modcma 1.2.0

Package Containing Modular CMA-ES optimizer

---

The Modular CMA-ES is a Python and C++ package that provides a modular implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. This package allows you to create various algorithmic variants of CMA-ES by enabling or disabling different modules, offering flexibility and customization in evolutionary optimization. In addition to the CMA-ES, the library includes an implementation of the Matrix Adaptation Evolution Strategy (MA-ES) algorithm, which has similar emprical performance on most problems, but signifanctly lower runtime. All modules implemented are compatible with both the CMA-ES and MA-ES.

This implementation is based on the algorithm introduced in the paper "Evolving the Structure of Evolution Strategies. (2016)" by Sander van Rijn et. al. If you would like to cite this work in your research, please cite the paper: "Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules (2021)" by Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck.

This README provides a high level overview of the implemented modules, and provides some usage examples for both the Python-only and the C++-based versions of the framework.

Table of Contents

• Table of Contents
• Installation
  • Python Installation
  • Installation from source
• Usage
  • C++ Backend
    • High Level interface
    • Low Level interface
  • Tuning
    • Configuration Space Generation
    • Creating Settings from a Configuration
  • Integer Variables
  • Python-only (Legacy)
    • Ask–Tell Interface
• Modules
  • Matrix Adaptation
  • Active Update
  • Elitism
  • Orthogonal Sampling
  • Sequential Selection
  • Threshold Convergence
  • Sample Sigma
  • Quasi-Gaussian Sampling
  • Recombination Weights
  • Mirrored Sampling
  • Step size adaptation
  • Restart Strategy
  • Bound correction
  • Sample Transformer
  • Repelling Restart
  • Center Placement
• Citation
• License

Installation

You can install the Modular CMA-ES package using pip.

Python Installation

pip install modcma

Installation from source

If you want to work on a development version of the library, you should follow the following steps. A C++ compiler is required, and the following is valid for g++ (v11.1.0):

1. Clone the repository:

   git clone [e-mail]:IOHprofiler/ModularCMAES.git
   cd ModularCMAES
   

2. Install dependencies (in a virtual environment)

   python3 -m venv env
   source ./env/bin/activate
   pip install -r requirements.txt   
   

3. Compile the library, we can optionally install the package globally:

   python setup.py install
   

   or install in develop mode, which is recommended if one would like to actively develop the library:

   python setup.py develop 
   

4. Ensure all functionality works as expected by running the unittests:

   python -m unittest discover   
   

Usage

Note:
The C++ backend is the primary and recommended interface for ModularCMAES.
The pure Python implementation remains available for reference and educational purposes,
but is no longer actively developed and may not include all new module options or performance improvements.

C++ Backend

For performance, completeness, and modularity, the C++ backend is the recommended interface for ModularCMAES. It exposes all available modules and options through a direct Python binding.

High Level interface

In addition to the fully specified method described below, we can run an optimization via a friendly fmin interface:

x0 = [0, 1, 2, 3] # Location to start the search from
sigma0 = 0.234    # Initial estimate of the stepsize, try 0.3 * (ub - lb) if you're unsure
budget = 100      # Total number of function evaluations
xopt, fopt, evals, cma = c_maes.fmin(
    func, x0, sigma0, budget,
    # We can specify modules and setting values as keyword arguments
    active=True,
    target=10.0,
    cc=0.8  
    matrix_adaptation='NONE'
)

Note that the func, x0, sigma0 and budget arguments are required. Modules and settings can be specified via keyword arguments by they corresponding names in the Modules and Settings objects. Module options, such as matrix_adaptation in the above example, can be specified by their name as str.

Low Level interface

To run an optimization, first create a Modules object specifying which modules and options to enable. Then construct a Settings object (which defines problem dimension and strategy parameters), and pass it through a Parameters object to the optimizer:

from modcma import c_maes

# Instantiate module configuration
modules = c_maes.parameters.Modules()
modules.active = True
modules.matrix_adaptation = c_maes.parameters.MatrixAdaptationType.MATRIX

# Create Settings and Parameters objects
settings = c_maes.parameters.Settings(dim=10, modules=modules, sigma0=2.5)
parameters = c_maes.Parameters(settings)

# Instantiate the optimizer
cma = c_maes.ModularCMAES(parameters)

# Define objective function
def func(x):
    return sum(x**2)

# Run the optimization
cma.run(func)

The API provides fine-grained control over the optimization process. Instead of calling run, you can explicitly step through the algorithm:

while not cma.break_conditions():
    cma.step(func)

Or execute the internal components of each iteration separately:

while not cma.break_conditions():
    cma.mutate(func)
    cma.select()
    cma.recombine()
    cma.adapt()

This modularity allows experimentation with specific parts of the evolution strategy, such as custom selection, recombination, or adaptation routines.

---

Tuning

To facilitate automated hyperparameter tuning, ModularCMAES now provides functionality to create and manipulate configuration spaces directly compatible with popular optimization and AutoML tools, such as SMAC, BOHB. This functionality allows users to systematically explore both algorithmic module combinations and numerical hyperparameters (e.g., population size, learning rates, damping coefficients).

Configuration Space Generation

The configuration space is automatically derived from the available modules and tunable parameters via the function:

from modcma.cmaescpp import get_configspace

Usage:

from modcma.cmaescpp import get_configspace

# Create a configuration space for a 10-dimensional problem
cs = get_configspace(dim=10)

This function returns a ConfigSpace.ConfigurationSpace object containing:

• Categorical parameters for all available modules (e.g., mirrored sampling, restart strategy, bound correction).
• Numeric parameters for key internal strategy settings such as lambda0, mu0, sigma0, cs, cc, cmu, c1, and damps.
• A built-in constraint ensuring mu0 ≤ lambda0.
• Optionally, the configuration space can include only module-level options by setting add_popsize=False, add_sigma=False, add_learning_rates=False.

Example:

# Get only the module configuration space (no numeric parameters)
cs_modules = get_configspace(add_popsize=False, add_sigma=False, add_learning_rates=False)

Creating Settings from a Configuration

Once a configuration has been selected—either manually or from a tuner—the library provides a simple interface to construct a corresponding Settings object:

from modcma.cmaescpp import settings_from_config
from ConfigSpace import Configuration

# Sample or load a configuration
config = cs.sample_configuration()

# Or for defaults
default = cs.default_configuration()

# The config can be edited
config['sampler'] = 'HALTON'

# Convert the configuration to a Settings object
# Note that keyword arguments like lb in the next example, can be passed to settings like so
settings = settings_from_config(dim=10, config=config, lb=np.ones(10))

The resulting Settings object can then be passed directly to the C++ backend:

from modcma import c_maes

parameters = c_maes.Parameters(settings)
cma = c_maes.ModularCMAES(parameters)
cma.run(func)

Integer Variables

A rudimentary mechanism for dealing with integer variables is implemented, which applies a lower bound on sigma for integer coordinates and rounds them to the nearest integer before evaluation, inspired by http://www.cmap.polytechnique.fr/~nikolaus.hansen/marty2024lb+ic-cma.pdf.

An option can be provided to the Settings object or settings_from_dict/settings_from_config functions:

dim = 5
settings = Settings(
    dim,
    integer_variables=[1, 2] # A list or numpy array of integer indices
)

settings = settings_from_dict(
    dim, 
    integer_variables=list(range(dim)) # All variables are integer
)

---

Python-only (Legacy)

Legacy notice:
The Python-only implementation is no longer actively developed and does not include all features of the C++ version.
It remains available for experimentation and teaching purposes. A complete API documentation can be found here (under construction).

The Python interface provides a simple API and includes a convenience fmin function for optimizing a single objective function in one call:

from modcma import fmin
xopt, fopt, used_budget = fmin(func=sum, x0=[1, 2, 3, 4], budget=1000, active=True, sigma0=2.5)

The main class is ModularCMAES, which mimics the structure of the C++ version but runs entirely in Python:

from modcma import ModularCMAES
import numpy as np

def func(x: np.ndarray):
    return sum(x)

dim = 10
budget = 10_000

# Instantiate and run
cma = ModularCMAES(func, dim, budget=budget)
cma = cma.run()

You can also run the algorithm step by step:

while not cma.break_conditions():
    cma.step()

Or explicitly call each internal phase — analogous to the C++ interface:

while not cma.break_conditions():
    cma.mutate()
    cma.select()
    cma.recombine()
    cma.adapt()

---

Ask–Tell Interface

The Ask–Tell interface is only available in the Python implementation. It provides an alternative interaction model where function evaluations are managed externally. This is particularly useful for parallel, asynchronous, or expensive objective evaluations,
where you want to control when and how points are evaluated.

The optimizer generates candidate solutions via ask(), and their objective values are later supplied with tell().

from modcma import AskTellCMAES

def func(x):
    return sum(x**2)

# Instantiate an ask-tell CMA-ES optimizer
# Note: the objective function is not passed at construction
cma = AskTellCMAES(dim=10, budget=10_000, active=True)

while not cma.break_conditions():
    # Get a candidate solution
    xi = cma.ask()
    # Evaluate externally
    fi = func(xi)
    # Report the result back
    cma.tell(xi, fi)

This design provides flexibility when the evaluation process is nontrivial,
such as when objectives are computed through simulations, APIs, or distributed systems.
However, note that this interface is not available in the C++ backend.

Modules

The CMA-ES Modular package provides various modules, grouped into 13 categories. For each of these categories a given option can be selected, which can be arbitrarly combined. The following table lists the categories and the available options. Not all modules are available in both versions (i.e. some are only implemented in C++), an overview is given in the table. By default, the first option in the table is selected for a given category. Boolean modules, i.e. modules that only can be turned on or off are turned off by default.

| Category | Option | Python | C++ | |---|---|---|---| | [Matrix Adaptation](#matrix-adaptation) | COVARIANCE | :green_circle: | :green_circle: | | | MATRIX | :red_circle: | :green_circle: | | | SEPARABLE | :red_circle: | :green_circle: | | | NONE | :red_circle: | :green_circle: | | | CHOLESKY | :red_circle: | :green_circle: | | | CMSA | :red_circle: | :green_circle: | | | NATURAL_GRADIENT | :red_circle: | :green_circle: | | [Active Update](#active-update) | Off/On | :green_circle: | :green_circle: | | [Elitism](#elitism) | Off/On | :green_circle: | :green_circle: | | [Orthogonal Sampling](#orthogonal-sampling) | Off/On | :green_circle: | :green_circle: | | [Sequential Selection](#sequential-selection) | Off/On | :green_circle: | :green_circle: | | [Threshold Convergence](#threshold-convergence) | Off/On | :green_circle: | :green_circle: | | [Sample Sigma](#sample-sigma) | Off/On | :green_circle: | :green_circle: | | [Base Sampler](#base-sampler) | GAUSSIAN | :green_circle: | :red_circle: *(use Sample Transformer = GAUSSIAN)* | | | SOBOL | :green_circle: | :green_circle: | | | HALTON | :green_circle: | :green_circle: | | | UNIFORM | :red_circle: | :green_circle: | | [Sample Transformer](#sample-transformer) | GAUSSIAN | :red_circle: | :green_circle: | | | SCALED_UNIFORM | :red_circle: | :green_circle: | | | LAPLACE | :red_circle: | :green_circle: | | | LOGISTIC | :red_circle: | :green_circle: | | | CAUCHY | :red_circle: | :green_circle: | | | DOUBLE_WEIBULL | :red_circle: | :green_circle: | | [Recombination Weights](#recombination-weights) | DEFAULT | :green_circle: | :green_circle: | | | EQUAL | :green_circle: | :green_circle: | | | 1/2^λ | :green_circle: | :red_circle: *(use EXPONENTIAL instead)* | | | EXPONENTIAL | :red_circle: | :green_circle: | | [Mirrored Sampling](#mirrored-sampling) | NONE | :green_circle: | :green_circle: | | | MIRRORED | :green_circle: | :green_circle: | | | PAIRWISE | :green_circle: | :green_circle: | | [Step size adaptation](#step-size-adaptation) | CSA | :green_circle: | :green_circle: | | | TPA | :green_circle: | :green_circle: | | | MSR | :green_circle: | :green_circle: | | | XNES | :green_circle: | :green_circle: | | | MXNES | :green_circle: | :green_circle: | | | LPXNES | :green_circle: | :green_circle: | | | PSR | :green_circle: | :green_circle: | | | SR | :red_circle: | :green_circle: | | | SA | :red_circle: | :green_circle: | | [Restart Strategy](#restart-strategy) | NONE | :green_circle: | :green_circle: | | | RESTART | :green_circle: | :green_circle: | | | IPOP | :green_circle: | :green_circle: | | | BIPOP | :green_circle: | :green_circle: | | | STOP | :red_circle: | :green_circle: | | [Bound correction](#bound-correction) | NONE | :green_circle: | :green_circle: | | | SATURATE | :green_circle: | :green_circle: | | | MIRROR | :green_circle: | :green_circle: | | | COTN | :green_circle: | :green_circle: | | | TOROIDAL | :green_circle: | :green_circle: | | | UNIFORM_RESAMPLE | :green_circle: | :green_circle: | | | RESAMPLE | :red_circle: | :green_circle: | | [Repelling Restart](#repelling-restart) | Off/On | :red_circle: | :green_circle: | | [Center Placement](#center-placement) | X0 | :red_circle: | :green_circle: | | | ZERO | :red_circle: | :green_circle: | | | UNIFORM | :red_circle: | :green_circle: | | | CENTER | :red_circle: | :green_circle: |

Notes

• In C++, BaseSampler generates uniform points in [0,1)^d; the actual search-space distribution is controlled by SampleTranformerType (e.g., GAUSSIAN, CAUCHY).
• Python’s “1/2^λ” recombination weights correspond to C++’s EXPONENTIAL.
• New C++-only modules: repelling_restart (bool), center_placement (enum), and extended ssa, bound_correction, and sampling options.

Matrix Adaptation

The ModularCMAES can be turned into an implementation of the (fast)-MA-ES algortihm by changing the matrix_adaptation option from COVARIANCE to MATRIX in the Modules object. This is currently only available in the C++ version of the framework. An example of specifying this, using the required MatrixAdaptationType enum:

...
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.COVARIANCE
# or for MA-ES
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.MATRIX
# We can also only perform step-size-adaptation
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.NONE
# Or use the seperable CMA-ES
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.SEPARABLE
# Other variants:
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.CHOLESKY
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.CMSA
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.COVARIANCE_NO_EIGV
modules.matrix_adaptation = c_maes.options.MatrixAdaptationType.NATURAL_GRADIENT

Active Update

In the standard update of the covariance matrix C in the CMA-ES algorithm, only the most successful mutations are considered. However, the Active Update, introduced by Jastrebski et al., offers an alternative approach. This module adapts the covariance matrix by incorporating the least successful individuals with negative weights in the update process.

For the Python only version, this can be enabled by passing the option active=True:

cma = ModularCMAES(func, dim, active=True)

For the C++ version, this can be done by setting the appropriate value in the Modules object:

...
modules.active = True

Elitism

When this option is selected, (𝜇 + 𝜆)-selection instead of (𝜇, 𝜆)-selection is enabled. This can be usefull to speed up convergence on unimodal problems, but can have a negative impact on population diversity.

For the C++ version, this can be done by setting the appropriate value in the Modules object:

...
modules.elitist = True

For the Python only version, this can be enabled by passing the option elitist=True:

cma = ModularCMAES(func, dim, elitist=True)

Orthogonal Sampling

Orthogonal Sampling was introduced by Wang et al. as an extension of Mirrored Sampling. This method improves sampling by ensuring that the newly sampled points in the population are orthonormalized using a Gram-Schmidt procedure.

For the Python only version, this can be enabled by passing the option orthogonal=True:

cma = ModularCMAES(func, dim, orthogonal=True)

And for C++:

...
modules.orthogonal = True

Sequential Selection

Sequential Selection option offers an alternative approach to selection, originally proposed by Brockhoff et al., which optimizes the use of objective function evaluations by immediately ranking and comparing candidate solutions with the current best solution. Then, whenever more than $\mu$ individuals have been sampled that improve on the current best found solution, no more additional function evaluations are performed.

For the Python only version, this can be enabled by passing the option sequential=True:

cma = ModularCMAES(func, dim, sequential=True)

And for C++:

...
modules.sequential_selection = True

Threshold Convergence

In evolutionary strategies (ES), balancing exploration and exploitation is a critical challenge. The Threshold Convergence option, proposed by Piad et al. [25], provides a method to address this issue. It aims to prolong the exploration phase of evolution by requiring mutation vectors to reach a specific length threshold. This threshold gradually decreases over successive generations to transition into local search.

For the Python only version, this can be enabled by passing the option threshold_convergence=True:

cma = ModularCMAES(func, dim, threshold_convergence=True)

And for C++:

...
modules.threshold_convergence = True

Sample Sigma

A method based on self-adaptation by co-evolution as seen in classical evolution strategies, where for each candidate solution the step size is sampled seperately from a lognormal distribution based on the global step size $\sigma$.

For the Python only version, this can be enabled by passing the option sample_sigma=True:

cma = ModularCMAES(func, dim, sample_sigma=True)

And for C++:

...
modules.sample_sigma = True

Quasi-Gaussian Sampling

Instead of performing the simple random sampling from the multivariate Gaussian, new solutions can alternatively be drawn from quasi-random sequences (a.k.a. low-discrepancy sequences). We implemented two options for this module, the Halton and Sobol sequences.

This can be selected by setting the base_sampler="sobol" or base_sampler="halton" in the Python only version:

cma = ModularCMAES(func, dim, base_sampler="gaussian")
# or 
cma = ModularCMAES(func, dim, base_sampler="sobol")
# or 
cma = ModularCMAES(func, dim, base_sampler="halton")

For C++, the BaseSampler enum should be provided to the sampler member of the Modules object:

...
modules.sampler = c_maes.options.BaseSampler.UNIFORM
# or
modules.sampler = c_maes.options.BaseSampler.SOBOL
# or
modules.sampler = c_maes.options.BaseSampler.HALTON

Here, this works slightly different. The base sampler only defined the method for generating uniform sampling in a $[0,1)^d$ hyperbox. In order to define that a, for example, Gaussian distribution (this is default) should be used when running the algorithm, we need to specify the sample_transformation type:

modules.sample_transformation = c_maes.options.SampleTranformerType.GAUSSIAN
# or 
modules.sample_transformation = c_maes.options.SampleTranformerType.SCALED_UNIFORM
# or 
modules.sample_transformation = c_maes.options.SampleTranformerType.LAPLACE
# or 
modules.sample_transformation = c_maes.options.SampleTranformerType.LOGISTIC
# or 
modules.sample_transformation = c_maes.options.SampleTranformerType.CAUCHY
# or 
modules.sample_transformation = c_maes.options.SampleTranformerType.DOUBLE_WEIBULL

Recombination Weights

We implemented three different variants of the recombination weights used in the update of the strategy parameters, default, equal and $1/2\lambda$.

This can be selected by setting the weights_option="sobol" or weights_option="halton" in the Python only version:

cma = ModularCMAES(func, dim, weights_option="default")
# or 
cma = ModularCMAES(func, dim, weights_option="equal")
# or 
cma = ModularCMAES(func, dim, weights_option="1/2^lambda")

For C++, the RecombinationWeights enum should be provided to the weights member of the Modules object:

...
modules.weights = c_maes.options.RecombinationWeights.DEFAULT
# or
modules.weights = c_maes.options.RecombinationWeights.EQUAL
# or
modules.weights = c_maes.options.RecombinationWeights.EXPONENTIAL

Mirrored Sampling

Mirrored Sampling, introduced by Brockhoff et al., aims to create a more evenly spaced sample of the search space. In this technique, half of the mutation vectors are drawn from the normal distribution, while the other half are the mirror image of the preceding random vectors. When using Pairwise Selection in combination with Mirrored Sampling, only the best point from each mirrored pair is selected for recombination. This approach ensures that the mirrored points do not cancel each other out during recombination. This module has three options, off, on and on + pairwise.

For Python, we can add the option mirrored="mirrored" or mirrored="mirrored pairwise".

cma = ModularCMAES(func, dim, mirrored=None)
# or
cma = ModularCMAES(func, dim, mirrored="mirrored")
# or 
cma = ModularCMAES(func, dim, mirrored="pairwise")

For C++ this can be configured using the c_maes.options.Mirror enum:

...
modules.mirrored = c_maes.options.Mirror.NONE
# or 
modules.mirrored = c_maes.options.Mirror.MIRRORED
# or 
modules.mirrored = c_maes.options.Mirror.PAIRWISE

Step size adaptation

Several methods for performing step size adaptation have been implemented in the framework. For more details on the implemented methods, we refer the interested reader to our 2021 paper.

The availble options for step_size_adaptation for the Python only interface are: {"csa", "tpa", "msr", "xnes", "m-xnes", "lp-xnes", "psr"}, for which one can be selected and pased to the algortihms als active option, for example:

cma = ModularCMAES(func, dim, step_size_adaptation="csa")
# or
cma = ModularCMAES(func, dim, step_size_adaptation="msr")

The same options are available for the C++ version, but should be passed via the StepSizeAdaptation enum, which has the following values available: {CSA, TPA, MSR, XNES, MXNEs, LPXNES, PSR} and can be configured via the ssa option:

...
modules.ssa = c_maes.options.StepSizeAdaptation.CSA
# or 
modules.ssa = c_maes.options.StepSizeAdaptation.MSR

Restart Strategy

Restarting an optimization algorithm, like CMA-ES, can be an effective way to overcome stagnation in the optimization process. The Modular CMA-ES package offers three restart strategies to help in such scenarios. The first restart option just restarts the algorithm. When IPOP is enabled, the algorithm employs a restart strategy that increases the size of the population after every restart. BIPOP on the other hand, not only changes the size of the population after a restart but alternates between larger and smaller population sizes.

For the Python only interface, this option can be configured with 4 values {None, "restart", "IPOP", "BIPOP"}:

cma = ModularCMAES(func, dim, local_restart=None)
# or
cma = ModularCMAES(func, dim, local_restart="IPOP")

For the C++ version these should be passed via the RestartStrategy enum, which has the following values available: {NONE, RESTART, IPOP, BIPOP, STOP} and can be configured via the restart_strategy option:

...
modules.restart_strategy = c_maes.options.RestartStrategy.NONE
# or 
modules.restart_strategy = c_maes.options.RestartStrategy.IPOP

Note that the C++ version has an addtional option here, STOP, which forces the algortihm to stop whenever a restart condition is met (not to be confused with a break condition). The C++ version also offers fine control over when a restart happens. The Parameters object has an criteria member, of which the items member defines a list of Criterion objects, which are triggers for when a restart should happen. This list can be freely changed, and default items can be deleted and modified if desired. For example, cma.p.criteria.items = [], clears this list entirely, and no restarts will happen. Several of the currently defined Criterion object can also be modified, often this can be controlled by setting the tolerance parameter. For example, for the MinSigma criterion, you can set c_maes.restart.MinSigma.tolerance = 1 to ensure a parameter value of sigma below 1 triggers a restart. Note that this is a static varaible, so modifying it in this fashion sets this for all instances of c_maes.restart.MinSigma. Alternatively, it is possible to define a custom Criterion like so:

class MyCriterion(modcma.restart.Criterion):
    def __init__(self):
        super().__init__("MyCriterionName")
    
    def on_reset(self, par: modcma.Parameters):
        """Called when a restart happens (also at the start)"""
    
    def update(self, par: modcma.Parameters):
        """Called after each iteration, needs to modify self.met"""
        self.met = True

# Python needs a reference to this object, 
# so you CANNOT create it in place, i.e. 
# cma.p.criteria.items = [MyCriterion()] 
# will produce an error
c = MyCriterion() 
cma.p.criteria.items = [c]

Bound correction

Several methods for performing bound correction have been implemented in the framework. For more details on the implemented methods, we refer the interested reader to our 2021 paper.

The availble options for bound_correction for the Python only interface are: {None, "saturate", "unif_resample", "COTN", "toroidal", "mirror"}, for which one can be selected and pased to the algortihms als active option, for example:

cma = ModularCMAES(func, dim, bound_correction=None)
# or
cma = ModularCMAES(func, dim, bound_correction="saturate")

The same options are available for the C++ version, but should be passed via the CorrectionMethod enum, which has the following values available {NONE, SATURATE, UNIFORM_RESAMPLE, COTN, TOROIDAL MIRROR} and can be configure via the bound_correction option:

...
modules.bound_correction = c_maes.options.CorrectionMethod.NONE
# or 
modules.bound_correction = c_maes.options.CorrectionMethod.SATURATE

Sample Transformer

Controls the transformation from a base uniform sampler in [0,1)^d to a target search-space distribution (e.g., GAUSSIAN, CAUCHY, LAPLACE, …) in the C++ backend. See the paper. By default, this performs a transform to a standard Gaussian.

Repelling Restart

C++-only boolean module to bias restarts away from previously explored regions (helpful in multimodal settings). See the paper

Center Placement

C++-only enum controlling the initial center of mass of the sampling distribution (X0, ZERO, UNIFORM, CENTER) on restart, iniatiated by a restart_strategy. The options are:

• X0 sets the intial center to x0 on every restart
• ZERO set the intial center to the all-zero vector
• UNIFORM picks a uniform random location inside the search space
• CENTER sets the center to the precise center of the search space.

Citation

The following BibTex entry can be used for the citation.

@inproceedings{denobel2021,
   author = {de Nobel, Jacob and Vermetten, Diederick and Wang, Hao and Doerr, Carola and B\"{a}ck, Thomas},
   title = {Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules},
   year = {2021},
   isbn = {9781450383516},
   publisher = {Association for Computing Machinery},
   address = {New York, NY, USA},
   url = {https://doi.org/10.1145/3449726.3463167},
   doi = {10.1145/3449726.3463167},
   booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
   pages = {1375–1384},
   numpages = {10},
   location = {Lille, France},
   series = {GECCO '21}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

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