Prise3D
/
Thesis-OptimizationModules


			
							123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449
							

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>Some examples &mdash; macop v0.1.6 documentation</title>
  

  <script type="text/javascript" src="_static/js/modernizr.min.js"></script>
  
    
      <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
        <script type="text/javascript" src="_static/jquery.js"></script>
        <script type="text/javascript" src="_static/underscore.js"></script>
        <script type="text/javascript" src="_static/doctools.js"></script>
        <script type="text/javascript" src="_static/language_data.js"></script>
    
    <script type="text/javascript" src="_static/js/theme.js"></script>

    
  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
    <link rel="index" title="Index" href="genindex.html" />
    <link rel="search" title="Search" href="search.html" />
    <link rel="next" title="Contributing" href="contributing.html" />
    <link rel="prev" title="macop.solutions.Solution" href="macop/macop.solutions.Solution.html" /> 
</head>

<body class="wy-body-for-nav">

   
  <div class="wy-grid-for-nav">
    
    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
      <div class="wy-side-scroll">
        <div class="wy-side-nav-search" >
          

            <a href="index.html" class="icon icon-home"> macop
          

          </a>

          
              <div class="version">
                0.1.6
              </div>
            
          
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

          
        </div>

        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          
            
              <p class="caption"><span class="caption-text">Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="description.html">Description</a></li>
<li class="toctree-l1"><a class="reference internal" href="macop.html">Documentation</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Some examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#mono-objective">1. Mono-objective</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#problem-definition">1.1 Problem definition</a></li>
<li class="toctree-l3"><a class="reference internal" href="#operators-and-policy">1.2 Operators and Policy</a></li>
<li class="toctree-l3"><a class="reference internal" href="#before-running-algorithm">1.3 Before running algorithm</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#multi-objective-example">2. Multi-objective example</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a></li>
</ul>

            
        </div>
      </div>
    </nav>

    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">

      
      <nav class="wy-nav-top" aria-label="top navigation">
        
          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
          <a href="index.html">macop</a>
        
      </nav>


      <div class="wy-nav-content">
        
        <div class="rst-content">
        
          
<div role="navigation" aria-label="breadcrumbs navigation">

  <ul class="wy-breadcrumbs">
    
      <li><a href="index.html">Docs</a> &raquo;</li>
        
      <li>Some examples</li>
    
    
      <li class="wy-breadcrumbs-aside">
        
            
              <a href="https://github.com/prise-3d/macop/blob/master/docs/source/examples.rst" class="fa fa-github"> Edit on GitHub</a>
            
          
      </li>
    
  </ul>

  
  <hr/>
</div>
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
            
  <div class="section" id="some-examples">
<h1>Some examples<a class="headerlink" href="#some-examples" title="Permalink to this headline">¶</a></h1>
<div class="section" id="mono-objective">
<h2>1. Mono-objective<a class="headerlink" href="#mono-objective" title="Permalink to this headline">¶</a></h2>
<p>In this tutorial, it will introduce the way of running your algorithm quickly.
First of all we need to define the kind of solution best represent the problem. In this tutorial, we use the well known knapsack problem using 30 objects.</p>
<div class="section" id="problem-definition">
<h3>1.1 Problem definition<a class="headerlink" href="#problem-definition" title="Permalink to this headline">¶</a></h3>
<p>Hence, we define our problem :
- value of each component of knapsack
- weight associated to each of these components (objects)</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">random</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>

<span class="n">elements_score</span> <span class="o">=</span> <span class="p">[</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span> <span class="p">]</span>
<span class="n">elements_weight</span> <span class="o">=</span> <span class="p">[</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span> <span class="p">]</span>
</pre></div>
</div>
<p>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).
The best way to represent this problem is to use the <cite>BinarySolution</cite> from <cite>macop</cite> which stores solution as a binary array.</p>
<p>Using the solution representation, we need to define multiple things to fit our algorithm :
- 1. function which validates or not a solution (based on constraints)
- 2. function which evaluates the solution (in order to obtain fitness)
- 3. initialization solution function</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">macop.solutions.BinarySolution</span> <span class="kn">import</span> <span class="n">BinarySolution</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>

<span class="n">elements_score</span> <span class="o">=</span> <span class="p">[</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span> <span class="p">]</span>
<span class="n">elements_weight</span> <span class="o">=</span> <span class="p">[</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span> <span class="p">]</span>

<span class="c1"># 1. validator function (we accept only bag with maximum weight 80kg)</span>
<span class="k">def</span> <span class="nf">validator</span><span class="p">(</span><span class="n">_solution</span><span class="p">):</span>

    <span class="n">weight_sum</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">elem</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_solution</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
        <span class="n">weight_sum</span> <span class="o">+=</span> <span class="n">elements_weight</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">*</span> <span class="n">elem</span>

    <span class="k">if</span> <span class="n">weight_sum</span> <span class="o">&lt;=</span> <span class="mi">80</span><span class="p">:</span>
        <span class="k">return</span> <span class="kc">True</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="kc">False</span>

<span class="c1"># 2. function which computes fitness of solution</span>
<span class="k">def</span> <span class="nf">evaluator</span><span class="p">(</span><span class="n">_solution</span><span class="p">):</span>

    <span class="n">fitness</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">elem</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_solution</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
        <span class="n">fitness</span> <span class="o">+=</span> <span class="p">(</span><span class="n">elements_score</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">*</span> <span class="n">elem</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">fitness</span>

<span class="c1"># 3. function which here initializes solution ramdomly and check validity of solution</span>
<span class="k">def</span> <span class="nf">init</span><span class="p">():</span>
    <span class="k">return</span> <span class="n">BinarySolution</span><span class="p">([],</span> <span class="mi">30</span><span class="p">)</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">validator</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="operators-and-policy">
<h3>1.2 Operators and Policy<a class="headerlink" href="#operators-and-policy" title="Permalink to this headline">¶</a></h3>
<p>In our algorithm we need to use some operators in order to improve current best solution found at current <cite>n</cite> evaluations.</p>
<p>In <cite>macop</cite> you have some available operators. In this example, we use 3 of them.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="kn">from</span> <span class="nn">macop.operators.mutators.SimpleMutation</span> <span class="kn">import</span> <span class="n">SimpleMutation</span>
<span class="kn">from</span> <span class="nn">macop.operators.mutators.SimpleBinaryMutation</span> <span class="kn">import</span> <span class="n">SimpleBinaryMutation</span>
<span class="kn">from</span> <span class="nn">macop.operators.crossovers.SimpleCrossover</span> <span class="kn">import</span> <span class="n">SimpleCrossover</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Algorithm parameters</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># list of operators instance to use</span>
<span class="n">operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">SimpleBinaryMutation</span><span class="p">(),</span> <span class="n">SimpleMutation</span><span class="p">(),</span> <span class="n">SimpleCrossover</span><span class="p">(),</span> <span class="n">RandomSplitCrossover</span><span class="p">()]</span>
</pre></div>
</div>
<p>As we defined multiple operators, we have to tell how we want to select them into the algorithm. This is why <strong>Policy</strong> classes have been implemented.
<cite>Policy</cite> class implementation enables to select the next operator to use and once new solution is generated, computes its score (in <cite>apply</cite> method). This class requires all the operators use to be instanciate.</p>
<p>Why computing score into <strong>Policy</strong> <cite>apply</cite> method ? Because it’s a way to get some important statistics from solution improvment using specific operator.
<strong>UCBPolicy</strong> as example, based on Upper Confidence Bound (<a class="reference external" href="https://banditalgs.com/2016/09/18/the-upper-confidence-bound-algorithm/">UCB</a>), computes reward each time a new solution is generated from an operator in order to better select next operator later. We use in this example the <cite>UCBPolicy</cite> implementation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="kn">from</span> <span class="nn">macop.operators.mutators.SimpleMutation</span> <span class="kn">import</span> <span class="n">SimpleMutation</span>
<span class="kn">from</span> <span class="nn">macop.operators.mutators.SimpleBinaryMutation</span> <span class="kn">import</span> <span class="n">SimpleBinaryMutation</span>
<span class="kn">from</span> <span class="nn">macop.operators.crossovers.SimpleCrossover</span> <span class="kn">import</span> <span class="n">SimpleCrossover</span>

<span class="kn">from</span> <span class="nn">macop.operators.policies.UCBPolicy</span> <span class="kn">import</span> <span class="n">UCBPolicy</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Algorithm parameters</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># list of operators instance to use</span>
<span class="n">operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">SimpleBinaryMutation</span><span class="p">(),</span> <span class="n">SimpleMutation</span><span class="p">(),</span> <span class="n">SimpleCrossover</span><span class="p">(),</span> <span class="n">RandomSplitCrossover</span><span class="p">()]</span>

<span class="c1"># `policy` instance is created using specific value for Upper Confidence Bound</span>
<span class="n">policy</span> <span class="o">=</span> <span class="n">UCBPolicy</span><span class="p">(</span><span class="n">operators</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">100.</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="before-running-algorithm">
<h3>1.3 Before running algorithm<a class="headerlink" href="#before-running-algorithm" title="Permalink to this headline">¶</a></h3>
<p>Before running algorithm we can define a logger to keep track of the all algorithm run.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="kn">import</span> <span class="nn">logging</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Algorithm parameters</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>

<span class="c1"># logging configuration</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/example.log&#39;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
</pre></div>
</div>
<p>We can now instanciate our algorithm. We use the Iterated Local Search in this example. It is mainly used to avoid local optima using multiple local search.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">from</span> <span class="nn">macop.algorithms.IteratedLocalSearch</span> <span class="kn">import</span> <span class="n">IteratedLocalSearch</span> <span class="k">as</span> <span class="n">ILS</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Algorithm parameters</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>

<span class="c1"># logging configuration</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/example.log&#39;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>

<span class="n">algo</span> <span class="o">=</span> <span class="n">ILS</span><span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">evaluator</span><span class="p">,</span> <span class="n">operators</span><span class="p">,</span> <span class="n">policy</span><span class="p">,</span> <span class="n">validator</span><span class="p">,</span> <span class="n">_maximise</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>The algorithm is now well defined and is ready to run ! But one thing can be done, and it’s very interesting to avoir restart from scratch the algorithm run.
The use of checkpoint is available in <cite>macop</cite>. A <cite>BasicCheckpoint</cite> class let the algorithm save at <cite>every</cite> evaluations the best solution found.</p>
<p>We need to specify the use of checkpoint if we prefer to restart from.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">imports part</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">from</span> <span class="nn">macop.algorithms.IteratedLocalSearch</span> <span class="kn">import</span> <span class="n">IteratedLocalSearch</span> <span class="k">as</span> <span class="n">ILS</span>
<span class="kn">from</span> <span class="nn">macop.checkpoints.BasicCheckpoint</span> <span class="kn">import</span> <span class="n">BasicCheckpoint</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Problem definition</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Algorithm parameters</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">...</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>

<span class="c1"># logging configuration</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;data/example.log&#39;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>

<span class="n">algo</span> <span class="o">=</span> <span class="n">ILS</span><span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">evaluator</span><span class="p">,</span> <span class="n">operators</span><span class="p">,</span> <span class="n">policy</span><span class="p">,</span> <span class="n">validator</span><span class="p">,</span> <span class="n">_maximise</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># we specify the checkpoint class directly, the frequency and the path we want to save algorithm evolution</span>
<span class="n">algo</span><span class="o">.</span><span class="n">addCheckpoint</span><span class="p">(</span><span class="n">_class</span><span class="o">=</span><span class="n">BasicCheckpoint</span><span class="p">,</span> <span class="n">_every</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">_filepath</span><span class="o">=</span><span class="s1">&#39;data/checkpoint.csv&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>In this way, now we can run and obtained the best solution found in <cite>n</cite> evaluations</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bestSol</span> <span class="o">=</span> <span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Solution score is </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">bestSol</span><span class="p">)))</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="multi-objective-example">
<h2>2. Multi-objective example<a class="headerlink" href="#multi-objective-example" title="Permalink to this headline">¶</a></h2>
<p>Available soon…</p>
</div>
</div>


           </div>
           
          </div>
          <footer>
  
    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
      
        <a href="contributing.html" class="btn btn-neutral float-right" title="Contributing" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
      
      
        <a href="macop/macop.solutions.Solution.html" class="btn btn-neutral float-left" title="macop.solutions.Solution" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
      
    </div>
  

  <hr/>

  <div role="contentinfo">
    <p>
        &copy; Copyright 2020, Jérôme BUISINE

    </p>
  </div>
  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 

</footer>

        </div>
      </div>

    </section>

  </div>
  

  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script>

  
</body>
</html>