Limited Memory Covariance Matrix Adaptation Evolution Strategy (LMCMAES)

class pypop7.optimizers.es.lmcmaes.LMCMAES(problem, options)

Limited-Memory Covariance Matrix Adaptation Evolution Strategy (LMCMAES).

Note

For perhaps better performance, please use its lateset version called LMCMA. Here we include it mainly for benchmarking purpose.

Parameters:

• problem (dict) –

  problem arguments with the following common settings (keys):

  • ’fitness_function’ - objective function to be minimized (func),

  • ’ndim_problem’ - number of dimensionality (int),

  • ’upper_boundary’ - upper boundary of search range (array_like),

  • ’lower_boundary’ - lower boundary of search range (array_like).

• options (dict) –

  optimizer options with the following common settings (keys):

  • ’max_function_evaluations’ - maximum of function evaluations (int, default: np.Inf),

  • ’max_runtime’ - maximal runtime to be allowed (float, default: np.Inf),

  • ’seed_rng’ - seed for random number generation needed to be explicitly set (int);

  and with the following particular settings (keys):

  • ’sigma’ - initial global step-size, aka mutation strength (float),

  • ’mean’ - initial (starting) point, aka mean of Gaussian search distribution (array_like),

    • if not given, it will draw a random sample from the uniform distribution whose search range is bounded by problem[‘lower_boundary’] and problem[‘upper_boundary’].

  • ’m’ - number of direction vectors (int, default: 4 + int(3*np.log(problem[‘ndim_problem’]))),

  • ’n_steps’ - target number of generations between vectors (int, default: options[‘m’]),

  • ’c_c’ - learning rate for evolution path update (float, default: 1.0/options[‘m’]).

  • ’c_1’ - learning rate for covariance matrix adaptation (float, default: 1.0/(10.0*np.log(problem[‘ndim_problem’] + 1.0))),

  • ’c_s’ - learning rate for population success rule (float, default: 0.3),

  • ’d_s’ - delay rate for population success rule (float, default: 1.0),

  • ’z_star’ - target success rate for population success rule (float, default: 0.25),

  • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 4 + int(3*np.log(problem[‘ndim_problem’]))),

  • ’n_parents’ - number of parents, aka parental population size (int, default: int(options[‘n_individuals’]/2)).

Examples

Use the optimizer to minimize the well-known test function Rosenbrock:

>>> import numpy
>>> from pypop7.benchmarks.base_functions import rosenbrock  # function to be minimized
>>> from pypop7.optimizers.es.lmcmaes import LMCMAES
>>> problem = {'fitness_function': rosenbrock,  # define problem arguments
...            'ndim_problem': 2,
...            'lower_boundary': -5*numpy.ones((2,)),
...            'upper_boundary': 5*numpy.ones((2,))}
>>> options = {'max_function_evaluations': 5000,  # set optimizer options
...            'seed_rng': 2022,
...            'mean': 3*numpy.ones((2,)),
...            'sigma': 0.1}  # the global step-size may need to be tuned for better performance
>>> lmcmaes = LMCMAES(problem, options)  # initialize the optimizer class
>>> results = lmcmaes.optimize()  # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"LMCMAES: {results['n_function_evaluations']}, {results['best_so_far_y']}")
LMCMAES: 5000, 4.590739937885748e-16

For its correctness checking of coding, refer to this code-based repeatability report for more details.

c_c

learning rate for evolution path update.

Type:

float

c_s

learning rate for population success rule.

Type:

float

c_1

learning rate for covariance matrix adaptation.

Type:

float

d_s

delay rate for population success rule.

Type:

float

m

number of direction vectors.

Type:

int

mean

initial (starting) point, aka mean of Gaussian search distribution.

Type:

array_like

n_individuals

number of offspring, aka offspring population size.

Type:

int

n_parents

number of parents, aka parental population size.

Type:

int

n_steps

target number of generations between vectors.

Type:

int

sigma

initial global step-size, aka mutation strength.

Type:

float

z_star

target success rate for population success rule.

Type:

float

References

Loshchilov, I., 2014, July. A computationally efficient limited memory CMA-ES for large scale optimization. In Proceedings of Annual Conference on Genetic and Evolutionary Computation (pp. 397-404). ACM. https://dl.acm.org/doi/abs/10.1145/2576768.2598294

See the official C++ version from Loshchilov: https://sites.google.com/site/lmcmaeses/