cma.constraints_handler.ConstrainedFitnessAL - Construct an unconstrained objective function from constraints.cma.constraints_handler.ConstrainedSolutionsArchive - Biobjective Pareto archive to store some Pareto optimal solutions for constrained optimization.cma.constraints_handler.CountLastSameChanges - An array/list of successive same-sign counts.cma.fitness_models.SurrogatePopulation.EvaluationManager - Manage incremental evaluation of a population of solutions.cma.interfaces.EvalParallel - allow construct with EvalParallel(fun) as eval_all:cma.utilities.python3for2.abc.MutableMappingcma.utilities.utils.DerivedDictBase - for conveniently adding methods/functionality to a dictionary.cma.utilities.utils.SolutionDict - dictionary with computation of an hash key.cma.evolution_strategy._CMASolutionDict_functional - No class docstring; 0/2 instance variable, 1/2 method documentedcma.utilities.utils.ShowInFolder - callable instance to save and show figures from matplotlib.collections.defaultdictcma.utilities.utils.DataDict - a dictionary of lists (of data)collections.dequecma.constraints_handler.DequeCDF - a queue with (in case) element-wise cdf computation.collections.namedtuple('CMAEvolutionStrategyResult', ['xbest', 'fbest',
'evals_best', 'evaluations', 'iterations', 'xfavorite', 'stds', 'stop'])cma.evolution_strategy.CMAEvolutionStrategyResult - A results tuple from CMAEvolutionStrategy property result.dictcma.evolution_strategy._CMASolutionDict_empty - a hack to get most code examples runningcma.evolution_strategy._CMAStopDict - keep and update a termination condition dictionary.cma.evolution_strategy.CMAOptions - a dictionary with the available options and their default values for class CMAEvolutionStrategy.cma.utilities.utils.DictClass - A class wrapped over dict to use class .-notation.cma.utilities.utils.DictFromTagsInString - read from a string or file all key-value pairs within all <python>...</python> tags and return a dict.listcma.constraints_handler.LoggerList - list of loggers with plot methodcma.fitness_transformations.ComposedFunction - compose an arbitrary number of functions.cma.fitness_transformations.FBoundTransform - shortcut for ComposedFunction([f, BoundTransform(bounds).transform]), see also below.cma.fitness_transformations.FixVariables - Insert variables with given values, thereby reducing the dimensionality of the resulting composed function.cma.fitness_transformations.IntegerMixedFunction - compose fitness function with some integer variables.cma.fitness_transformations.Rotated - return a rotated version of a function for testing purpose.cma.fitness_transformations.ScaleCoordinates - compose a (fitness) function with a preceding scaling and offset.cma.fitness_transformations.Shifted - compose a function with a shift in x-space.cma.purecma.SquareMatrix - rudimental square matrix classcma.purecma.DecomposingPositiveMatrix - Symmetric matrix maintaining its own eigendecomposition.cma.recombination_weights.RecombinationWeights - a list of decreasing (recombination) weight values.cma.utilities.utils.ExclusionListOfVectors - For delayed selective mirrored samplingcma.utilities.utils.ListOfCallables - A list of callables that can be called like a single callable.cma.utilities.utils.MoreToWrite - make sure that this list does not grow unboundedobjectcma.bbobbenchmarks.AbstractTestFunction - Abstract class for test functions.cma.bbobbenchmarks.BBOBFunction - Abstract class of BBOB test functions.cma.bbobbenchmarks._F8F2 - Abstract F8F2 sum of Griewank-Rosenbrock 2-D blockscma.bbobbenchmarks.F125 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with Gauss noisecma.bbobbenchmarks.F126 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with uniform noisecma.bbobbenchmarks.F127 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with seldom Cauchy noisecma.bbobbenchmarks.F19 - F8F2 sum of Griewank-Rosenbrock 2-D blocks, noise-freecma.bbobbenchmarks._FDiffPow - Abstract Sum of different powers, between x^2 and x^6.cma.bbobbenchmarks.F119 - Sum of different powers with Gauss noise, between x^2 and x^6cma.bbobbenchmarks.F120 - Sum of different powers with uniform noise, between x^2 and x^6cma.bbobbenchmarks.F121 - Sum of different powers with seldom Cauchy noise, between x^2 and x^6cma.bbobbenchmarks.F14 - Sum of different powers, between x^2 and x^6, noise-freecma.bbobbenchmarks._FEllipsoid - Abstract Ellipsoid with monotone transformation.cma.bbobbenchmarks.F10 - Ellipsoid with monotone transformation, condition 1e6cma.bbobbenchmarks.F116 - Ellipsoid with Gauss noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F117 - Ellipsoid with uniform noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F118 - Ellipsoid with Cauchy noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks._FGallagher - Abstract Gallagher with nhighpeaks Gaussian peaks, condition up to 1000, one global rotationcma.bbobbenchmarks.F128 - Gallagher with 101 Gaussian peaks with Gauss noise, condition up to 1000, one global rotationcma.bbobbenchmarks.F129 - Gallagher with 101 Gaussian peaks with uniform noise, condition up to 1000, one global rotationcma.bbobbenchmarks.F130 - Gallagher with 101 Gaussian peaks with seldom Cauchy noise, condition up to 1000, one global rotationcma.bbobbenchmarks.F21 - Gallagher with 101 Gaussian peaks, condition up to 1000, one global rotation, noise-freecma.bbobbenchmarks.F22 - Gallagher with 21 Gaussian peaks, condition up to 1000, one global rotationcma.bbobbenchmarks._FRosenbrock - Abstract Rosenbrock, non-rotatedcma.bbobbenchmarks.F104 - Rosenbrock non-rotated with moderate Gauss noisecma.bbobbenchmarks.F105 - Rosenbrock non-rotated with moderate uniform noisecma.bbobbenchmarks.F106 - Rosenbrock non-rotated with moderate Cauchy noisecma.bbobbenchmarks.F110 - Rosenbrock non-rotated with Gauss noisecma.bbobbenchmarks.F111 - Rosenbrock non-rotated with uniform noisecma.bbobbenchmarks.F112 - Rosenbrock non-rotated with Cauchy noisecma.bbobbenchmarks.F8 - Rosenbrock noise-freecma.bbobbenchmarks._FSchaffersF7 - Abstract Schaffers F7 with asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F122 - Schaffers F7 with Gauss noise, with asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F123 - Schaffers F7 with uniform noise, asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F124 - Schaffers F7 with seldom Cauchy noise, asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F17 - Schaffers F7 with asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F18 - Schaffers F7 with asymmetric non-linear transformation, condition 1000cma.bbobbenchmarks._FSphere - Abstract Sphere function.cma.bbobbenchmarks.F1 - Noise-free Sphere functioncma.bbobbenchmarks.F101 - Sphere with moderate Gauss noisecma.bbobbenchmarks.F102 - Sphere with moderate uniform noisecma.bbobbenchmarks.F103 - Sphere with moderate Cauchy noisecma.bbobbenchmarks.F107 - Sphere with Gauss noisecma.bbobbenchmarks.F108 - Sphere with uniform noisecma.bbobbenchmarks.F109 - Sphere with Cauchy noisecma.bbobbenchmarks._FStepEllipsoid - Abstract Step-ellipsoid, condition 100cma.bbobbenchmarks.F113 - Step-ellipsoid with gauss noise, condition 100cma.bbobbenchmarks.F114 - Step-ellipsoid with uniform noise, condition 100cma.bbobbenchmarks.F115 - Step-ellipsoid with Cauchy noise, condition 100cma.bbobbenchmarks.F7 - Step-ellipsoid, condition 100, noise-freecma.bbobbenchmarks.BBOBCauchyFunction - Class of the Cauchy noise functions of BBOB.cma.bbobbenchmarks.F103 - Sphere with moderate Cauchy noisecma.bbobbenchmarks.F106 - Rosenbrock non-rotated with moderate Cauchy noisecma.bbobbenchmarks.F109 - Sphere with Cauchy noisecma.bbobbenchmarks.F112 - Rosenbrock non-rotated with Cauchy noisecma.bbobbenchmarks.F115 - Step-ellipsoid with Cauchy noise, condition 100cma.bbobbenchmarks.F118 - Ellipsoid with Cauchy noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F121 - Sum of different powers with seldom Cauchy noise, between x^2 and x^6cma.bbobbenchmarks.F124 - Schaffers F7 with seldom Cauchy noise, asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F127 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with seldom Cauchy noisecma.bbobbenchmarks.F130 - Gallagher with 101 Gaussian peaks with seldom Cauchy noise, condition up to 1000, one global rotationcma.bbobbenchmarks.BBOBGaussFunction - Class of the Gauss noise functions of BBOB.cma.bbobbenchmarks.F101 - Sphere with moderate Gauss noisecma.bbobbenchmarks.F104 - Rosenbrock non-rotated with moderate Gauss noisecma.bbobbenchmarks.F107 - Sphere with Gauss noisecma.bbobbenchmarks.F110 - Rosenbrock non-rotated with Gauss noisecma.bbobbenchmarks.F113 - Step-ellipsoid with gauss noise, condition 100cma.bbobbenchmarks.F116 - Ellipsoid with Gauss noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F119 - Sum of different powers with Gauss noise, between x^2 and x^6cma.bbobbenchmarks.F122 - Schaffers F7 with Gauss noise, with asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F125 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with Gauss noisecma.bbobbenchmarks.F128 - Gallagher with 101 Gaussian peaks with Gauss noise, condition up to 1000, one global rotationcma.bbobbenchmarks.BBOBNfreeFunction - Class of the noise-free functions of BBOB.cma.bbobbenchmarks._FTemplate - Template based on F1cma.bbobbenchmarks.F1 - Noise-free Sphere functioncma.bbobbenchmarks.F10 - Ellipsoid with monotone transformation, condition 1e6cma.bbobbenchmarks.F11 - Discus (tablet) with monotone transformation, condition 1e6cma.bbobbenchmarks.F12 - Bent cigar with asymmetric space distortion, condition 1e6cma.bbobbenchmarks.F13 - Sharp ridgecma.bbobbenchmarks.F14 - Sum of different powers, between x^2 and x^6, noise-freecma.bbobbenchmarks.F15 - Rastrigin with asymmetric non-linear distortion, "condition" 10cma.bbobbenchmarks.F16 - Weierstrass, condition 100cma.bbobbenchmarks.F17 - Schaffers F7 with asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F18 - Schaffers F7 with asymmetric non-linear transformation, condition 1000cma.bbobbenchmarks.F19 - F8F2 sum of Griewank-Rosenbrock 2-D blocks, noise-freecma.bbobbenchmarks.F2 - Separable ellipsoid with monotone transformationcma.bbobbenchmarks.F20 - Schwefel with tridiagonal variable transformationcma.bbobbenchmarks.F21 - Gallagher with 101 Gaussian peaks, condition up to 1000, one global rotation, noise-freecma.bbobbenchmarks.F22 - Gallagher with 21 Gaussian peaks, condition up to 1000, one global rotationcma.bbobbenchmarks.F23 - Katsuura functioncma.bbobbenchmarks.F24 - Lunacek bi-Rastrigin, condition 100cma.bbobbenchmarks.F3 - Rastrigin with monotone transformation separable "condition" 10cma.bbobbenchmarks.F4 - skew Rastrigin-Bueche, condition 10, skew-"condition" 100cma.bbobbenchmarks.F5 - Linear slopecma.bbobbenchmarks.F6 - Attractive sector functioncma.bbobbenchmarks.F7 - Step-ellipsoid, condition 100, noise-freecma.bbobbenchmarks.F8 - Rosenbrock noise-freecma.bbobbenchmarks.F9 - Rosenbrock, rotatedcma.bbobbenchmarks.BBOBUniformFunction - Class of the uniform noise functions of BBOB.cma.bbobbenchmarks.F102 - Sphere with moderate uniform noisecma.bbobbenchmarks.F105 - Rosenbrock non-rotated with moderate uniform noisecma.bbobbenchmarks.F108 - Sphere with uniform noisecma.bbobbenchmarks.F111 - Rosenbrock non-rotated with uniform noisecma.bbobbenchmarks.F114 - Step-ellipsoid with uniform noise, condition 100cma.bbobbenchmarks.F117 - Ellipsoid with uniform noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F120 - Sum of different powers with uniform noise, between x^2 and x^6cma.bbobbenchmarks.F123 - Schaffers F7 with uniform noise, asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F126 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with uniform noisecma.bbobbenchmarks.F129 - Gallagher with 101 Gaussian peaks with uniform noise, condition up to 1000, one global rotationcma.bbobbenchmarks.BBOBUniformFunction - Class of the uniform noise functions of BBOB.cma.bbobbenchmarks.F102 - Sphere with moderate uniform noisecma.bbobbenchmarks.F105 - Rosenbrock non-rotated with moderate uniform noisecma.bbobbenchmarks.F108 - Sphere with uniform noisecma.bbobbenchmarks.F111 - Rosenbrock non-rotated with uniform noisecma.bbobbenchmarks.F114 - Step-ellipsoid with uniform noise, condition 100cma.bbobbenchmarks.F117 - Ellipsoid with uniform noise, monotone x-transformation, condition 1e4cma.bbobbenchmarks.F120 - Sum of different powers with uniform noise, between x^2 and x^6cma.bbobbenchmarks.F123 - Schaffers F7 with uniform noise, asymmetric non-linear transformation, condition 10cma.bbobbenchmarks.F126 - F8F2 sum of Griewank-Rosenbrock 2-D blocks with uniform noisecma.bbobbenchmarks.F129 - Gallagher with 101 Gaussian peaks with uniform noise, condition up to 1000, one global rotationcma.constraints_handler.AugmentedLagrangian - Augmented Lagrangian with adaptation of the coefficientscma.constraints_handler.BoundaryHandlerBase - quick hack versatile base classcma.constraints_handler.BoundNone - no boundariescma.constraints_handler.BoundPenalty - Compute a bound penalty and update coordinate-wise penalty weights.cma.constraints_handler.BoundTransform - Handle boundaries by a smooth, piecewise linear and quadratic transformation into the feasible domain.cma.constraints_handler.PopulationEvaluator - evaluate and store f- and g-values of a population in attributes F and G.cma.evolution_strategy._CMAParameters - strategy parameters like population size and learning rates.cma.evolution_strategy.MetaParameters - collection of many meta parameters.cma.fitness_functions.FitnessFunctions - collection of objective functions.cma.fitness_models.LQModel - Up to a full quadratic model using the pseudo inverse to compute the model coefficients.cma.fitness_models.ModelInjectionCallback - inject model.xopt and decrease sigma if mean is close to model.xopt.cma.fitness_models.SurrogatePopulation - surrogate f-values for a population.cma.fitness_models.Tau - placeholder to store Kendall tau related thingscma.fitness_transformations.Function - a declarative base class, indicating that a derived class instance "is" a (fitness/objective) function.cma.fitness_transformations.ComposedFunction - compose an arbitrary number of functions.cma.fitness_transformations.FBoundTransform - shortcut for ComposedFunction([f, BoundTransform(bounds).transform]), see also below.cma.fitness_transformations.FixVariables - Insert variables with given values, thereby reducing the dimensionality of the resulting composed function.cma.fitness_transformations.IntegerMixedFunction - compose fitness function with some integer variables.cma.fitness_transformations.Rotated - return a rotated version of a function for testing purpose.cma.fitness_transformations.ScaleCoordinates - compose a (fitness) function with a preceding scaling and offset.cma.fitness_transformations.Shifted - compose a function with a shift in x-space.cma.fitness_transformations.Expensify - Add waiting time to each evaluation, to simulate "expensive" behaviorcma.fitness_transformations.GlueArguments - deprecated, use functools.partial or cma.fitness_transformations.partial instead, which has the same functionality and interface.cma.fitness_transformations.NoisyFitness - apply noise via f += rel_noise(dim) * f + abs_noise(dim)cma.fitness_transformations.SomeNaNFitness - transform fitness_function to return sometimes NaNcma.fitness_transformations.StackFunction - a function that returns f1(x[:n1]) + f2(x[n1:]).cma.interfaces.BaseDataLogger - abstract base class for a data logger that can be used with an OOOptimizer.cma.logger.CMADataLogger - data logger for class CMAEvolutionStrategy.cma.purecma.CMAESDataLogger - data logger for class CMAES, that can record and plot data.cma.interfaces.OOOptimizer - abstract base class for an Object Oriented Optimizer interface.cma.evolution_strategy.CMAEvolutionStrategy - CMA-ES stochastic optimizer class with ask-and-tell interface.cma.purecma.CMAES - class for non-linear non-convex numerical minimization with CMA-ES.cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass - yet versatile base class to replace a sampler namely in CMAEvolutionStrategycma.restricted_gaussian_sampler.GaussVDSampler - Restricted Gaussian Sampler for VD-CMA VD-CMA: Linear Time/Space Comparison-based Natural Gradient Optimization The covariance matrix is limited as C = D * (I + v*v^t) * D, where D is a diagonal, v is a vector.cma.restricted_gaussian_sampler.GaussVkDSampler - Restricted Gaussian Sampler for VkD-CMA O(N*k^2 + k^3) Time/Space Variant of CMA-ES with C = D * (I + V * V^T) * Dcma.sampler.GaussSampler - No class docstring; 3/3 properties, 0/3 instance variable, 3/3 methods documentedcma.sampler.GaussDiagonalSampler - Multi-variate normal distribution with zero mean and diagonal covariance matrix.cma.sampler.GaussFullSampler - Multi-variate normal distribution with zero mean.cma.sampler.GaussStandardConstant - Standard Multi-variate normal distribution with zero mean.cma.logger.Logger - log an arbitrary number of data (a data row) per "timestep".cma.logger.LoggerDummy - use to fake a Logger in non-verbose settingcma.optimization_tools.BestSolution - container to keep track of the best solution seen.cma.optimization_tools.BestSolution2 - minimal tracker of a smallest f-value with variable meta-infocma.optimization_tools.EvalParallel2 - A class and context manager for parallel evaluations.cma.optimization_tools.ExponentialSmoothing - not in use (yet)cma.optimization_tools.EvolutionPath - not in use (yet)cma.optimization_tools.BinaryEvolutionPath - No class docstring; 2/2 properties, 1/1 method documentedcma.optimization_tools.NoiseHandler - Noise handling according to [Hansen et al 2009, A Method for Handling Uncertainty in Evolutionary Optimization...]cma.optimization_tools.OldEvolutionPath - not in use (yet)cma.optimization_tools.Sections - plot sections through an objective function.cma.purecma.BestSolution - container to keep track of the best solution seencma.purecma.CMAESParameters - static "internal" parameter setting for CMAEScma.purecma.ff - versatile collection of test functions in static methodscma.restricted_gaussian_sampler.ExponentialMovingAverage - Exponential Moving Average, Variance, and SNR (Signal-to-Noise Ratio)cma.sigma_adaptation.CMAAdaptSigmaBase - step-size adaptation base class, implement hsig (for stalling distribution update) functionality via an isotropic evolution path.cma.sigma_adaptation.CMAAdaptSigmaCSA - CSA cumulative step-size adaptation AKA path length control.cma.sigma_adaptation.CMAAdaptSigmaDistanceProportional - artificial setting of sigma for test purposes, e.g. to simulate optimal progress rates.cma.sigma_adaptation.CMAAdaptSigmaMedianImprovement - Compares median fitness to the 27%tile fitness of the previous iteration, see Ait ElHara et al, GECCO 2013.cma.sigma_adaptation.CMAAdaptSigmaNone - constant step-size sigmacma.sigma_adaptation.CMAAdaptSigmaTPA - two point adaptation for step-size sigma.cma.transformations.AdaptiveDecoding - base class for adaptive decoding.cma.transformations.DiagonalDecoding - Diagonal linear transformation with exponential update.cma.transformations.BoxConstraintsTransformationBase - Implements a transformation into boundaries and is used in top level boundary handling classes.cma.transformations._BoxConstraintsTransformationTemplate - copy/paste this template to implement a new boundary handling transformationcma.transformations.BoxConstraintsLinQuadTransformation - implement a bijective, monotonous transformation between [lb - al, ub + au] and [lb, ub].cma.transformations.ConstRandnShift - ConstRandnShift()(x) adds a fixed realization of stddev * randn(len(x)) to the vector x.cma.transformations.GenoPheno - Genotype-phenotype transformation.cma.transformations.Rotation - implement an orthogonal linear transformation for each dimension.cma.utilities.math.MathHelperFunctions - static convenience math helper functions, if the function name is preceded with an "a", a numpy array is returnedcma.utilities.math.UpdatingAverage - use instead of a list when too many values must be averagedcma.utilities.utils.BlancClass - blanc container class to have a collection of attributes.cma.utilities.utils.DefaultSettings - resembling somewhat types.SimpleNamespace from Python >=3.3 but with instantiation and resembling even more the dataclass decorator from Python >=3.7.cma.fitness_models.LQModelSettings - Undocumentedcma.fitness_models.ModelInjectionCallbackSettings - Undocumentedcma.fitness_models.SurrogatePopulationSettings - Undocumentedcma.utilities.utils.ElapsedWCTime - measure elapsed cumulative time while not paused and elapsed time since last tic.cma.utilities.utils.TimingWrapper - wrap a timer around a callable.tuplecma.evolution_strategy._CMAEvolutionStrategyResult - A results tuple from CMAEvolutionStrategy property result.UserWarningcma.evolution_strategy.InjectionWarning - Injected solutions are not passed to tell as expected