Class that encapsulates terminal primitive in expression. Terminals can be values or 0-arity functions.

Class that encapsulates a primitive and when called with arguments it returns the Python code to call the primitive with the arguments. >

r"""This decorator returns penalized fitness for invalid individuals and the original fitness value for valid individuals. The penalized fitness i

Estimation of Multivariate Normal Algorithm (EMNA) as described by Algorithm 1 in: Fabien Teytaud and Olivier Teytaud. 2009. Why one m

Meta-Class that creates a terminal which value is set when the object is created. To mutate the value, a new object has to be generated.

The Moving Peaks Benchmark is a fitness function changing over time. It consists of a number of peaks, changing in height, width and location. The

Hypervolume computation based on variant 3 of the algorithm in the paper: C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimensi

A special data structure needed by FonsecaHyperVolume. It consists of several doubly linked lists that share common nodes. So, every node has

Test object for unittest of deap/benchmarks.

r"""This decorator returns penalized fitness for invalid individuals and the original fitness value for valid individuals. The penalized fitness i

The fitness is a measure of quality of a solution. If *values* are provided as a tuple, the fitness is initialized using those values, otherwi

The hall of fame contains the best individual that ever lived in the population during the evolution. It is lexicographically sorted at all ti

The :class:`History` class helps to build a genealogy of all the individuals produced in the evolution. It contains two attributes, the :attr:

Evolution records as a chronological list of dictionaries. Data can be retrieved via the :meth:`select` method given the appropriate names.

Dictionary of :class:`Statistics` object allowing to compute statistics on multiple keys using a single call to :meth:`compile`. It takes a se

The Pareto front hall of fame contains all the non-dominated individuals that ever lived in the population. That means that the Pareto front hall

Class same as :class:`~deap.gp.PrimitiveSetTyped`, except there is no definition of type.

Class that contains the primitives that can be used to solve a Strongly Typed GP problem. The set also defined the researched function return

Tree specifically formatted for optimization of genetic programming operations. The tree is represented with a list, where the nodes are appen

Sorting network class. From Wikipedia : A sorting network is an abstract mathematical model of a network of wires and comparator modules that

Sorting network class. From Wikipedia : A sorting network is an abstract mathematical model of a network of wires and comparator modules that

Object that compiles statistics on a list of arbitrary objects. When created the statistics object receives a *key* argument that is used to g

A strategy that will keep track of the basic parameters of the CMA-ES algorithm ([Hansen2001]_). :param centroid: An iterable object tha

A CMA-ES strategy that combines the :math:`(1 + \\lambda)` paradigm [Igel2007]_, the mixed integer modification [Hansen2011]_, active covarian

Multiobjective CMA-ES strategy based on the paper [Voss2010]_. It is used similarly as the standard CMA-ES strategy with a generate-update sch

r""" A CMA-ES strategy that uses the :math:`1 + \lambda` paradigm ([Igel2007]_). :param parent: An iterable object that indicates where to st

A toolbox for evolution that contains the evolutionary operators. At first the toolbox contains a :meth:`~deap.toolbox.clone` method that dupl

Decorator for crossover and mutation functions, it changes the individuals after the modification is done to bring it back in the allowed *bou

Decorator for evaluation functions, it evaluates the objective function and adds noise by calling the function(s) provided in the *noise* argu

Decorator for evaluation functions, it rotates the objective function by *matrix* which should be a valid orthogonal NxN rotation matrix, with N

Decorator for evaluation functions, it scales the objective function by *factor* which should be the same length as the individual size. When

Class version of NSGA-III selection including memory for best, worst and extreme points. Registering this operator in a toolbox is a bit different

Decorator for evaluation functions, it translates the objective function by *vector* which should be the same length as the individual size. W