paper

paper/paper.md

@@ -43,7 +43,6 @@ In a typical Python implementation, populations are initially defined as lists o
 
 A concise comparison between `pyrimidine` and several popular frameworks is provided in \autoref{frameworks}, such as [`DEAP`](https://deap.readthedocs.io/) [@fortin] and [`gaft`](https://github.com/PytLab/gaft), which have significantly influenced the design of `pyrimidine`.
 
-
 : Comparison of the popular genetic algorithm frameworks. \label{frameworks}
 
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@@ -84,12 +83,7 @@ The advantages of the model are summarized as follows:
 We introduce the concept of a **container**, simulating an **(algebraic) system** where specific operators are not yet defined.
 
 A container $s$ of type $S$, with elements of type $A$, is represented by following expression:
-
-    $$
-    s = \{a:A\}: S \quad \text{or} \quad s:S[A] 
-    \label{container}
-    $$
-
+$$s = \{a:A\}: S \quad \text{or} \quad s:S[A] \label{container}$$
 where the symbol $\{\cdot\}$ signifies either a set, or a sequence to emphasize the order of the elements. The notation $S[A]$ mimicks Python syntax, borrowed from the module [typing](https://docs.python.org/3.11/library/typing.html?highlight=typing#module-typing).
 
 Building upon the foundational concept, we define a population in `pyrimidine` as a container of individuals. The introduction of multi-population further extends this notion, representing a container of populations, often referred to as "the high-order container". `Pyrimidine` distinguishes itself with its inherent ability to seamlessly implement multi-population GAs. Populations in a multi-population behave analogously to individuals in a population. Notably, it allows to define containers in higher order, such as a container of multi-populations, potentially intertwined with conventional populations.