Merge pull request DEAP#46 from joernhees/patch-1

tutorial part2: minor language fixes

doc/tutorials/basic/part2.rst

@@ -3,7 +3,7 @@
 Operators and Algorithms
 ========================
 
-Before starting with complex algorithms, we will see some basis of DEAP.
+Before starting with complex algorithms, we will see some basics of DEAP.
 First, we will start by creating simple individuals (as seen in the
 :ref:`creating-types` tutorial) and make them interact with each other using
 different operators. Afterwards, we will learn how to use the algorithms and
@@ -12,7 +12,7 @@ other tools.
 A First Individual
 ------------------
 
-First import the required modules and register the different functions required to create individuals that are a list of floats with a minimizing  two objectives fitness.
+First import the required modules and register the different functions required to create individuals that are lists of floats with a minimizing two objectives fitness.
 
 .. literalinclude:: /code/tutorials/part_3/3_next_step.py
    :lines: 2-16
@@ -34,11 +34,11 @@ Evaluation
 
 The evaluation is the most personal part of an evolutionary algorithm, it is
 the only part of the library that you must write yourself. A typical
-evaluation function takes one individual as argument and return its fitness as
+evaluation function takes one individual as argument and returns its fitness as
 a :class:`tuple`. As shown in the in the :ref:`core` section, a fitness is a list of floating point values and has a
 property :attr:`~deap.base.Fitness.valid` to know if this individual shall be re-evaluated. The
 fitness is set by setting the :attr:`~deap.base.Fitness.values` to the
-associated :class:`tuple`. For example, the following evaluates the previously created individual ``ind1`` and assign its fitness to the corresponding values.
+associated :class:`tuple`. For example, the following evaluates the previously created individual ``ind1`` and assigns its fitness to the corresponding values.
 
 .. literalinclude:: /code/tutorials/part_3/3_next_step.py
    :lines: 24-32
@@ -49,21 +49,21 @@ Mutation
 --------
 The next kind of operator that we will present is the mutation operator.
 There is a variety of mutation operators in the :mod:`deap.tools` module.
-Each mutation has its own characteristics and may be applied to different type
-of individual. Be careful to read the documentation of the selected operator
+Each mutation has its own characteristics and may be applied to different types
+of individuals. Be careful to read the documentation of the selected operator
 in order to avoid undesirable behaviour.
 
 The general rule for mutation operators is that they **only** mutate, this
 means that an independent copy must be made prior to mutating the individual
-if the original individual has to be kept or is a *reference* to an other individual (see the selection operator).
+if the original individual has to be kept or is a *reference* to another individual (see the selection operator).
 
 In order to apply a mutation (here a gaussian mutation) on the individual ``ind1``,
 simply apply the desired function.
 
 .. literalinclude:: /code/tutorials/part_3/3_next_step.py
    :lines: 35-37
 
-The fitness' values are deleted because they not related to the individual anymore. As stated above, the mutation does mutate and only mutate an individual it is not responsible of invalidating the fitness nor anything else. The following shows that ``ind2`` and ``mutant`` are in fact the same individual.
+The fitness' values are deleted because they're not related to the individual anymore. As stated above, the mutation does mutate and only mutate an individual it is neither responsible of invalidating the fitness nor anything else. The following shows that ``ind2`` and ``mutant`` are in fact the same individual.
 
 .. literalinclude:: /code/tutorials/part_3/3_next_step.py
    :lines: 39-40
@@ -73,13 +73,13 @@ Crossover
 
 The second kind of operator that we will present is the crossover operator.
 There is a variety of crossover operators in the :mod:`deap.tools` module.
-Each crossover has its own characteristics and may be applied to different type
+Each crossover has its own characteristics and may be applied to different types
 of individuals. Be careful to read the documentation of the selected operator
 in order to avoid undesirable behaviour.
 
 The general rule for crossover operators is that they **only** mate individuals, this
 means that an independent copies must be made prior to mating the individuals
-if the original individuals have to be kept or is are *references* to other
+if the original individuals have to be kept or are *references* to other
 individuals (see the selection operator).
 
 Lets apply a crossover operation to produce the two children that are cloned beforehand.
@@ -104,7 +104,7 @@ Selection
 ---------
 
 Selection is made among a population by the selection operators that are
-available in the :mod:`deap.operators` module. The selection operator usually
+available in the :mod:`deap.tools` module. The selection operator usually
 takes as first argument an iterable container of individuals and the number of
 individuals to select. It returns a list containing the references to the
 selected individuals. The selection is made as follow.
@@ -130,7 +130,7 @@ Using the Toolbox
 
 The toolbox is intended to contain all the evolutionary tools, from the object
 initializers to the evaluation operator. It allows easy configuration of each
-algorithms. The toolbox has basically two methods,
+algorithm. The toolbox has basically two methods,
 :meth:`~deap.toolbox.Toolbox.register` and
 :meth:`~deap.toolbox.Toolbox.unregister`, that are used to add or remove tools
 from the toolbox.
@@ -163,22 +163,22 @@ very simple generational evolutionary algorithm.
 This is a complete algorithm. It is generic enough to accept any kind of
 individual and any operator, as long as the operators are suitable for the
 chosen individual type. As shown in the last example, the usage of the toolbox
-allows to write algorithms that are as close as possible to the pseudo code.
-Now it is up to you to write and experiment your own.
+allows to write algorithms that are as close as possible to pseudo code.
+Now it is up to you to write and experiment on your own.
 
 Tool Decoration
 +++++++++++++++
 Tool decoration is a very powerful feature that helps to control very precise
-thing during an evolution without changing anything in the algorithm or
+things during an evolution without changing anything in the algorithm or
 operators. A decorator is a wrapper that is called instead of a function. It
 is asked to make some initialization and termination work before and after the
 actual function is called. For example, in the case of a constrained domain,
 one can apply a decorator to the mutation and crossover in order to keep any
 individual from being out-of-bound. The following defines a decorator that
-checks if any attribute in the list is out-of-bound and clips it if it is the
+checks if any attribute in the list is out-of-bound and clips it if this is the
 case. The decorator is defined using three functions in order to receive the
 *min* and *max* arguments. Whenever the mutation or crossover is called,
-bounds will be check on the resulting individuals.
+bounds will be checked on the resulting individuals.
 
 .. literalinclude:: /code/tutorials/part_3/3_6_2_tool_decoration.py
    :lines: 8-
@@ -194,28 +194,28 @@ and `Python Decorator Libary <http://wiki.python.org/moin/PythonDecoratorLibrary
 
 Variations
 ----------
-Variations allows to build simple algorithms using predefined small building blocks. In
+Variations allow to build simple algorithms using predefined small building blocks. In
 order to use a variation, the toolbox must be set to contain the required
 operators. For example in the lastly presented complete algorithm, the
 crossover and mutation are regrouped in the :func:`~deap.algorithms.varAnd`
 function, this function requires the toolbox to contain the :func:`~deap.mate`
-and :func:`~deap.mutate` functions. The variations can be used to simplify
-the writing of an algorithm as follow.
+and :func:`~deap.mutate` functions. This variation can be used to simplify
+the writing of an algorithm as follows.
 
 .. literalinclude:: /code/tutorials/part_3/3_7_variations.py
    :lines: 33-
 
 This last example shows that using the variations makes it straight forward to
-build algorithms that are very close to the pseudo-code.
+build algorithms that are very close to pseudo code.
 
 Algorithms
 ----------
-There is several algorithms implemented in the :mod:`~deap.algorithms` module.
+There are several algorithms implemented in the :mod:`~deap.algorithms` module.
 They are very simple and
 reflect the basic types of evolutionary algorithms present in the literature.
 The algorithms use a :class:`~deap.base.Toolbox` as defined in the last
 sections. In order to setup a toolbox for an algorithm, you must register the
-desired operators under a specified names, refer to the documentation of the
+desired operators under the specified names, refer to the documentation of the
 selected algorithm for more details. Once the toolbox is ready, it is time to
 launch the algorithm. The simple evolutionary algorithm takes 5 arguments, a
 *population*, a *toolbox*, a probability of mating each individual at each
@@ -225,8 +225,8 @@ generation (*mutpb*) and a number of generations to accomplish (*ngen*).
 .. literalinclude:: /code/tutorials/part_3/3_8_algorithms.py
    :lines: 33-
 
-The best way to understand what the simple evolutionary algorithm does, it to
-take a look at the documentation or the source code
+The best way to understand what the simple evolutionary algorithm does, is to
+take a look at the documentation or the source code.
 
 Now that you built your own evolutionary algorithm in python, you are welcome
 to gives us feedback and appreciation. We would also really like to hear about