Demographic inference for more than three populations
The demographic inference for more than three populations is a special case for GADMA.
First of all, the method for automatic inference using structure demographic model is not working. The only option for four and more populations is to use custom model specified by the researcher using the utilities of the engines.
Moreover, if either dadi, moments or momentsLD are specified as an engine in GADMA then Bayesian optimization (BO) should be used for faster inference instead usual genetic algorithm.
Warning
if momi2 is chosen as an engine in GADMA then Bayesian optimization is a bad choice. Momi2 is very fast and it is better to use the usual genetic algorithm for it.
To change optimization to Bayesian optimization set:
# param_file
...
# Set Bayesian optimization
Global optimizer : SMAC_BO_combination
# Set small initial design
Num init const: 2
# Set number of evaluations
Global maxeval: 200
...
Setting Global maxeval tells GADMA how many evaluations of log-likelihood should be performed. It is required for Bayesian optimization. We recommend 200 evaluations for demographic inference with <15 parameters and 400 evaluations if the number of parameters is bigger than 15.
Setting Num init const should be set to 2. Bayesian optimization require much smaller initial random search and this option restricts number of evaluations there.