As seen in the :ref:`cma-es` example, the :func:`~deap.algorithms.eaGenerateUpdate` algorithm is suitable for algorithms learning the problem distribution from the population. Here we'll cover how to implement a strategy that generates individuals based on an updated sampling function learnt from the sampled population.
The basic concept concept behind EDA is to sample \lambda individuals with a certain distribution and estimate the problem distribution from the \mu best individuals. This really simple concept adhere to the generate-update logic. The strategy contains a random number generator which is adapted from the population. The following :class:`EDA` class do just that.
.. literalinclude:: /code/examples/eda/fctmin.py :pyobject: EDA
A normal random number generator is initialized with a certain mean (centroid) and standard deviation (sigma) for each dimension. The :meth:`generate` method uses numpy to generate lambda_ sequences in dim dimensions, then the sequences are used to initialize individuals of class given in the ind_init argument. Finally, the :meth:`update` computes the average (centre) of the mu best individuals and estimates the variance over all attributes of each individual. Once :meth:`update` is called the distributions parameters are changed and a new population can be generated.
Two classes are needed, a minimization fitness and a individual that will combine the fitness and the real values. Moreover, we will use :class:`numpy.ndarray` as base class for our individuals.
.. literalinclude:: /code/examples/eda/fctmin.py :lines: 28-29
The :func:`~deap.algorithms.eaGenerateUpdate` algorithm requires to set in a toolbox an evaluation function, an generation method and an update method. We will use the method of an initialized :class:`EDA`. For the generate method, we set the class that the individuals are transferred in to our :class:`Individual` class containing a fitness.
.. literalinclude:: /code/examples/eda/fctmin.py :pyobject: main
The complete example : [source code].