cleanup of comments (e.g., removal of unnecessary "relevant" tags)

thomasWeise · Apr 22, 2020 · 205890c · 205890c
1 parent c1ce4d2
commit 205890c
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Showing 10 changed files with 14 additions and 79 deletions.
diff --git a/src/main/java/aitoa/algorithms/EA1p1.java b/src/main/java/aitoa/algorithms/EA1p1.java
@@ -28,28 +28,26 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class EA1p1<X, Y> implements IMetaheuristic<X, Y> {
-// end relevant
+
   /** create */
   public EA1p1() {
     super();
   }
 
   /** {@inheritDoc} */
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
 // init local variables x_cur, x_best, nullary, unary, random
-// end relevant
+
     final X x_cur = process.getSearchSpace().create();
     final X x_best = process.getSearchSpace().create();
     final INullarySearchOperator<X> nullary =
         process.getNullarySearchOperator(); // get nullary op
     final IUnarySearchOperator<X> unary =
         process.getUnarySearchOperator(); // get unary op
     final Random random = process.getRandom();// get random gen
-// start relevant
+
 // create starting point: a random point in the search space
     nullary.apply(x_best, random); // put random point in x_best
     double f_best = process.evaluate(x_best); // map & evaluate
@@ -66,7 +64,6 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
       } // otherwise, i.e., f_cur > f_best: just forget x_cur
     } // until time is up
   } // process will have remembered the best candidate solution
-// end relevant
 
   /** {@inheritDoc} */
   @Override
@@ -101,6 +98,4 @@ public final void printSetup(final Writer output)
     return IMetaheuristic.getSetupNameWithUnaryOperator(this,
         builder);
   }
-// start relevant
 }
-// end relevant
diff --git a/src/main/java/aitoa/algorithms/EA1p1WithFitness.java b/src/main/java/aitoa/algorithms/EA1p1WithFitness.java
@@ -29,13 +29,11 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class EA1p1WithFitness<X, Y>
     implements IMetaheuristic<X, Y> {
   /** the fitness assignment process */
   public final FitnessAssignmentProcess<? super X> fitness;
 
-// end relevant
   /**
    * create
    *
@@ -51,7 +49,6 @@ public EA1p1WithFitness(
   /** {@inheritDoc} */
   @Override
   @SuppressWarnings("unchecked")
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
 // init local variables pop, nullary, unary, random
 // end relevant

diff --git a/src/main/java/aitoa/algorithms/EAWithRestarts.java b/src/main/java/aitoa/algorithms/EAWithRestarts.java
@@ -141,11 +141,9 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
         final X x = searchSpace.create();
         nullary.apply(x, random);
         population[i] = new Individual<>(x, process.evaluate(x));
-// end relevant
         if (process.shouldTerminate()) {
           return;
         }
-// start relevant
       }
 
       while (nonImprovedGen < this.generationsUntilRestart) {

diff --git a/src/main/java/aitoa/algorithms/EDAWithFitness.java b/src/main/java/aitoa/algorithms/EDAWithFitness.java
@@ -22,10 +22,8 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class EDAWithFitness<X, Y>
     implements IMetaheuristic<X, Y> {
-// end relevant
 
   /** the number of solution to be selected */
   public final int mu;
@@ -100,9 +98,7 @@ public final String toString() {
   /** {@inheritDoc} */
   @SuppressWarnings("unchecked")
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
-// end relevant
 // create local variables
     final Random random = process.getRandom();
     final ISpace<X> searchSpace = process.getSearchSpace();
@@ -113,7 +109,7 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     final FitnessIndividual<X>[] P =
         new FitnessIndividual[this.lambda];
     this.fitness.initialize();
-// start relevant
+
 // local variable initialization omitted for brevity
     Model.initialize(); // initialize model=uniform distribution
 
@@ -122,11 +118,9 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
       final X x = searchSpace.create();
       nullary.apply(x, random);
       P[i] = new FitnessIndividual<>(x, process.evaluate(x));
-// end relevant
       if (process.shouldTerminate()) { // we return
         return; // best solution is stored in process
       }
-// start relevant
     }
 
     for (;;) {// each iteration: update model, sample model
@@ -146,4 +140,3 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     } // the end of the main loop
   }
 }
-// end relevant
diff --git a/src/main/java/aitoa/algorithms/HillClimber2.java b/src/main/java/aitoa/algorithms/HillClimber2.java
@@ -29,23 +29,19 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class HillClimber2<X, Y>
     implements IMetaheuristic<X, Y> {
 
-// end relevant
   /** create */
   public HillClimber2() {
     super();
   }
 
   /** {@inheritDoc} */
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
 // init local variables x_cur, x_best, nullary, unary, random,
 // f_best, improved: omitted here for brevity
-// end relevant
     final X x_cur = process.getSearchSpace().create();
     final X x_best = process.getSearchSpace().create();
     final INullarySearchOperator<X> nullary =
@@ -54,7 +50,7 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
         process.getUnarySearchOperator(); // get unary op
     final Random random = process.getRandom();// get random gen
     boolean improved = true;
-// start relevant
+
 // create starting point: a random point in the search space
     nullary.apply(x_best, random); // put random point in x_best
     final double[] f_best = { process.evaluate(x_best) }; // evaluate
@@ -79,7 +75,6 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     }
 
   } // process will have remembered the best candidate solution
-// end relevant
 
   /** {@inheritDoc} */
   @Override
@@ -94,6 +89,4 @@ public final String toString() {
     return IMetaheuristic.getSetupNameWithUnaryOperator(this,
         builder);
   }
-// start relevant
 }
-// end relevant
diff --git a/src/main/java/aitoa/algorithms/HybridEDA.java b/src/main/java/aitoa/algorithms/HybridEDA.java
@@ -27,10 +27,8 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class HybridEDA<X, Y>
     implements IMetaheuristic<X, Y> {
-// end relevant
 
   /** the number of solution to be selected */
   public final int mu;
@@ -120,9 +118,7 @@ public final String toString() {
   /** {@inheritDoc} */
   @SuppressWarnings("unchecked")
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
-// end relevant
 // create local variables
     final Random random = process.getRandom();
     final ISpace<X> searchSpace = process.getSearchSpace();
@@ -135,7 +131,6 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     final Individual<X>[] P = new Individual[this.lambda];
     final X temp = searchSpace.create();
 
-// start relevant
 // the initialization of local variables is omitted for brevity
     Model.initialize(); // initialize model=uniform distribution
 
@@ -144,11 +139,9 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
       final X x = searchSpace.create();
       nullary.apply(x, random);
       P[i] = new Individual<>(x, process.evaluate(x));
-// end relevant
       if (process.shouldTerminate()) { // we return
         return; // best solution is stored in process
       }
-// start relevant
     }
 
     for (;;) {// each iteration: LS, update model, then sample
@@ -187,4 +180,3 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     } // the end of the main loop
   }
 }
-// end relevant
diff --git a/src/main/java/aitoa/algorithms/HybridEDAWithFitness.java b/src/main/java/aitoa/algorithms/HybridEDAWithFitness.java
@@ -24,10 +24,8 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class HybridEDAWithFitness<X, Y>
     implements IMetaheuristic<X, Y> {
-// end relevant
 
   /** the number of solution to be selected */
   public final int mu;
@@ -127,9 +125,7 @@ public final String toString() {
   /** {@inheritDoc} */
   @SuppressWarnings("unchecked")
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
-// end relevant
 // create local variables
     final Random random = process.getRandom();
     final ISpace<X> searchSpace = process.getSearchSpace();
@@ -144,7 +140,6 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     final X temp = searchSpace.create();
     this.fitness.initialize();
 
-// start relevant
 // the initialization of local variables is omitted for brevity
     Model.initialize(); // initialize model=uniform distribution
 
@@ -153,11 +148,9 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
       final X x = searchSpace.create();
       nullary.apply(x, random);
       P[i] = new FitnessIndividual<>(x, process.evaluate(x));
-// end relevant
       if (process.shouldTerminate()) { // we return
         return; // best solution is stored in process
       }
-// start relevant
     }
 
     for (;;) {// each iteration: LS, update model, then sample
@@ -194,12 +187,9 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
         Model.sample(dest.x, random);
         dest.quality = process.evaluate(dest.x);
       } // the end of the new points generation
-// end relevant
       if (process.shouldTerminate()) { // we return
         return; // best solution is stored in process
       }
-// start relevant
     } // the end of the main loop
   }
 }
-// end relevant
diff --git a/src/main/java/aitoa/algorithms/MA.java b/src/main/java/aitoa/algorithms/MA.java
@@ -142,12 +142,13 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
 
     while (!process.shouldTerminate()) { // main loop
       for (final LSIndividual<X> ind : P) {
+// If ind is not known to be local optimum, refine it with local
+// search à la HillClimber2 for a given number of maximum steps
+// (code omitted for brevity).
+// end relevant
         if (ind.isOptimum) {
           continue;
         }
-// refine ind with local search à la HillClimber2 (code omitted)
-// for a given number of maximum steps
-// end relevant
         int steps = this.maxLSSteps;
         do { // local search in style of HillClimber2
           improved = unary.enumerate(random, ind.x, temp, //
@@ -189,9 +190,10 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
         } while (p2 == p1);
 // perform recombination of the two selected individuals
         binary.apply(sel.x, P[p2].x, dest.x, random);
-// map to solution/schedule and evaluate quality
         dest.quality = process.evaluate(dest.x);
+// end relevant
         dest.isOptimum = false;
+// start relevant
       } // the end of the offspring generation
     } // the end of the main loop
   }

diff --git a/src/main/java/aitoa/algorithms/MAWithClearing.java b/src/main/java/aitoa/algorithms/MAWithClearing.java
@@ -49,10 +49,8 @@
  * @param <Y>
  *          the solution space
  */
-// start relevant
 public final class MAWithClearing<X, Y>
     implements IMetaheuristic<X, Y> {
-// end relevant
 
   /** the number of selected parents */
   public final int mu;
@@ -128,12 +126,7 @@ public final String toString() {
   /** {@inheritDoc} */
   @SuppressWarnings("unchecked")
   @Override
-// start relevant
   public final void solve(final IBlackBoxProcess<X, Y> process) {
-// omitted: initialize local variables random, searchSpace,
-// nullary, unary, binary, and arrays P and P2 of length
-// mu+lambda, and array T to null
-// end relevant
 // create local variables
     final Random random = process.getRandom();
     final ISpace<X> searchSpace = process.getSearchSpace();
@@ -149,18 +142,16 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
         P2 = new LSIndividual[P.length];
     boolean improved = false;
     int p2 = -1;
-// start relevant
+
     restart: while (!process.shouldTerminate()) {
 // first generation: fill population with random individuals
       for (int i = P.length; (--i) >= 0;) {
         final X x = searchSpace.create();
         nullary.apply(x, random);
         P[i] = new LSIndividual<>(x, process.evaluate(x));
-// end relevant
         if (process.shouldTerminate()) {
           return;
         }
-// start relevant
       }
 
       while (!process.shouldTerminate()) { // main loop
@@ -236,7 +227,6 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
       } // the end of the main loop
     } // end of the restart loop
   }
-// end relevant
 
   /** {@inheritDoc} */
   @Override
@@ -245,6 +235,4 @@ public final void solve(final IBlackBoxProcess<X, Y> process) {
     return IMetaheuristic.getSetupNameWithUnaryAndBinaryOperator(//
         this, builder);
   }
-// start relevant
 }
-// end relevant