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  <div class="section" id="module-macop.algorithms.mono">
<span id="macop-algorithms-mono"></span><h1>macop.algorithms.mono<a class="headerlink" href="#module-macop.algorithms.mono" title="Permalink to this headline">¶</a></h1>
<p>Mono-objective available algorithms</p>
<p class="rubric">Classes</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#macop.algorithms.mono.HillClimberBestImprovment" title="macop.algorithms.mono.HillClimberBestImprovment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HillClimberBestImprovment</span></code></a>(initializer, …)</p></td>
<td><p>Hill Climber Best Improvment used as exploitation optimisation algorithm</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#macop.algorithms.mono.HillClimberFirstImprovment" title="macop.algorithms.mono.HillClimberFirstImprovment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HillClimberFirstImprovment</span></code></a>(initializer, …)</p></td>
<td><p>Hill Climber First Improvment used as quick exploration optimisation algorithm</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#macop.algorithms.mono.IteratedLocalSearch" title="macop.algorithms.mono.IteratedLocalSearch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IteratedLocalSearch</span></code></a>(initializer, evaluator, …)</p></td>
<td><p>Iterated Local Search used to avoid local optima and increave EvE (Exploration vs Exploitation) compromise</p></td>
</tr>
</tbody>
</table>
<dl class="class">
<dt id="macop.algorithms.mono.HillClimberBestImprovment">
<em class="property">class </em><code class="sig-prename descclassname">macop.algorithms.mono.</code><code class="sig-name descname">HillClimberBestImprovment</code><span class="sig-paren">(</span><em class="sig-param">initializer</em>, <em class="sig-param">evaluator</em>, <em class="sig-param">operators</em>, <em class="sig-param">policy</em>, <em class="sig-param">validator</em>, <em class="sig-param">maximise=True</em>, <em class="sig-param">parent=None</em>, <em class="sig-param">verbose=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#HillClimberBestImprovment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment" title="Permalink to this definition">¶</a></dt>
<dd><p>Hill Climber Best Improvment used as exploitation optimisation algorithm</p>
<ul class="simple">
<li><p>First, this algorithm do a neighborhood exploration of a new generated solution (by doing operation on the current solution obtained) in order to find the best solution from the neighborhood space.</p></li>
<li><p>Then replace the best solution found from the neighbordhood space as current solution to use.</p></li>
<li><p>And do these steps until a number of evaluation (stopping criterion) is reached.</p></li>
</ul>
<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.initalizer">
<code class="sig-name descname">initalizer</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.initalizer" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function strategy to initialize solution</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.evaluator">
<code class="sig-name descname">evaluator</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.evaluator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function in order to obtained fitness (mono or multiple objectives)</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.operators">
<code class="sig-name descname">operators</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.operators" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Operator]} – list of operator to use when launching algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.policy">
<code class="sig-name descname">policy</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.policy" title="Permalink to this definition">¶</a></dt>
<dd><p>{Policy} – Policy class implementation strategy to select operators</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.validator">
<code class="sig-name descname">validator</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.validator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function to check if solution is valid or not under some constraints</p>
</dd></dl>

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

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.currentSolution">
<code class="sig-name descname">currentSolution</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.currentSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – current solution managed for current evaluation</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.bestSolution">
<code class="sig-name descname">bestSolution</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.bestSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – best solution found so far during running algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.callbacks">
<code class="sig-name descname">callbacks</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.callbacks" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and <cite>load</cite> when initializing algorithm</p>
</dd></dl>

<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">random</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.crossovers</span> <span class="kn">import</span> <span class="n">SimpleCrossover</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.mutators</span> <span class="kn">import</span> <span class="n">SimpleMutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># policy import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.policies.classicals</span> <span class="kn">import</span> <span class="n">RandomPolicy</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># solution and algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.solutions.discrete</span> <span class="kn">import</span> <span class="n">BinarySolution</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.algorithms.mono</span> <span class="kn">import</span> <span class="n">HillClimberBestImprovment</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.evaluators.discrete.mono</span> <span class="kn">import</span> <span class="n">KnapsackEvaluator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator initialization (worths objects passed into data)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">problem_size</span> <span class="o">=</span> <span class="mi">20</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">worths</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">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">evaluator</span> <span class="o">=</span> <span class="n">KnapsackEvaluator</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;worths&#39;</span><span class="p">:</span> <span class="n">worths</span><span class="p">})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># validator specification (based on weights of each objects)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</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">30</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">validator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">solution</span><span class="p">:</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">sum</span><span class="p">([</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">value</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="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">200</span> <span class="k">else</span> <span class="kc">False</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># initializer function with lambda function</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">initializer</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="o">=</span><span class="mi">20</span><span class="p">:</span> <span class="n">BinarySolution</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">validator</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators list with crossover and mutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">SimpleCrossover</span><span class="p">(),</span> <span class="n">SimpleMutation</span><span class="p">()]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">policy</span> <span class="o">=</span> <span class="n">RandomPolicy</span><span class="p">(</span><span class="n">operators</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">algo</span> <span class="o">=</span> <span class="n">HillClimberBestImprovment</span><span class="p">(</span><span class="n">initializer</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="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># run the algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</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">100</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</span><span class="o">.</span><span class="n">_score</span>
<span class="go">104</span>
</pre></div>
</div>
<dl class="method">
<dt id="macop.algorithms.mono.HillClimberBestImprovment.run">
<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">evaluations</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#HillClimberBestImprovment.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.HillClimberBestImprovment.run" title="Permalink to this definition">¶</a></dt>
<dd><p>Run the local search algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>evaluations</strong> – {int} – number of Local search evaluations</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>{Solution} – best solution found</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment">
<em class="property">class </em><code class="sig-prename descclassname">macop.algorithms.mono.</code><code class="sig-name descname">HillClimberFirstImprovment</code><span class="sig-paren">(</span><em class="sig-param">initializer</em>, <em class="sig-param">evaluator</em>, <em class="sig-param">operators</em>, <em class="sig-param">policy</em>, <em class="sig-param">validator</em>, <em class="sig-param">maximise=True</em>, <em class="sig-param">parent=None</em>, <em class="sig-param">verbose=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#HillClimberFirstImprovment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment" title="Permalink to this definition">¶</a></dt>
<dd><p>Hill Climber First Improvment used as quick exploration optimisation algorithm</p>
<ul class="simple">
<li><p>First, this algorithm do a neighborhood exploration of a new generated solution (by doing operation on the current solution obtained) in order to find a better solution from the neighborhood space.</p></li>
<li><p>Then replace the current solution by the first one from the neighbordhood space which is better than the current solution.</p></li>
<li><p>And do these steps until a number of evaluation (stopping criterion) is reached.</p></li>
</ul>
<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.initalizer">
<code class="sig-name descname">initalizer</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.initalizer" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function strategy to initialize solution</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.evaluator">
<code class="sig-name descname">evaluator</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.evaluator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function in order to obtained fitness (mono or multiple objectives)</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.operators">
<code class="sig-name descname">operators</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.operators" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Operator]} – list of operator to use when launching algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.policy">
<code class="sig-name descname">policy</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.policy" title="Permalink to this definition">¶</a></dt>
<dd><p>{Policy} – Policy class implementation strategy to select operators</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.validator">
<code class="sig-name descname">validator</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.validator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function to check if solution is valid or not under some constraints</p>
</dd></dl>

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

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.currentSolution">
<code class="sig-name descname">currentSolution</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.currentSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – current solution managed for current evaluation</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.bestSolution">
<code class="sig-name descname">bestSolution</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.bestSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – best solution found so far during running algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.callbacks">
<code class="sig-name descname">callbacks</code><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.callbacks" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and <cite>load</cite> when initializing algorithm</p>
</dd></dl>

<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">random</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.crossovers</span> <span class="kn">import</span> <span class="n">SimpleCrossover</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.mutators</span> <span class="kn">import</span> <span class="n">SimpleMutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># policy import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.policies.classicals</span> <span class="kn">import</span> <span class="n">RandomPolicy</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># solution and algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.solutions.discrete</span> <span class="kn">import</span> <span class="n">BinarySolution</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.algorithms.mono</span> <span class="kn">import</span> <span class="n">HillClimberFirstImprovment</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.evaluators.discrete.mono</span> <span class="kn">import</span> <span class="n">KnapsackEvaluator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator initialization (worths objects passed into data)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">problem_size</span> <span class="o">=</span> <span class="mi">20</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">worths</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">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">evaluator</span> <span class="o">=</span> <span class="n">KnapsackEvaluator</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;worths&#39;</span><span class="p">:</span> <span class="n">worths</span><span class="p">})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># validator specification (based on weights of each objects)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</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">30</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">validator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">solution</span><span class="p">:</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">sum</span><span class="p">([</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">value</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="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">200</span> <span class="k">else</span> <span class="kc">False</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># initializer function with lambda function</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">initializer</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="o">=</span><span class="mi">20</span><span class="p">:</span> <span class="n">BinarySolution</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">validator</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators list with crossover and mutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">SimpleCrossover</span><span class="p">(),</span> <span class="n">SimpleMutation</span><span class="p">()]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">policy</span> <span class="o">=</span> <span class="n">RandomPolicy</span><span class="p">(</span><span class="n">operators</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">algo</span> <span class="o">=</span> <span class="n">HillClimberFirstImprovment</span><span class="p">(</span><span class="n">initializer</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="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># run the algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</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">100</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</span><span class="o">.</span><span class="n">_score</span>
<span class="go">128</span>
</pre></div>
</div>
<dl class="method">
<dt id="macop.algorithms.mono.HillClimberFirstImprovment.run">
<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">evaluations</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#HillClimberFirstImprovment.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.HillClimberFirstImprovment.run" title="Permalink to this definition">¶</a></dt>
<dd><p>Run the local search algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>evaluations</strong> – {int} – number of Local search evaluations</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>{Solution} – best solution found</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="macop.algorithms.mono.IteratedLocalSearch">
<em class="property">class </em><code class="sig-prename descclassname">macop.algorithms.mono.</code><code class="sig-name descname">IteratedLocalSearch</code><span class="sig-paren">(</span><em class="sig-param">initializer</em>, <em class="sig-param">evaluator</em>, <em class="sig-param">operators</em>, <em class="sig-param">policy</em>, <em class="sig-param">validator</em>, <em class="sig-param">maximise=True</em>, <em class="sig-param">parent=None</em>, <em class="sig-param">verbose=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#IteratedLocalSearch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch" title="Permalink to this definition">¶</a></dt>
<dd><p>Iterated Local Search used to avoid local optima and increave EvE (Exploration vs Exploitation) compromise</p>
<ul class="simple">
<li><p>A number of evaluations (<cite>ls_evaluations</cite>) is dedicated to local search process, here <cite>HillClimberFirstImprovment</cite> algorithm</p></li>
<li><p>Starting with the new generated solution, the local search algorithm will return a new solution</p></li>
<li><p>If the obtained solution is better than the best solution known into <cite>IteratedLocalSearch</cite>, then the solution is replaced</p></li>
<li><p>Restart this process until stopping critirion (number of expected evaluations)</p></li>
</ul>
<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.initalizer">
<code class="sig-name descname">initalizer</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.initalizer" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function strategy to initialize solution</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.evaluator">
<code class="sig-name descname">evaluator</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.evaluator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function in order to obtained fitness (mono or multiple objectives)</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.operators">
<code class="sig-name descname">operators</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.operators" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Operator]} – list of operator to use when launching algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.policy">
<code class="sig-name descname">policy</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.policy" title="Permalink to this definition">¶</a></dt>
<dd><p>{Policy} – Policy class implementation strategy to select operators</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.validator">
<code class="sig-name descname">validator</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.validator" title="Permalink to this definition">¶</a></dt>
<dd><p>{function} – basic function to check if solution is valid or not under some constraints</p>
</dd></dl>

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

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.currentSolution">
<code class="sig-name descname">currentSolution</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.currentSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – current solution managed for current evaluation</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.bestSolution">
<code class="sig-name descname">bestSolution</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.bestSolution" title="Permalink to this definition">¶</a></dt>
<dd><p>{Solution} – best solution found so far during running algorithm</p>
</dd></dl>

<dl class="attribute">
<dt id="macop.algorithms.mono.IteratedLocalSearch.callbacks">
<code class="sig-name descname">callbacks</code><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.callbacks" title="Permalink to this definition">¶</a></dt>
<dd><p>{[Callback]} – list of Callback class implementation to do some instructions every number of evaluations and <cite>load</cite> when initializing algorithm</p>
</dd></dl>

<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">random</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.crossovers</span> <span class="kn">import</span> <span class="n">SimpleCrossover</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.operators.discrete.mutators</span> <span class="kn">import</span> <span class="n">SimpleMutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># policy import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.policies.classicals</span> <span class="kn">import</span> <span class="n">RandomPolicy</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># solution and algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.solutions.discrete</span> <span class="kn">import</span> <span class="n">BinarySolution</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.algorithms.mono</span> <span class="kn">import</span> <span class="n">IteratedLocalSearch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator import</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">macop.evaluators.discrete.mono</span> <span class="kn">import</span> <span class="n">KnapsackEvaluator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># evaluator initialization (worths objects passed into data)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">problem_size</span> <span class="o">=</span> <span class="mi">20</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">worths</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">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">evaluator</span> <span class="o">=</span> <span class="n">KnapsackEvaluator</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;worths&#39;</span><span class="p">:</span> <span class="n">worths</span><span class="p">})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># validator specification (based on weights of each objects)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</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">30</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">problem_size</span><span class="p">)</span> <span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">validator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">solution</span><span class="p">:</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">sum</span><span class="p">([</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">value</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="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">200</span> <span class="k">else</span> <span class="kc">False</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># initializer function with lambda function</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">initializer</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="o">=</span><span class="mi">20</span><span class="p">:</span> <span class="n">BinarySolution</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">validator</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># operators list with crossover and mutation</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">SimpleCrossover</span><span class="p">(),</span> <span class="n">SimpleMutation</span><span class="p">()]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">policy</span> <span class="o">=</span> <span class="n">RandomPolicy</span><span class="p">(</span><span class="n">operators</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">algo</span> <span class="o">=</span> <span class="n">IteratedLocalSearch</span><span class="p">(</span><span class="n">initializer</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="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># run the algorithm</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</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">100</span><span class="p">,</span> <span class="n">ls_evaluations</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">solution</span><span class="o">.</span><span class="n">_score</span>
<span class="go">137</span>
</pre></div>
</div>
<dl class="method">
<dt id="macop.algorithms.mono.IteratedLocalSearch.run">
<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">evaluations</em>, <em class="sig-param">ls_evaluations=100</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/macop/algorithms/mono.html#IteratedLocalSearch.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#macop.algorithms.mono.IteratedLocalSearch.run" title="Permalink to this definition">¶</a></dt>
<dd><p>Run the iterated local search algorithm using local search (EvE compromise)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>evaluations</strong> – {int} – number of global evaluations for ILS</p></li>
<li><p><strong>ls_evaluations</strong> – {int} – number of Local search evaluations (default: 100)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>{Solution} – best solution found</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</div>


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