Genetic Algorithms

Quantum Assisted Genetic Algorithm Plus

This solver is only available for commercial and academic licenses.
  • Short Name: QAGA+
  • Algorithm Type: Hybrid
  • Category: Hybrid-Internal
  • Native Input Format: QUBO
The QAGA+ algorithm uses 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. For QAGA+, Quantum Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using QAGA+ using the D-Wave backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="QAGA+",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
        "use_qpu": True,
    },
    qpu_tokens=TokenProvider(
        dwave=QpuToken(
            source="inline",
            token="<dwave token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding QAGA+ using the D-Wave backend as an algorithm to LunaBench
algorithms = {
    "QAGA+": {
        "location": "cloud",
        "provider": "dwave",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "dwave": {
                "source": "inline",
                "token": "<dwave token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

D-Wave

Quantum Assisted Genetic Algorithm Plus available on D-Wave
D-Wave Systems, based in Burnaby, Canada, is renowned for its quantum annealing technology, specifically designed for solving optimization problems. Unlike traditional gate-based quantum computers, D-Wave’s annealers are optimized to find the lowest-energy solutions to complex problems by leveraging quantum tunneling. Their latest quantum systems offer thousands of qubits and are used extensively in applications like logistics, machine learning, and material science. D-Wave’s focus on quantum annealing sets it apart as a leader in delivering commercial quantum solutions to real-world challenges.

Read more about D-Wave Systems here.

Quantum Assisted Genetic Algorithm Parallel

This solver is only available for commercial and academic licenses.
  • Short Name: QAGA_PL
  • Algorithm Type: Hybrid
  • Category: Hybrid-Internal
  • Native Input Format: QUBO
The QAGA+ algorithm uses 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. For QAGA+, Quantum Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using QAGA_PL using the D-Wave backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="QAGA_PL",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
    },
    qpu_tokens=TokenProvider(
        dwave=QpuToken(
            source="inline",
            token="<dwave token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding QAGA_PL using the D-Wave backend as an algorithm to LunaBench
algorithms = {
    "QAGA_PL": {
        "location": "cloud",
        "provider": "dwave",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "dwave": {
                "source": "inline",
                "token": "<dwave token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

D-Wave

Quantum Assisted Genetic Algorithm Parallel temporarily unavailable on D-Wave
D-Wave Systems, based in Burnaby, Canada, is renowned for its quantum annealing technology, specifically designed for solving optimization problems. Unlike traditional gate-based quantum computers, D-Wave’s annealers are optimized to find the lowest-energy solutions to complex problems by leveraging quantum tunneling. Their latest quantum systems offer thousands of qubits and are used extensively in applications like logistics, machine learning, and material science. D-Wave’s focus on quantum annealing sets it apart as a leader in delivering commercial quantum solutions to real-world challenges.

Read more about D-Wave Systems here.

Quantum Assisted Genetic Algorithm Pairwise

This solver is only available for commercial and academic licenses.
  • Short Name: QAGA_PW
  • Algorithm Type: Hybrid
  • Category: Hybrid-Internal
  • Native Input Format: QUBO
The QAGA+ algorithm uses 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. For QAGA+, Quantum Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using QAGA_PW using the D-Wave backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="QAGA_PW",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
    },
    qpu_tokens=TokenProvider(
        dwave=QpuToken(
            source="inline",
            token="<dwave token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding QAGA_PW using the D-Wave backend as an algorithm to LunaBench
algorithms = {
    "QAGA_PW": {
        "location": "cloud",
        "provider": "dwave",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "dwave": {
                "source": "inline",
                "token": "<dwave token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

D-Wave

Quantum Assisted Genetic Algorithm Pairwise temporarily unavailable on D-Wave
D-Wave Systems, based in Burnaby, Canada, is renowned for its quantum annealing technology, specifically designed for solving optimization problems. Unlike traditional gate-based quantum computers, D-Wave’s annealers are optimized to find the lowest-energy solutions to complex problems by leveraging quantum tunneling. Their latest quantum systems offer thousands of qubits and are used extensively in applications like logistics, machine learning, and material science. D-Wave’s focus on quantum annealing sets it apart as a leader in delivering commercial quantum solutions to real-world challenges.

Read more about D-Wave Systems here.

Simulated Annealing Genetic Algorithm Plus

This solver is only available for commercial and academic licenses.
  • Short Name: SAGA+
  • Algorithm Type: Classical
  • Category: Classical
  • Native Input Format: QUBO
The SAGA algorithm uses 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. For SAGA, Simulated Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using SAGA+ using the Luna backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="SAGA+",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
        "num_sweeps": 10,
        "num_sweeps_inc_factor": 1.2,
        "num_sweeps_inc_max": 7000,
        "beta_range_type": "default",
        "beta_range": None,
    },
    qpu_tokens=None,
)

Usage via LunaBench

# Example of adding SAGA+ using the Luna backend as an algorithm to LunaBench
algorithms = {
    "SAGA+": {
        "location": "cloud",
        "provider": "dwave",
    },
}

Backends

This algorithm can be run on the following backends:

Luna

Simulated Annealing Genetic Algorithm Plus available on Luna
Luna provides powerful servers designed to run classical algorithms. These servers offer the computational capacity required to efficiently solve non-quantum tasks, ensuring high performance for optimization and other classical workloads.

Simulated Annealing Genetic Algorithm Parallel

This solver is only available for commercial and academic licenses.
  • Short Name: SAGA_PL
  • Algorithm Type: Classical
  • Category: Classical
  • Native Input Format: QUBO
The SAGA algorithm uses 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. For SAGA, Simulated Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using SAGA_PL using the Luna backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="SAGA_PL",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
        "num_sweeps": 10,
        "num_sweeps_inc_factor": 1.2,
        "num_sweeps_inc_max": 7000,
        "beta_range_type": "default",
        "beta_range": None,
    },
    qpu_tokens=None,
)

Usage via LunaBench

# Example of adding SAGA_PL using the Luna backend as an algorithm to LunaBench
algorithms = {
    "SAGA_PL": {
        "location": "cloud",
        "provider": "dwave",
    },
}

Backends

This algorithm can be run on the following backends:

Luna

Simulated Annealing Genetic Algorithm Parallel temporarily unavailable on Luna
Luna provides powerful servers designed to run classical algorithms. These servers offer the computational capacity required to efficiently solve non-quantum tasks, ensuring high performance for optimization and other classical workloads.

Simulated Annealing Genetic Algorithm Pairwise

This solver is only available for commercial and academic licenses.
  • Short Name: SAGA_PW
  • Algorithm Type: Classical
  • Category: Classical
  • Native Input Format: QUBO
The SAGA algorithm uses 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. For SAGA, Simulated Annealing is used during the mutation phase.

Usage via LunaSolve

# Example of using SAGA_PW using the Luna backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="SAGA_PW",
    provider="dwave",
    solver_parameters={
        "p_size": 20,
        "p_inc_num": 5,
        "p_max": 160,
        "pct_random_states": 0.25,
        "mut_rate": 0.5,
        "rec_rate": 1,
        "rec_method": "random_crossover",
        "select_method": "simple",
        "target": None,
        "atol": 0.0,
        "rtol": 0.0,
        "timeout": 60.0,
        "max_iter": 100,
        "num_sweeps": 10,
        "num_sweeps_inc_factor": 1.2,
        "num_sweeps_inc_max": 7000,
        "beta_range_type": "default",
        "beta_range": None,
    },
    qpu_tokens=None,
)

Usage via LunaBench

# Example of adding SAGA_PW using the Luna backend as an algorithm to LunaBench
algorithms = {
    "SAGA_PW": {
        "location": "cloud",
        "provider": "dwave",
    },
}

Backends

This algorithm can be run on the following backends:

Luna

Simulated Annealing Genetic Algorithm Pairwise temporarily unavailable on Luna
Luna provides powerful servers designed to run classical algorithms. These servers offer the computational capacity required to efficiently solve non-quantum tasks, ensuring high performance for optimization and other classical workloads.

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