A2. Configuration Parameters

The following section has information about the parameters used by MLC to manipulate the creation and evolution of the populations throughout an entire experiment. The parameters shown in this section are exclusively used internally by MLC, so if the user find another parameters inside the configuration files of the examples, that means they were added on purpose to be used in that particular experiment

LOGGING

Parameter	Description	Range of Values
logmode	Log configuration used by MLC	root, testing, file, console, (...customs)

POPULATION

Parameter	Description	Range of Values
size	Number of individuals of every population	integer
sensors	Number of inputs for control laws	integer
sensor_spec	If true, sensors' indexes are retrieved from the parameter sensor_list. If False, sensors' indexes will be completed in an autonumeric form	boolean (0,1)
sensor_list	Set the sensor indexes for the sensors set in the LISP regressive tree function	integer (list)
controls	Number of control laws	integer
sensor_prob	Probability of create a sensor-based operation	integer (0 to 1)
leaf_prob	Probability of create a leaf node	integer (0 to 1)
range	Absolute value of minimum and maximum of generated constans	integer
precision	Decimal precision of generated constantes	1-20
opsetrange	Index of operations to use	1(+), 2(-), 3(*), 4(/), 5(sin), 6(cos), 7(log), 8(exp), 9(tanh)
formal	TODO	-
end_character	TODO	-
individual_type	TODO	-

GP

Parameter	Description	Range of Values
maxdepth	Maximum depth of all individuals	integer
maxdepthfirst	Maximum depth of individuals in first generation	integer
mindepth	Minimum depth of individuals before simplification	integer
mutmindepth	Minimum depth at which a node is selected for mutation or crossover	integer
mutmaxdepth	Maximum depth at which a mode is selected for mutation or crossover	integer
mutsubtreemindepth	Minimum depth of a subtree for crossover	integer
generation_method	Choose the way the individuals will be created	fixed_maxdepthfirst, mixed_ramped_gauss, random_maxdepthfirst, full_maxdepthfirst, mixed_maxdepthfirst, mixed_ramped_even
gaussigma	Standard deviaton for the gaussian distribution (if mixed_ramped_gauss is used)	float
ramp	Range of tree depth to be sued in ramped generation methods	array
maxtries	TODO	-
mutation_types	TODO	-

OPTIMIZATION

Parameter	Description	Range of Values
elitism	Number of individuals selected by elitism	integer
probrep	Replication probability	float
probmut	Mutation probability	float
probcro	Crossover probability	float
selectionmethod	Method by which the individuals are selected	tournament, fitness_proportional, pareto
tournamentsize	Number of individuals that enter each tournament	integer
lookforduplicates	Boolean value that remove duplicates in the same generation	0, 1
simplify	Boolean value that simplify LISP expressions using rules and actions coded in opset	0, 1
cascade	TODO	-

EVALUATOR

Parameter	Description	Range of Values
evaluation_method	Dictates the way the evaluation of the individuals will be made	test, mfile_multi, mfile_standalone
evaluation_function	Dynamically choose the function of evaluation to be used	arduino, toy_problem (for the moment)
indfile	Name of the file where an external evaluator reads the individual	string
Jfile	Name of the file where an external evaluator returns the cost	string
exchangedir	Path for indfile and Jfile	string
evaluate_all	TODO	-
ev_again_best	Boolean value which indicates if a population has to be reevaluated before evolve it.	true, false
ev_again_nb	TODO (I don't really know what this param does :()	-
ev_again_times	Number of times a population will be evaluated if ev_again_best is true.	Integer
artificialnoise	Noise added to the function to the problem to be solved	0,1
execute_before_evaluation	TODO	-
badvalue	cost value attributed to evaluation failures.Legal individuals with cost higher than badvalues has its cost changed to badvalue. This value can be expressed in scientific notation (e.g. 1e36)	double
badvalues_elim	Tell in which populations individuals with bad values must be eliminated	none, first, all
preevaluation	TODO	-
preev_function	TODO	-

BEHAVIOUR

Parameter	Description	Range of Values
save	If set on 1, saves the MLC object in 'mlc_be' before evaluation and in 'mlc_ae' after evaluation. Boolean value	0, 1
saveincomplete	TODO	-
fgen	TODO	-
savedir	If set on 1, will trigger a display of the best individual has defined in the problem function	-

ARDUINO

This parameters are used by the evaluation method 'arduino'

Parameter	Description	Range of Values
baudrate	Baudrate of the Serial port	9600, 115200
port	Port where the Arduino was mounted. Use the command dmesg to get this value	/dev/ttyACM0, /dev/USB0, etc
command_opcode	Opcode of the command sent by the arduino function. Opcode dos activate timeouts and retries	1, 2
wait_period	Time the arduino will wait after a comand is received to send a response	integer (milisencods)
read_timeout	Time the Python serial port will wait for a response	integer (miliseconds)
read_retries	Amount of times the arduino function will try to resend a command	integer