update math syntax on paper

CONTRIBUTING.md

@@ -2,7 +2,7 @@ Contribution guidelines
 =====================================
 
 <p align="center">
-    Minimalist And Customizable Optimization Package
+    Minimalist And Customisable Optimisation Package
 </p>
 
 

README.md

@@ -1,4 +1,4 @@
-# Minimalist And Customizable Optimization Package
+# Minimalist And Customisable Optimisation Package
 
 ![](https://img.shields.io/github/workflow/status/jbuisine/macop/build?style=flat-square) ![](https://img.shields.io/pypi/v/macop?style=flat-square) ![](https://img.shields.io/pypi/dm/macop?style=flat-square)
 
@@ -9,7 +9,7 @@
 
 ## Description
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`macop` is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
 
 ## Modules
 

docs/build/html/_modules/macop/algorithms/Algorithm.html

@@ -171,7 +171,7 @@
 <span class="sd">        operators: {[Operator]} -- list of operator to use when launching algorithm</span>
 <span class="sd">        policy: {Policy} -- Policy class implementation strategy to select operators</span>
 <span class="sd">        validator: {function} -- basic function to check if solution is valid or not under some constraints</span>
-<span class="sd">        maximise: {bool} -- specify kind of optimization problem </span>
+<span class="sd">        maximise: {bool} -- specify kind of optimisation problem </span>
 <span class="sd">        currentSolution: {Solution} -- current solution managed for current evaluation</span>
 <span class="sd">        bestSolution: {Solution} -- best solution found so far during running algorithm</span>
 <span class="sd">        callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm</span>

docs/build/html/_modules/macop/algorithms/mono/IteratedLocalSearch.html

@@ -171,7 +171,7 @@
 <span class="sd">        operators: {[Operator]} -- list of operator to use when launching algorithm</span>
 <span class="sd">        policy: {Policy} -- Policy class implementation strategy to select operators</span>
 <span class="sd">        validator: {function} -- basic function to check if solution is valid or not under some constraints</span>
-<span class="sd">        maximise: {bool} -- specify kind of optimization problem </span>
+<span class="sd">        maximise: {bool} -- specify kind of optimisation problem </span>
 <span class="sd">        currentSolution: {Solution} -- current solution managed for current evaluation</span>
 <span class="sd">        bestSolution: {Solution} -- best solution found so far during running algorithm</span>
 <span class="sd">        callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm</span>

docs/build/html/_modules/macop/algorithms/mono/LocalSearch.html

@@ -162,7 +162,7 @@
 
 
 <div class="viewcode-block" id="LocalSearch"><a class="viewcode-back" href="../../../../macop/macop.algorithms.mono.LocalSearch.html#macop.algorithms.mono.LocalSearch.LocalSearch">[docs]</a><span class="k">class</span> <span class="nc">LocalSearch</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Local Search used as exploitation optimization algorithm</span>
+    <span class="sd">&quot;&quot;&quot;Local Search used as exploitation optimisation algorithm</span>
 
 <span class="sd">    Attributes:</span>
 <span class="sd">        initalizer: {function} -- basic function strategy to initialize solution</span>
@@ -170,7 +170,7 @@
 <span class="sd">        operators: {[Operator]} -- list of operator to use when launching algorithm</span>
 <span class="sd">        policy: {Policy} -- Policy class implementation strategy to select operators</span>
 <span class="sd">        validator: {function} -- basic function to check if solution is valid or not under some constraints</span>
-<span class="sd">        maximise: {bool} -- specify kind of optimization problem </span>
+<span class="sd">        maximise: {bool} -- specify kind of optimisation problem </span>
 <span class="sd">        currentSolution: {Solution} -- current solution managed for current evaluation</span>
 <span class="sd">        bestSolution: {Solution} -- best solution found so far during running algorithm</span>
 <span class="sd">        callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm</span>

docs/build/html/_modules/macop/algorithms/multi/MOEAD.html

@@ -190,7 +190,7 @@
 <span class="sd">        operators: {[Operator]} -- list of operator to use when launching algorithm</span>
 <span class="sd">        policy: {Policy} -- Policy class implementation strategy to select operators</span>
 <span class="sd">        validator: {function} -- basic function to check if solution is valid or not under some constraints</span>
-<span class="sd">        maximise: {bool} -- specify kind of optimization problem </span>
+<span class="sd">        maximise: {bool} -- specify kind of optimisation problem </span>
 <span class="sd">        population: [{Solution}] -- population of solution, one for each sub problem</span>
 <span class="sd">        pfPop: [{Solution}] -- pareto front population</span>
 <span class="sd">        weights: [[{float}]] -- random weights used for custom mu sub problems</span>

docs/build/html/_modules/macop/algorithms/multi/MOSubProblem.html

@@ -172,7 +172,7 @@
 <span class="sd">        operators: {[Operator]} -- list of operator to use when launching algorithm</span>
 <span class="sd">        policy: {Policy} -- Policy class implementation strategy to select operators</span>
 <span class="sd">        validator: {function} -- basic function to check if solution is valid or not under some constraints</span>
-<span class="sd">        maximise: {bool} -- specify kind of optimization problem </span>
+<span class="sd">        maximise: {bool} -- specify kind of optimisation problem </span>
 <span class="sd">        currentSolution: {Solution} -- current solution managed for current evaluation</span>
 <span class="sd">        bestSolution: {Solution} -- best solution found so far during running algorithm</span>
 <span class="sd">        callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm</span>

docs/build/html/_sources/description.rst.txt

@@ -9,7 +9,7 @@ Description
 Context
 ------------
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`macop` is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
 
 Installation
 ------------

docs/build/html/_sources/examples.rst.txt

@@ -64,7 +64,7 @@ Hence, we define our problem :
     elements_score = [ random.randint(1, 20) for _ in range(30) ] # value of each object
     elements_weight = [ random.randint(5, 25) for _ in range(30) ] # weight of each object
 
-We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimization way selecting or not each component (object).
+We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimisation way selecting or not each component (object).
 The best way to represent this problem is to use the `BinarySolution` from `macop` which stores solution as a binary array.
 
 Using the solution representation, we need to define multiple elements to fit our algorithm :
@@ -351,7 +351,7 @@ In multi-objective algorithm, we do not only found one solution but a set of non
     elements_weight = [ random.randint(5, 25) for _ in range(200) ] # weight of each object
 
 
-We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimization way selecting or not each component (object).
+We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimisation way selecting or not each component (object).
 The best way to represent this problem is to use the `BinarySolution` from `macop` which stores solution as a binary array.
 
 Using the solution representation, we need to define multiple elements to fit our algorithm :

docs/build/html/_sources/index.rst.txt

@@ -1,4 +1,4 @@
-Minimalist And Customizable Optimization Package
+Minimalist And Customisable Optimisation Package
 ================================================
 
 .. image:: _static/logo_macop.png
@@ -8,7 +8,7 @@ Minimalist And Customizable Optimization Package
 What's `macop` ?
 =================
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`macop` is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
 
 .. toctree::
    :maxdepth: 1

docs/build/html/description.html

@@ -36,7 +36,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Documentation" href="macop.html" />
-    <link rel="prev" title="Minimalist And Customizable Optimization Package" href="index.html" /> 
+    <link rel="prev" title="Minimalist And Customisable Optimisation Package" href="index.html" /> 
 </head>
 
 <body class="wy-body-for-nav">
@@ -165,7 +165,7 @@
 <a class="reference internal image-reference" href="_images/logo_macop.png"><img alt="_images/logo_macop.png" class="align-center" src="_images/logo_macop.png" style="width: 350px;" /></a>
 <div class="section" id="context">
 <h2>Context<a class="headerlink" href="#context" title="Permalink to this headline">¶</a></h2>
-<p><cite>macop</cite> is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.</p>
+<p><cite>macop</cite> is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.</p>
 </div>
 <div class="section" id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
@@ -187,7 +187,7 @@
         <a href="macop.html" class="btn btn-neutral float-right" title="Documentation" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="index.html" class="btn btn-neutral float-left" title="Minimalist And Customizable Optimization Package" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="index.html" class="btn btn-neutral float-left" title="Minimalist And Customisable Optimisation Package" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   

docs/build/html/index.html

@@ -8,7 +8,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Minimalist And Customizable Optimization Package &mdash; macop v1.0.0 documentation</title>
+  <title>Minimalist And Customisable Optimisation Package &mdash; macop v1.0.0 documentation</title>
   
 
   
@@ -134,7 +134,7 @@
     
       <li><a href="#">Docs</a> &raquo;</li>
         
-      <li>Minimalist And Customizable Optimization Package</li>
+      <li>Minimalist And Customisable Optimisation Package</li>
     
     
       <li class="wy-breadcrumbs-aside">
@@ -155,13 +155,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="minimalist-and-customizable-optimization-package">
-<h1>Minimalist And Customizable Optimization Package<a class="headerlink" href="#minimalist-and-customizable-optimization-package" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="minimalist-and-customisable-optimisation-package">
+<h1>Minimalist And Customisable Optimisation Package<a class="headerlink" href="#minimalist-and-customisable-optimisation-package" title="Permalink to this headline">¶</a></h1>
 <a class="reference internal image-reference" href="_images/logo_macop.png"><img alt="_images/logo_macop.png" class="align-center" src="_images/logo_macop.png" style="width: 450px;" /></a>
 </div>
 <div class="section" id="what-s-macop">
 <h1>What’s <cite>macop</cite> ?<a class="headerlink" href="#what-s-macop" title="Permalink to this headline">¶</a></h1>
-<p><cite>macop</cite> is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.</p>
+<p><cite>macop</cite> is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.</p>
 <div class="toctree-wrapper compound">
 <p class="caption"><span class="caption-text">Contents:</span></p>
 <ul>

docs/build/html/macop/macop.algorithms.Algorithm.html

@@ -225,7 +225,7 @@
 <dl class="attribute">
 <dt id="macop.algorithms.Algorithm.Algorithm.maximise">
 <code class="sig-name descname">maximise</code><a class="headerlink" href="#macop.algorithms.Algorithm.Algorithm.maximise" title="Permalink to this definition">¶</a></dt>
-<dd><p>{bool} – specify kind of optimization problem</p>
+<dd><p>{bool} – specify kind of optimisation problem</p>
 </dd></dl>
 
 <dl class="attribute">

docs/build/html/macop/macop.algorithms.mono.IteratedLocalSearch.html

@@ -225,7 +225,7 @@
 <dl class="attribute">
 <dt id="macop.algorithms.mono.IteratedLocalSearch.IteratedLocalSearch.maximise">
 <code class="sig-name descname">maximise</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.IteratedLocalSearch.maximise" title="Permalink to this definition">¶</a></dt>
-<dd><p>{bool} – specify kind of optimization problem</p>
+<dd><p>{bool} – specify kind of optimisation problem</p>
 </dd></dl>
 
 <dl class="attribute">

docs/build/html/macop/macop.algorithms.multi.MOEAD.html

@@ -255,7 +255,7 @@
 <dl class="attribute">
 <dt id="macop.algorithms.multi.MOEAD.MOEAD.maximise">
 <code class="sig-name descname">maximise</code><a class="headerlink" href="#macop.algorithms.multi.MOEAD.MOEAD.maximise" title="Permalink to this definition">¶</a></dt>
-<dd><p>{bool} – specify kind of optimization problem</p>
+<dd><p>{bool} – specify kind of optimisation problem</p>
 </dd></dl>
 
 <dl class="attribute">

docs/build/html/macop/macop.algorithms.multi.MOSubProblem.html

@@ -237,7 +237,7 @@
 <dl class="attribute">
 <dt id="macop.algorithms.multi.MOSubProblem.MOSubProblem.maximise">
 <code class="sig-name descname">maximise</code><a class="headerlink" href="#macop.algorithms.multi.MOSubProblem.MOSubProblem.maximise" title="Permalink to this definition">¶</a></dt>
-<dd><p>{bool} – specify kind of optimization problem</p>
+<dd><p>{bool} – specify kind of optimisation problem</p>
 </dd></dl>
 
 <dl class="attribute">

docs/source/conf.py

@@ -187,7 +187,7 @@ man_pages = [
 #  dir menu entry, description, category)
 texinfo_documents = [
     (master_doc, 'macop', 'macop Documentation',
-     author, 'macop', 'Minimalist And Customizable Optimization Package',
+     author, 'macop', 'Minimalist And Customisable Optimisation Package',
      'Miscellaneous'),
 ]
 

docs/source/description.rst

@@ -9,7 +9,7 @@ Description
 Context
 ------------
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`macop` is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
 
 Installation
 ------------

docs/source/examples.rst

@@ -64,7 +64,7 @@ Hence, we define our problem :
     elements_score = [ random.randint(1, 20) for _ in range(30) ] # value of each object
     elements_weight = [ random.randint(5, 25) for _ in range(30) ] # weight of each object
 
-We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimization way selecting or not each component (object).
+We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimisation way selecting or not each component (object).
 The best way to represent this problem is to use the `BinarySolution` from `macop` which stores solution as a binary array.
 
 Using the solution representation, we need to define multiple elements to fit our algorithm :
@@ -351,7 +351,7 @@ In multi-objective algorithm, we do not only found one solution but a set of non
     elements_weight = [ random.randint(5, 25) for _ in range(200) ] # weight of each object
 
 
-We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimization way selecting or not each component (object).
+We can now define the solution representation. In knapsack problem we want to fill our knapsack in an optimisation way selecting or not each component (object).
 The best way to represent this problem is to use the `BinarySolution` from `macop` which stores solution as a binary array.
 
 Using the solution representation, we need to define multiple elements to fit our algorithm :

docs/source/index.rst

@@ -1,4 +1,4 @@
-Minimalist And Customizable Optimization Package
+Minimalist And Customisable Optimisation Package
 ================================================
 
 .. image:: _static/logo_macop.png
@@ -8,7 +8,7 @@ Minimalist And Customizable Optimization Package
 What's `macop` ?
 =================
 
-`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+`macop` is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
 
 .. toctree::
    :maxdepth: 1

macop/algorithms/Algorithm.py

@@ -18,7 +18,7 @@ class Algorithm():
         operators: {[Operator]} -- list of operator to use when launching algorithm
         policy: {Policy} -- Policy class implementation strategy to select operators
         validator: {function} -- basic function to check if solution is valid or not under some constraints
-        maximise: {bool} -- specify kind of optimization problem 
+        maximise: {bool} -- specify kind of optimisation problem 
         currentSolution: {Solution} -- current solution managed for current evaluation
         bestSolution: {Solution} -- best solution found so far during running algorithm
         callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm

macop/algorithms/mono/IteratedLocalSearch.py

@@ -18,7 +18,7 @@ class IteratedLocalSearch(Algorithm):
         operators: {[Operator]} -- list of operator to use when launching algorithm
         policy: {Policy} -- Policy class implementation strategy to select operators
         validator: {function} -- basic function to check if solution is valid or not under some constraints
-        maximise: {bool} -- specify kind of optimization problem 
+        maximise: {bool} -- specify kind of optimisation problem 
         currentSolution: {Solution} -- current solution managed for current evaluation
         bestSolution: {Solution} -- best solution found so far during running algorithm
         callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm

macop/algorithms/mono/LocalSearch.py

@@ -9,7 +9,7 @@ from ..Algorithm import Algorithm
 
 
 class LocalSearch(Algorithm):
-    """Local Search used as exploitation optimization algorithm
+    """Local Search used as exploitation optimisation algorithm
 
     Attributes:
         initalizer: {function} -- basic function strategy to initialize solution
@@ -17,7 +17,7 @@ class LocalSearch(Algorithm):
         operators: {[Operator]} -- list of operator to use when launching algorithm
         policy: {Policy} -- Policy class implementation strategy to select operators
         validator: {function} -- basic function to check if solution is valid or not under some constraints
-        maximise: {bool} -- specify kind of optimization problem 
+        maximise: {bool} -- specify kind of optimisation problem 
         currentSolution: {Solution} -- current solution managed for current evaluation
         bestSolution: {Solution} -- best solution found so far during running algorithm
         callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm

macop/algorithms/multi/MOEAD.py

@@ -37,7 +37,7 @@ class MOEAD(Algorithm):
         operators: {[Operator]} -- list of operator to use when launching algorithm
         policy: {Policy} -- Policy class implementation strategy to select operators
         validator: {function} -- basic function to check if solution is valid or not under some constraints
-        maximise: {bool} -- specify kind of optimization problem 
+        maximise: {bool} -- specify kind of optimisation problem 
         population: [{Solution}] -- population of solution, one for each sub problem
         pfPop: [{Solution}] -- pareto front population
         weights: [[{float}]] -- random weights used for custom mu sub problems

macop/algorithms/multi/MOSubProblem.py

@@ -19,7 +19,7 @@ class MOSubProblem(Algorithm):
         operators: {[Operator]} -- list of operator to use when launching algorithm
         policy: {Policy} -- Policy class implementation strategy to select operators
         validator: {function} -- basic function to check if solution is valid or not under some constraints
-        maximise: {bool} -- specify kind of optimization problem 
+        maximise: {bool} -- specify kind of optimisation problem 
         currentSolution: {Solution} -- current solution managed for current evaluation
         bestSolution: {Solution} -- best solution found so far during running algorithm
         callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm

setup.py

@@ -81,7 +81,7 @@ class TestCommand(distutils.command.check.check):
 setup(
     name='macop',
     version='1.0.0',
-    description='Minimalist And Customizable Optimization Package',
+    description='Minimalist And Customisable Optimisation Package',
     long_description=open('README.md').read(),
     long_description_content_type='text/markdown',
     classifiers=[