8000 docs: correct context on "multi-objective" by divineforge · Pull Request #254 · ahmedfgad/GeneticAlgorithmPython · GitHub
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docs: correct context on "multi-objective" #254

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Jan 27, 2024
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docs: correct context on "multi-objective" #254

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docs: correct context on "multi-objective"
divineforge Dec 2, 2023
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docs: edit pygad.rst
divineforge Dec 10, 2023
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8 changes: 4 additions & 4 deletions docs/source/pygad.rst
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8C93 Expand Up @@ -1039,7 +1039,7 @@ Let's discuss how to do each of these steps.
Preparing the ``fitness_func`` Parameter
-----------------------------------------

Even there are some steps in the genetic algorithm pipeline that can
Even though some steps in the genetic algorithm pipeline can
work the same regardless of the problem being solved, one critical step
is the calculation of the fitness value. There is no unique way of
calculating the fitness value and it changes from one problem to
Expand All @@ -1060,14 +1060,14 @@ optimization problem is single-objective or multi-objective.
``pygad.GA`` class.

- If the fitness function returns a ``list``, ``tuple``, or
``numpy.ndarray``, then the problem is single-objective. Even if
``numpy.ndarray``, then the problem is multi-objective. Even if
there is only one element, the problem is still considered
multi-objective. Each element represents the fitness value of its
corresponding objective.

Using a user-defined fitness function allows the user to freely use
PyGAD to solve any problem by passing the appropriate fitness
function/method. It is very important to understand the problem well for
PyGAD solves any problem by passing the appropriate fitness
function/method. It is very important to understand the problem well before
creating it.

Let's discuss an example:
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