Configuration file for NEAT Algorithm

config-FeedForward.txt

[NEAT] # -> The Configuration File with all variables and parameters are defined
fitness_criterion     = max #-> This is to determine the best birds to select [ Select the birds with highest fitness]
fitness_threshold     = 100 #-> Number we need to reach before we terminate the program
pop_size              = 20 #-> The number of birds we want in the experiment 
reset_on_extinction   = False #-> Separates the kind of population into species, and creates random species if they get extinct

[DefaultGenome] #-> All of population members (birds) genome, nodes= neurons and genes = connections,  
# node activation options
activation_default      = tanh #-> Activation function
activation_mutate_rate  = 0.0 #-> to change it randomly 
activation_options      = tanh #-> options for having different activation functions

# node aggregation options #-> how to process the connections and weights
aggregation_default     = sum
aggregation_mutate_rate = 0.0
aggregation_options     = sum

# node bias options #-> parameters for how likely the bias changes 
bias_init_mean          = 0.0
bias_init_stdev         = 1.0
bias_max_value          = 30.0
bias_min_value          = -30.0
bias_mutate_power       = 0.5
bias_mutate_rate        = 0.7
bias_replace_rate       = 0.1

# genome compatibility options  #-> adjustable parameters that control how genetic differences between neural network genomes are evaluated when calculating their similarity or genetic distance
compatibility_disjoint_coefficient = 1.0 #-> influence of differences in disjoint and excess genes on the genetic distance calculation.
compatibility_weight_coefficient   = 0.5 #-> differences in weights, biases, and other attributes on the genetic distance calculation

# connection add/remove rates #-> for adding or removing a new connection
conn_add_prob           = 0.5
conn_delete_prob        = 0.5

# connection enable options #-> connections will have active or deactivated parameters
enabled_default         = True
enabled_mutate_rate     = 0.01

feed_forward            = True #-> feed forward neural network
initial_connection      = full #-> fully connected layers to start

# node add/remove rates #-> node[Neurons] add or delete probability
node_add_prob           = 0.2
node_delete_prob        = 0.2

# network parameters #-> Setting the layer parameters, we have no hidden layer, since the logic is pretty straightforward
num_hidden              = 0
num_inputs              = 3
num_outputs             = 1

# node response options #-> parameters for how likely the node response changes  
response_init_mean      = 1.0
response_init_stdev     = 0.0
response_max_value      = 30.0
response_min_value      = -30.0
response_mutate_power   = 0.0
response_mutate_rate    = 0.0
response_replace_rate   = 0.0

# connection weight options #-> parameters for how likely the weight changes 
weight_init_mean        = 0.0
weight_init_stdev       = 1.0
weight_max_value        = 30
weight_min_value        = -30
weight_mutate_power     = 0.5
weight_mutate_rate      = 0.8
weight_replace_rate     = 0.1

[DefaultSpeciesSet] 
compatibility_threshold = 3.0

[DefaultStagnation] 
species_fitness_func = max #-> This setting specifies the function used to compute the fitness of a species.
max_stagnation       = 20 #-> how many generations we go without increasing fitness
species_elitism      = 2 #-> This parameter controls how many of the best-performing individuals from each species are preserved as elite individuals

[DefaultReproduction]
elitism            = 2 #-> This setting determines how many individuals are considered as elite individuals and preserved unchanged in the next generation.
survival_threshold = 0.2 #-> This parameter sets a survival threshold for the proportion of individuals within a species.