#	It is extra parameters file for GADMA software.

#	Lines started from # are ignored.
#	Also comments at the end of a line are ignored too.
#	Every line contains: Identificator of parameter : value.
#	If you want to change some default parameters, you need to 
#	remove # at the begining of line and change corresponding 
#	parameter.


#	Lower and upper bounds on N - sizes of populations, 
#	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: 0.01
max_N: 100.0
min_T: 1e-15
max_T: 5.0
min_M: 0
max_M: 10


#	Parameters for Genetic Algorithm.
#
#	Size of population of demographic models in GA:
#	Default: 10
Size of generation: 10

#	Fractions of current models, mutated models and crossed models 
#	to be taken to new population.
#	Sum of fractions should be <= 1, the remaining fraction is 
#	fraction of random models.
#	Default: 0.2,0.3,0.3
Fractions: [0.55560528752, 0.18828153004, 0.12600048532]

#	Mutation strength - fraction of parameters in model to mutate 
#	during global mutation process of model.
#	Number of parameters to mutate is sampled from binomial 
#	distribution, so we need to set mean.
#	Default: 0.2
Mean mutation strength: 0.625049
#
#	Mutation strength can be adaptive: if mutation is good, 
#	i.e. has the best fitness function (log likelihood),
#	then mutation strength is increased multiplying by const 
#	otherwise it decreases dividing by (1/4)^const.
#	When const is 1.0 it is not adaptive.
#	Default: 1.0
Const for mutation strength: 1.016571

#	Mutation rate - fraction of any parameter to change during
#	its mutation.
#	Mutation rate is sampled from truncated normal distribution, 
#	so we need mean (std can be specified in extra params).
#	Default 0.2
Mean mutation rate: 0.453272
#
#	Mutation rate also can be adaptive as mutation strength.
#	Default: 1.02
Const for mutation rate: 1.068062

#	Genetic algorithm stops when it couldn't improve model by
#	more that epsilon in logLL
#	Default: 1e-2
Eps: 0.01
#	
#	and it happens during N iterations:
#	Default: 100
Stuck generation number: 100



#	Parameters for output of optimizations algorithms

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

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


#	Options of mutation, crossing and random generating.

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



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


#	Other parameters of run without description:
dadi_available: true
global_maxiter: 3
global_optimizer: Genetic_algorithm
local_maxiter: null
migration_masks: null
moments_available: true
mutation_rate: null
n_elitism: 2
num_init_const: null
outgroup: true
p_crossover: 0.18828153004
p_mutation: 0.55560528752
p_random: 0.12600048532
sequence_length: null
split_fractions: true
symmetric_migrations: false
