AQARIOS
SAGA - Simulated Annealing Assisted Genetic Algorithm
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
num_sweeps : int
The number of sweeps for simulated annealing.
Default: 10num_sweeps_inc_factor : float
factor of increasement for `num_sweeps` after each iteration
Default: 1.2num_sweeps_inc_max : int
Maximum number of num_sweeps that may be reached when increasing the `num_sweeps` value.
Default: 7000beta_range_type : 'default', 'percent', 'fixed', 'inc'
Method that is used to compute the beta range. default': the same as percent with values [50, 1] 'percent': the percentage chance of flipping qubits from hot to cold temperature 'fixed': a fixed temperature as a value 'inc': the default or percentage beta range but with decreasing percentages from iteration to iteration
Default: "default"beta_range : Tuplefloat, float
Explicit beta range that is used for beta_range_type 'fixed' and 'percent'.
Default: Nonep_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100SAGAPW - Simulated Annealing Assisted Genetic Algorithm Pairwise
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
num_sweeps : int
The number of sweeps for simulated annealing.
Default: 10num_sweeps_inc_factor : float
factor of increasement for `num_sweeps` after each iteration
Default: 1.2num_sweeps_inc_max : int
Maximum number of num_sweeps that may be reached when increasing the `num_sweeps` value.
Default: 7000beta_range_type : 'default', 'percent', 'fixed', 'inc'
Method that is used to compute the beta range. default': the same as percent with values [50, 1] 'percent': the percentage chance of flipping qubits from hot to cold temperature 'fixed': a fixed temperature as a value 'inc': the default or percentage beta range but with decreasing percentages from iteration to iteration
Default: "default"beta_range : Tuplefloat, float
Explicit beta range that is used for beta_range_type 'fixed' and 'percent'.
Default: Nonep_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100SAGAMP - Simulated Annealing Assisted Genetic Algorithm Multiprocessing
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
num_sweeps : int
The number of sweeps for simulated annealing.
Default: 10num_sweeps_inc_factor : float
factor of increasement for `num_sweeps` after each iteration
Default: 1.2num_sweeps_inc_max : int
Maximum number of num_sweeps that may be reached when increasing the `num_sweeps` value.
Default: 7000beta_range_type : 'default', 'percent', 'fixed', 'inc'
Method that is used to compute the beta range. default': the same as percent with values [50, 1] 'percent': the percentage chance of flipping qubits from hot to cold temperature 'fixed': a fixed temperature as a value 'inc': the default or percentage beta range but with decreasing percentages from iteration to iteration
Default: "default"beta_range : Tuplefloat, float
Explicit beta range that is used for beta_range_type 'fixed' and 'percent'.
Default: Nonep_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100QAGA - Quantum Assisted Genetic Algorithm
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
use_qpu : True
Whether to use the QPU for the optimization problem.
Default: truep_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100QAGAPW - Quantum Assisted Genetic Algorithm Pairwise
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
p_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100QAGAMP - Quantum Assisted Genetic Algorithm Multiprocessing
QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms: We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions. This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time / iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned. The Simulated Annealing Genetic Algorithm is used for the mutation.
Provider: dwave
Solution Example:
Parameters:
p_size : int
Size of the population.
Default: 20p_inc_num : int
Number of individuals that are added to the population size after each iteration.
Default: 5p_max : int
Maximum size of the population.
Default: 160pct_random_states : float
Percentage of random states that are added to the population after each iteration.
Default: 0.25mut_rate : float
Mutation rate, i.e., probability to mutate an individual. Min: 0.0, Max: 1.0
Default: 0.5rec_rate : int
Recombination rate, i.e. number of mates each individual is recombined with after each iteration
Default: 1rec_method : 'cluster_moves', 'one_point_crossover', 'random_crossover'
The recombination method for the genetic algorithm.
Default: "random_crossover"select_method : 'simple', 'shared_energy'
Method used for the selection phase in the genetic algorithm.
Default: "simple"target : float
Energy level that the algorithm tries to reach. If `None`, the algorithm will run until any other stopping criterion is reached.
Default: Noneatol : float
Absolute tolerance used to compare the energies of the target and the individuals.
Default: 0rtol : float
Relative tolerance used to compare the energies of the target and the individuals.
Default: 0timeout : float
The total solving time after which the solver should be stopped. This total solving time includes preprocessing, network overhead when communicating with DWave's API, as well as the actual annealing time.
Default: 60max_iter : int
Maximum number of iterations after which the algorithm will stop.
Default: 100