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  <h1>Source code for macop.algorithms.mono</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Mono-objective available algorithms</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="c1"># main imports</span>
<span class="kn">import</span> <span class="nn">logging</span>

<span class="c1"># module imports</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">Algorithm</span>


<div class="viewcode-block" id="HillClimberFirstImprovment"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.HillClimberFirstImprovment">[docs]</a><span class="k">class</span> <span class="nc">HillClimberFirstImprovment</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Hill Climber First Improvment used as quick exploration optimisation algorithm</span>

<span class="sd">    - 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.</span>
<span class="sd">    - Then replace the current solution by the first one from the neighbordhood space which is better than the current solution.</span>
<span class="sd">    - And do these steps until a number of evaluation (stopping criterion) is reached.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        initalizer: {function} -- basic function strategy to initialize solution</span>
<span class="sd">        evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives)</span>
<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 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>
<span class="sd">    </span>
<span class="sd">    Example:</span>

<span class="sd">    &gt;&gt;&gt; import random</span>
<span class="sd">    &gt;&gt;&gt; # operators import</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.crossovers import SimpleCrossover</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.mutators import SimpleMutation</span>
<span class="sd">    &gt;&gt;&gt; # policy import</span>
<span class="sd">    &gt;&gt;&gt; from macop.policies.classicals import RandomPolicy</span>
<span class="sd">    &gt;&gt;&gt; # solution and algorithm</span>
<span class="sd">    &gt;&gt;&gt; from macop.solutions.discrete import BinarySolution</span>
<span class="sd">    &gt;&gt;&gt; from macop.algorithms.mono import HillClimberFirstImprovment</span>
<span class="sd">    &gt;&gt;&gt; # evaluator import</span>
<span class="sd">    &gt;&gt;&gt; from macop.evaluators.discrete.mono import KnapsackEvaluator</span>
<span class="sd">    &gt;&gt;&gt; # evaluator initialization (worths objects passed into data)</span>
<span class="sd">    &gt;&gt;&gt; problem_size = 20</span>
<span class="sd">    &gt;&gt;&gt; worths = [ random.randint(0, 20) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; evaluator = KnapsackEvaluator(data={&#39;worths&#39;: worths})</span>
<span class="sd">    &gt;&gt;&gt; # validator specification (based on weights of each objects)</span>
<span class="sd">    &gt;&gt;&gt; weights = [ random.randint(5, 30) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) &lt; 200 else False</span>
<span class="sd">    &gt;&gt;&gt; # initializer function with lambda function</span>
<span class="sd">    &gt;&gt;&gt; initializer = lambda x=20: BinarySolution.random(x, validator)</span>
<span class="sd">    &gt;&gt;&gt; # operators list with crossover and mutation</span>
<span class="sd">    &gt;&gt;&gt; operators = [SimpleCrossover(), SimpleMutation()]</span>
<span class="sd">    &gt;&gt;&gt; policy = RandomPolicy(operators)</span>
<span class="sd">    &gt;&gt;&gt; algo = HillClimberFirstImprovment(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)</span>
<span class="sd">    &gt;&gt;&gt; # run the algorithm</span>
<span class="sd">    &gt;&gt;&gt; solution = algo.run(100)</span>
<span class="sd">    &gt;&gt;&gt; solution._score</span>
<span class="sd">    128</span>
<span class="sd">    &quot;&quot;&quot;</span>
<div class="viewcode-block" id="HillClimberFirstImprovment.run"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.HillClimberFirstImprovment.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">evaluations</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Run the local search algorithm</span>

<span class="sd">        Args:</span>
<span class="sd">            evaluations: {int} -- number of Local search evaluations</span>
<span class="sd">            </span>
<span class="sd">        Returns:</span>
<span class="sd">            {Solution} -- best solution found</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="c1"># by default use of mother method to initialize variables</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">evaluations</span><span class="p">)</span>

        <span class="c1"># initialize current solution and best solution</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initRun</span><span class="p">()</span>

        <span class="n">solutionSize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="o">.</span><span class="n">_size</span>

        <span class="c1"># local search algorithm implementation</span>
        <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</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="n">solutionSize</span><span class="p">):</span>

                <span class="c1"># update current solution using policy</span>
                <span class="n">newSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="p">)</span>

                <span class="c1"># if better solution than currently, replace it and stop current exploration (first improvment)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isBetter</span><span class="p">(</span><span class="n">newSolution</span><span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span> <span class="o">=</span> <span class="n">newSolution</span>
                    <span class="k">break</span>

                <span class="c1"># increase number of evaluations</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">increaseEvaluation</span><span class="p">()</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">progress</span><span class="p">()</span>
                <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;---- Current </span><span class="si">{</span><span class="n">newSolution</span><span class="si">}</span><span class="s2"> - SCORE </span><span class="si">{</span><span class="n">newSolution</span><span class="o">.</span><span class="n">fitness</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

                <span class="c1"># stop algorithm if necessary</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">():</span>
                    <span class="k">break</span>

            <span class="c1"># set new current solution using best solution found in this neighbor search</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span>

        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;End of </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">, best solution found </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span></div></div>


<div class="viewcode-block" id="HillClimberBestImprovment"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.HillClimberBestImprovment">[docs]</a><span class="k">class</span> <span class="nc">HillClimberBestImprovment</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Hill Climber Best Improvment used as exploitation optimisation algorithm</span>

<span class="sd">    - 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.</span>
<span class="sd">    - Then replace the best solution found from the neighbordhood space as current solution to use.</span>
<span class="sd">    - And do these steps until a number of evaluation (stopping criterion) is reached.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        initalizer: {function} -- basic function strategy to initialize solution</span>
<span class="sd">        evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives)</span>
<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 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>
<span class="sd">    </span>
<span class="sd">    Example:</span>

<span class="sd">    &gt;&gt;&gt; import random</span>
<span class="sd">    &gt;&gt;&gt; # operators import</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.crossovers import SimpleCrossover</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.mutators import SimpleMutation</span>
<span class="sd">    &gt;&gt;&gt; # policy import</span>
<span class="sd">    &gt;&gt;&gt; from macop.policies.classicals import RandomPolicy</span>
<span class="sd">    &gt;&gt;&gt; # solution and algorithm</span>
<span class="sd">    &gt;&gt;&gt; from macop.solutions.discrete import BinarySolution</span>
<span class="sd">    &gt;&gt;&gt; from macop.algorithms.mono import HillClimberBestImprovment</span>
<span class="sd">    &gt;&gt;&gt; # evaluator import</span>
<span class="sd">    &gt;&gt;&gt; from macop.evaluators.discrete.mono import KnapsackEvaluator</span>
<span class="sd">    &gt;&gt;&gt; # evaluator initialization (worths objects passed into data)</span>
<span class="sd">    &gt;&gt;&gt; problem_size = 20</span>
<span class="sd">    &gt;&gt;&gt; worths = [ random.randint(0, 20) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; evaluator = KnapsackEvaluator(data={&#39;worths&#39;: worths})</span>
<span class="sd">    &gt;&gt;&gt; # validator specification (based on weights of each objects)</span>
<span class="sd">    &gt;&gt;&gt; weights = [ random.randint(5, 30) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) &lt; 200 else False</span>
<span class="sd">    &gt;&gt;&gt; # initializer function with lambda function</span>
<span class="sd">    &gt;&gt;&gt; initializer = lambda x=20: BinarySolution.random(x, validator)</span>
<span class="sd">    &gt;&gt;&gt; # operators list with crossover and mutation</span>
<span class="sd">    &gt;&gt;&gt; operators = [SimpleCrossover(), SimpleMutation()]</span>
<span class="sd">    &gt;&gt;&gt; policy = RandomPolicy(operators)</span>
<span class="sd">    &gt;&gt;&gt; algo = HillClimberBestImprovment(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)</span>
<span class="sd">    &gt;&gt;&gt; # run the algorithm</span>
<span class="sd">    &gt;&gt;&gt; solution = algo.run(100)</span>
<span class="sd">    &gt;&gt;&gt; solution._score</span>
<span class="sd">    104</span>
<span class="sd">    &quot;&quot;&quot;</span>
<div class="viewcode-block" id="HillClimberBestImprovment.run"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.HillClimberBestImprovment.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">evaluations</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Run the local search algorithm</span>

<span class="sd">        Args:</span>
<span class="sd">            evaluations: {int} -- number of Local search evaluations</span>
<span class="sd">            </span>
<span class="sd">        Returns:</span>
<span class="sd">            {Solution} -- best solution found</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="c1"># by default use of mother method to initialize variables</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">evaluations</span><span class="p">)</span>

        <span class="c1"># initialize current solution and best solution</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initRun</span><span class="p">()</span>

        <span class="n">solutionSize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="o">.</span><span class="n">_size</span>

        <span class="c1"># local search algorithm implementation</span>
        <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</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="n">solutionSize</span><span class="p">):</span>

                <span class="c1"># update current solution using policy</span>
                <span class="n">newSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span><span class="p">)</span>

                <span class="c1"># if better solution than currently, replace it</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isBetter</span><span class="p">(</span><span class="n">newSolution</span><span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span> <span class="o">=</span> <span class="n">newSolution</span>

                <span class="c1"># increase number of evaluations</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">increaseEvaluation</span><span class="p">()</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">progress</span><span class="p">()</span>
                <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;---- Current </span><span class="si">{</span><span class="n">newSolution</span><span class="si">}</span><span class="s2"> - SCORE </span><span class="si">{</span><span class="n">newSolution</span><span class="o">.</span><span class="n">fitness</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

                <span class="c1"># stop algorithm if necessary</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">():</span>
                    <span class="k">break</span>

            <span class="c1"># set new current solution using best solution found in this neighbor search</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_currentSolution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span>

        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;End of </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">, best solution found </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span></div></div>


<div class="viewcode-block" id="IteratedLocalSearch"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.IteratedLocalSearch">[docs]</a><span class="k">class</span> <span class="nc">IteratedLocalSearch</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Iterated Local Search used to avoid local optima and increave EvE (Exploration vs Exploitation) compromise</span>

<span class="sd">    - A number of evaluations (`ls_evaluations`) is dedicated to local search process, here `HillClimberFirstImprovment` algorithm</span>
<span class="sd">    - Starting with the new generated solution, the local search algorithm will return a new solution</span>
<span class="sd">    - If the obtained solution is better than the best solution known into `IteratedLocalSearch`, then the solution is replaced</span>
<span class="sd">    - Restart this process until stopping critirion (number of expected evaluations)</span>

<span class="sd">    Attributes:</span>
<span class="sd">        initalizer: {function} -- basic function strategy to initialize solution</span>
<span class="sd">        evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives)</span>
<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 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>
<span class="sd">    </span>
<span class="sd">    Example:</span>

<span class="sd">    &gt;&gt;&gt; import random</span>
<span class="sd">    &gt;&gt;&gt; # operators import</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.crossovers import SimpleCrossover</span>
<span class="sd">    &gt;&gt;&gt; from macop.operators.discrete.mutators import SimpleMutation</span>
<span class="sd">    &gt;&gt;&gt; # policy import</span>
<span class="sd">    &gt;&gt;&gt; from macop.policies.classicals import RandomPolicy</span>
<span class="sd">    &gt;&gt;&gt; # solution and algorithm</span>
<span class="sd">    &gt;&gt;&gt; from macop.solutions.discrete import BinarySolution</span>
<span class="sd">    &gt;&gt;&gt; from macop.algorithms.mono import IteratedLocalSearch</span>
<span class="sd">    &gt;&gt;&gt; # evaluator import</span>
<span class="sd">    &gt;&gt;&gt; from macop.evaluators.discrete.mono import KnapsackEvaluator</span>
<span class="sd">    &gt;&gt;&gt; # evaluator initialization (worths objects passed into data)</span>
<span class="sd">    &gt;&gt;&gt; problem_size = 20</span>
<span class="sd">    &gt;&gt;&gt; worths = [ random.randint(0, 20) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; evaluator = KnapsackEvaluator(data={&#39;worths&#39;: worths})</span>
<span class="sd">    &gt;&gt;&gt; # validator specification (based on weights of each objects)</span>
<span class="sd">    &gt;&gt;&gt; weights = [ random.randint(5, 30) for i in range(problem_size) ]</span>
<span class="sd">    &gt;&gt;&gt; validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) &lt; 200 else False</span>
<span class="sd">    &gt;&gt;&gt; # initializer function with lambda function</span>
<span class="sd">    &gt;&gt;&gt; initializer = lambda x=20: BinarySolution.random(x, validator)</span>
<span class="sd">    &gt;&gt;&gt; # operators list with crossover and mutation</span>
<span class="sd">    &gt;&gt;&gt; operators = [SimpleCrossover(), SimpleMutation()]</span>
<span class="sd">    &gt;&gt;&gt; policy = RandomPolicy(operators)</span>
<span class="sd">    &gt;&gt;&gt; algo = IteratedLocalSearch(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)</span>
<span class="sd">    &gt;&gt;&gt; # run the algorithm</span>
<span class="sd">    &gt;&gt;&gt; solution = algo.run(100, ls_evaluations=10)</span>
<span class="sd">    &gt;&gt;&gt; solution._score</span>
<span class="sd">    137</span>
<span class="sd">    &quot;&quot;&quot;</span>
<div class="viewcode-block" id="IteratedLocalSearch.run"><a class="viewcode-back" href="../../../macop/macop.algorithms.mono.html#macop.algorithms.mono.IteratedLocalSearch.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">evaluations</span><span class="p">,</span> <span class="n">ls_evaluations</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Run the iterated local search algorithm using local search (EvE compromise)</span>

<span class="sd">        Args:</span>
<span class="sd">            evaluations: {int} -- number of global evaluations for ILS</span>
<span class="sd">            ls_evaluations: {int} -- number of Local search evaluations (default: 100)</span>

<span class="sd">        Returns:</span>
<span class="sd">            {Solution} -- best solution found</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="c1"># by default use of mother method to initialize variables</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">evaluations</span><span class="p">)</span>

        <span class="c1"># enable resuming for ILS</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">resume</span><span class="p">()</span>

        <span class="c1"># initialize current solution</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initRun</span><span class="p">()</span>

        <span class="c1"># passing global evaluation param from ILS</span>
        <span class="n">ls</span> <span class="o">=</span> <span class="n">HillClimberFirstImprovment</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_initializer</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_evaluator</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_operators</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_policy</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_validator</span><span class="p">,</span>
                         <span class="bp">self</span><span class="o">.</span><span class="n">_maximise</span><span class="p">,</span>
                         <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_verbose</span><span class="p">,</span>
                         <span class="n">parent</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span>

        <span class="c1"># add same callbacks</span>
        <span class="k">for</span> <span class="n">callback</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_callbacks</span><span class="p">:</span>
            <span class="n">ls</span><span class="o">.</span><span class="n">addCallback</span><span class="p">(</span><span class="n">callback</span><span class="p">)</span>

        <span class="c1"># local search algorithm implementation</span>
        <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">():</span>

            <span class="c1"># create and search solution from local search</span>
            <span class="n">newSolution</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">ls_evaluations</span><span class="p">)</span>

            <span class="c1"># if better solution than currently, replace it</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isBetter</span><span class="p">(</span><span class="n">newSolution</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span> <span class="o">=</span> <span class="n">newSolution</span>

            <span class="c1"># number of evaluatins increased from LocalSearch</span>
            <span class="c1"># increase number of evaluations and progress are then not necessary there</span>
            <span class="c1">#self.increaseEvaluation()</span>
            <span class="c1">#self.progress()</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">information</span><span class="p">()</span>

        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;End of </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">, best solution found </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">end</span><span class="p">()</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bestSolution</span></div></div>
</pre></div>

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