Single-objective problems

Unconstrained

class jmetal.problem.singleobjective.unconstrained.OneMax(number_of_bits: int = 256)

Bases: BinaryProblem

The OneMax problem is a simple optimization problem that counts the number of ones in a binary string.

The objective is to maximize the number of ones in the binary string, which is equivalent to minimizing the negative count of ones.

Args:

number_of_bits: The length of the binary string (default: 256)

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

class jmetal.problem.singleobjective.unconstrained.Rastrigin(number_of_variables: int = 10)

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.singleobjective.unconstrained.Sphere(number_of_variables: int = 10)

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.singleobjective.unconstrained.SubsetSum(C: int, W: list)

Bases: BinaryProblem

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

Knapsack

class jmetal.problem.singleobjective.knapsack.Knapsack(number_of_items: int = 50, capacity: float = 1000, weights: list = None, profits: list = None, from_file: bool = False, filename: str = None)

Bases: BinaryProblem

Class representing Knapsack Problem.

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

TSP

class jmetal.problem.singleobjective.tsp.TSP(instance: str = None)

Bases: PermutationProblem

Class representing TSP Problem.

create_solution() → PermutationSolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: PermutationSolution) → PermutationSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int