the objective function to be minimize (f maps an integer vector to a double)
the starting point for the search (seed for GA)
the constraint function to be satisfied, if any
the maximum/starting step size (make larger for larger domains)
Pair consisting of an integer vector and its functional value (a double)
For each individual in the population, cross it with some other individual.
The objective function f re-scaled by a weighted penalty, if constrained.
The objective function f re-scaled by a weighted penalty, if constrained.
the coordinate values of the currrent point
For each individual in the population, cross it with some other individual.
For each individual in the population, cross it with some other individual. Let the crossover be dependent of the fitness of the individual.
Find the fittest individual (smallest value of objective function).
Generate an initial population of individuals.
Randomly select individuals for mutation (change a value at one position).
Print the current population
Solve the minimization problem using a genetic algorithm.
This class performs local search to find minima of functions defined on integer vector domains (z^n).
minimize f(x) subject to g(x) <= 0, x in Z^n