#    This is the extra parameters file for the GADMA software.

#    Lines that begin with # are ignored.
#    Comments at the end of a line are also ignored.
#    Each line contains: Parameter identifier : value.

#    Lower and upper bounds on N - population sizes, 
#    T - time of time periods, M - migration rates.
#    All bounds are relative to N_A: N is in N_A units, 
#    T in 2 * N_A units, and M in 1 / (2 * N_A).
#    Default: min_N = 0.01, max_N = 100.0, min_T = 0.0, 
#             max_T = 5.0, min_M = 0.0, max_M = 10.0
min_N : 
max_N : 
min_T : 
max_T : 
min_M : 
max_M : 


#    Parameters for the Genetic Algorithm.
#
#    Constant for initial design. The number of solutions in the initial
#    design is equal to the number of parameters multiplied by this constant.
#    Default: 10
Num init const :

#    Size of one generation of solutions in the GA:
#    Default: 10
Size of generation : 

#    Fractions of current models, mutated models, and crossed models 
#    to be taken to the new population.
#    The sum of the fractions should be <= 1, and the remaining fraction is 
#    for random models.
#    Default: 0.3, 0.2, 0.3
Fractions : 

#    The fractions can be set independently here.
#    Number of the best solutions taken from the previous generation.
#    Default: 3
N elitism :
#    Fraction of mutated solutions in the new generation.
#    Default: 0.2
P mutation :
#    Fraction of crossed solutions in the new generation.
#    Default: 0.3
P crossover :
#    Fraction of randomly generated solutions in the new generation.
#    Default: 0.2
P random :

#    Mutation strength - the fraction of parameters in a model to mutate 
#    during the global mutation process.
#    The number of parameters to mutate is sampled from a binomial 
#    distribution, so we need to set the mean.
#    Default: 0.775539
Mean mutation strength : 
#
#    Mutation strength can be adaptive: if the mutation is successful (i.e., 
#    it has the best fitness function, log likelihood), the mutation strength 
#    increases by multiplying it by a constant; otherwise, it decreases by dividing 
#    it by (1/4)^constant.
#    When the constant is 1.0, it is not adaptive.
#    Default: 1.302280
Const for mutation strength : 

#    Mutation rate - the fraction of any parameter to change during
#    its mutation.
#    The mutation rate is sampled from a truncated normal distribution, 
#    so we need the mean (standard deviation can be specified in extra params).
#    Default: 0.273263
Mean mutation rate : 
#
#    Mutation rate can also be adaptive, like mutation strength.
#    Default: 1.475288
Const for mutation rate : 

#    The genetic algorithm stops when it is unable to improve the model 
#    by more than epsilon in log likelihood.
#    Default: 1e-2
Eps : 
#    
#    And this happens during N iterations:
#    Default: 100
Stuck generation number : 



#    Parameters for output from optimization algorithms.

#    If > 0, print the optimization status every <verbose> steps.
#    Default: 1
Verbose : 

#    Parameter for drawn SFS plots. vmin - minimum value plotted for SFS.
#    Default: 1 or None if all values of SFS are <= 1.0
Vmin : 


#    Options for mutation, crossing, and random generation.

#    To get a random model, we sample numbers between bounds (min_N, 
#    max_N, min_T, etc.) and then scale them with optimal_sfs_scaling. 
#    We can choose N_A randomly between min_N and max_N or always take 
#    it as 1.0.
#    Default: True
Random N_A : 



#    Options for printing summary information about repeats 
#    in the parent process.
#    How often to print all the best models by likelihood and by BIC.
#    Default: 1 (minute)
Time to print summary : 



#    Global parameters for optimization (both global and local).
#
#    Global optimizer name.
#    Default: Genetic_algorithm
Global optimizer :

#    If True, x will be transformed by the logarithm.
#    In our case, that means population sizes and times will be inferred 
#    in log-scale.
#
#    For global optimization:
#    Default: True
Global log transform :
#    For local optimization:
#    Default: True
Local log transform :

#    Maximum number of log-likelihood evaluations for 
#    global optimization.
#    Default: None
Global maxeval :
#    For local optimization:
#    Default: None
Local maxeval :

#    Maximum number of algorithm iterations for 
#    global optimization.
#    Default: None
Global maxiter :
#    For local optimization:
#    Default: None
Local maxiter :
