offers significant additional functionality that permits the creation of emulations of simulation results, derived using the same sampling techniques in the global sensitivity analysis techniques, and the generation of combinations of these machine learning algorithms to one create one predictive tool, more commonly known as an ensemble model. Version 3.0 also improved the standard of the graphs produced in the original sensitivity analysis techniques, and introduced a polar plot to examine parameter sensitivity.

screen_nsga2_parameters: Screens NSGA-2 related parameters, guiding which to select for evolving parameter sets

Description

Usage

Arguments

Value