the type of the meta-fitness
the type of the meta-fitness
Type of the state maintained to study the evolution of the algorithm
Type of the state maintained to study the evolution of the algorithm
Deviation considered has sufficiently low to stop
Deviation considered has sufficiently low to stop
Size of the sliding window to evaluate the deviation
Size of the sliding window to evaluate the deviation
Compute the diversity metric of the values
Compute the diversity metric of the values
a set of values
a diversity sequence in the diversity of the individual i at the position i
Filter the individuals
Filter the individuals
the set of evaluated individuals
the filtrated individuals
Compute the initial state
Compute the initial state
the initial state
Generate a population from a set of indiviuals
Generate a population from a set of indiviuals
the population with the meta-fitness for each individual
Test if the algorithm has converged.
Test if the algorithm has converged.
the current population
the actual termination state
a boolean which is equal to true if a terminal state has been detected and the new termination state
Generate a population from a set of indiviuals that is filtered in a first time
Generate a population from a set of indiviuals that is filtered in a first time
a set of individual
the filtred population with the meta-fitness for each individual
Termination creterium computed from the variation of the maximum crowding distance among the population elements. It stop when this metric stabilize.
FIXME: take into account only the last ranked individual, pb it can be empty due to the filter on the positive infinity diversity