#	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: 0.0
max_M: 10.0


#	Parameters for Genetic Algorithm.
#
#	Constant for initial design. Number of solutions in initial
#	design is equal to number of parameters multiplied by this constant.
#	Default: 10
Num init const: 5

#	Size of one generation of solutions 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: 0.3,0.2,0.3
Fractions: [0.3, 0.2, 0.3]

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

#	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: 0.775539
Mean mutation strength: 0.775539
#
#	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: 1.302280
Const for mutation strength: 1.30228

#	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 0.273263
Mean mutation rate: 0.273263
#
#	Mutation rate also can be adaptive as mutation strength.
#	Default: 1.475288
Const for mutation rate: 1.475288

#	Genetic algorithm stops when it couldn't improve model by
#	more that epsilon in logLL
#	Default: 1e-2
Eps: 1
#	
#	and it happens during N iterations:
#	Default: 100
Stuck generation number: 5



#	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: False



#	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



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

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

#	Maximum number of log-likelihood evalutions
#	In global optimization
#	Default: None
Global maxeval: Null
#	In local optimization
#	Default: None
Local maxeval: Null

#	Maximum number of algorithm iterations
#	In global optimization
#	Default: None
Global maxiter: 2
#	In local optimization
#	Default: None
Local maxiter: 1

#	Other parameters of run without description:
acquisition_function: EI
bed_file: null
bed_files_dir: null
dadi_available: true
dadi_extrapolation: make_extrap_log_func
demes_available: true
demesdraw_available: true
fixed_ancestral_size: null
kernel: Matern52
ld_kwargs: null
moments_available: true
non_ascertained_pops: null
preprocessed_data: null
recombination_maps: null
region_len: 6400000
