# evol.problems.functions package¶

## evol.problems.functions.variableinput module¶

class evol.problems.functions.variableinput.FunctionProblem(size=2)[source]
check_solution(solution: Sequence[float]) → Sequence[float][source]
eval_function(solution: Sequence[float]) → float[source]
value(solution)[source]
class evol.problems.functions.variableinput.Rastrigin(size=2)[source]
value(solution: Sequence[float]) → float[source]

The optimal value can be found when a sequence of zeros is given. :param solution: a sequence of x_i values :return: the value of the Rosenbrock function

class evol.problems.functions.variableinput.Rosenbrock(size=2)[source]
value(solution: Sequence[float]) → float[source]

The optimal value can be found when a sequence of ones is given. :param solution: a sequence of x_i values :return: the value of the Rosenbrock function

class evol.problems.functions.variableinput.Sphere(size=2)[source]
value(solution: Sequence[float]) → float[source]

The optimal value can be found when a sequence of zeros is given. :param solution: a sequence of x_i values :return: the value of the Sphere function

## Module contents¶

The evol.problems.functions part of the library contains simple problem instances that do with known math functions.

The functions in here are typically inspired from wikipedia: https://en.wikipedia.org/wiki/Test_functions_for_optimization