added (1+1)-EA experiments

README.md

@@ -45,15 +45,15 @@ First, you need to add the following repository, which is a repository that can
 ```
 
 Than you can add the dependency on our `aitoa-code` repository into your `dependencies` section.
-Here, `0.8.68` is the current version of `aitoa-code`.
+Here, `0.8.69` is the current version of `aitoa-code`.
 Notice that you may have more dependencies in your `dependencies` section, say on `junit`, but here I just put the one for `aitoa-code` as example.
 
 ```xml
 <dependencies>
   <dependency>
     <groupId>com.github.thomasWeise</groupId>
     <artifactId>aitoa-code</artifactId>
-    <version>0.8.68</version>
+    <version>0.8.69</version>
   </dependency>
 </dependencies>
 ```

pom.xml

@@ -5,7 +5,7 @@
 
 	<groupId>aitoa</groupId>
 	<artifactId>aitoa-code</artifactId>
-	<version>0.8.68</version>
+	<version>0.8.69</version>
 	<packaging>jar</packaging>
 	<name>aitoa-code</name>
 	<description>Example Source Codes from the Book "Introduction to Optimization Algorithms"</description>

src/main/java/aitoa/algorithms/EA1p1.java

@@ -28,8 +28,9 @@
  * @param <Y>
  *          the solution space
  */
+// start relevant
 public final class EA1p1<X, Y> extends Metaheuristic1<X, Y> {
-
+// end relevant
   /**
    * Create the (1+1) EA
    *
@@ -45,8 +46,9 @@ public EA1p1(final INullarySearchOperator<X> pNullary,
 
   /** {@inheritDoc} */
   @Override
+// start relevant
   public void solve(final IBlackBoxProcess<X, Y> process) {
-// init local variables xCur, xBest, nullary, unary, random
+// initialize local variables xCur, xBest, random
     final X xCur = process.getSearchSpace().create();
     final X xBest = process.getSearchSpace().create();
     final Random random = process.getRandom();// get random gen
@@ -60,13 +62,14 @@ public void solve(final IBlackBoxProcess<X, Y> process) {
       this.unary.apply(xBest, xCur, random);
 // map xCur from X to Y and evaluate candidate solution
       final double fCur = process.evaluate(xCur);
-      if (fCur <= fBest) { // we found a better solution
+      if (fCur <= fBest) { // we found a not-worse solution
 // remember best objective value and copy xCur to xBest
         fBest = fCur;
         process.getSearchSpace().copy(xCur, xBest);
       } // otherwise, i.e., fCur > fBest: just forget xCur
     } // until time is up
   } // process will have remembered the best candidate solution
+// end relevant
 
   /** {@inheritDoc} */
   @Override
@@ -95,4 +98,6 @@ public void printSetup(final Writer output)
     output.write(LogFormat.mapEntry("restarts", false)); //$NON-NLS-1$
     output.write(System.lineSeparator());
   }
+// start relevant
 }
+// end relevant

src/main/java/aitoa/algorithms/HillClimber.java

@@ -47,7 +47,7 @@ public HillClimber(final INullarySearchOperator<X> pNullary,
   @Override
 // start relevant
   public void solve(final IBlackBoxProcess<X, Y> process) {
-// init local variables xCur, xBest, random
+// initialize local variables xCur, xBest, random
 // end relevant
     final X xCur = process.getSearchSpace().create();
     final X xBest = process.getSearchSpace().create();

src/main/java/aitoa/algorithms/HillClimber2.java

@@ -29,9 +29,10 @@
  * @param <Y>
  *          the solution space
  */
+// start relevant
 public final class HillClimber2<X, Y>
     extends Metaheuristic1<X, Y> {
-
+// end relevant
   /**
    * Create the hill climber
    *
@@ -51,9 +52,9 @@ public HillClimber2(final INullarySearchOperator<X> pNullary,
 
   /** {@inheritDoc} */
   @Override
+// start relevant
   public void solve(final IBlackBoxProcess<X, Y> process) {
-// init local variables xCur, xBest, n random, fBest, improved:
-// omitted here for brevity
+// init local variables xCur, xBest, n random, fBest, improved
     final X xCur = process.getSearchSpace().create();
     final X xBest = process.getSearchSpace().create();
     final Random random = process.getRandom();// get random gen
@@ -81,13 +82,15 @@ public void solve(final IBlackBoxProcess<X, Y> process) {
       });
 // repeat until time is up or no further improvement possible
     }
-
   } // process will have remembered the best candidate solution
+// end relevant
 
   /** {@inheritDoc} */
   @Override
   public String toString() {
     return Experiment.nameFromObjectsMerge("hc2f", //$NON-NLS-1$
         this.unary);
   }
+// start relevant
 }
+// end relevant

src/main/java/aitoa/algorithms/HillClimber2WithRestarts.java

@@ -64,7 +64,7 @@ public HillClimber2WithRestarts(
 // start relevant
   public void solve(final IBlackBoxProcess<X, Y> process) {
 // initialization of local variables xCur, xBest, random omitted
-// for brevety
+// for brevity
 // end relevant
     final X xCur = process.getSearchSpace().create();
     final X xBest = process.getSearchSpace().create();

src/main/java/aitoa/algorithms/HillClimberWithRestarts.java

@@ -115,8 +115,8 @@ public void solve(final IBlackBoxProcess<X, Y> process) {
     final Random random = process.getRandom();// get random gen
 
 // start relevant
-// omitted: initialize local variables xCur, xBest, random,
-// failsBeforeRestart, and failCounter=0
+// omitted for brevity initialize local variables xCur, xBest,
+// random, failsBeforeRestart, and failCounter=0
     while (!(process.shouldTerminate())) { // outer loop: restart
       this.nullary.apply(xBest, random); // start=random solution
       double fBest = process.evaluate(xBest); // evaluate it

src/main/java/aitoa/examples/jssp/EJSSPExperimentStage.java

@@ -8,6 +8,7 @@
 import java.util.stream.Stream;
 
 import aitoa.algorithms.EA;
+import aitoa.algorithms.EA1p1;
 import aitoa.algorithms.EAWithClearing;
 import aitoa.algorithms.EDA;
 import aitoa.algorithms.EDAWithClearing;
@@ -439,6 +440,36 @@ public enum EJSSPExperimentStage implements
       }
       return list.stream();
     }
+  },
+
+  /**
+   * The tenth stage: 1+1-EA with FFA
+   */
+  STAGE_10 {
+
+    /**
+     * Get a stream of algorithm suppliers for a given problem
+     *
+     * @param problem
+     *          the problem
+     * @return the stream of suppliers
+     */
+    @Override
+    public
+        Stream<Supplier<
+            IMetaheuristic<int[], JSSPCandidateSolution>>>
+        getAlgorithms(//
+            final JSSPMakespanObjectiveFunction problem) {
+
+      return Stream.of(
+          () -> new EA1p1<>(
+              new JSSPNullaryOperator(problem.instance),
+              new JSSPUnaryOperator1Swap()),
+          () -> new EA1p1<>(
+              new JSSPNullaryOperator(problem.instance),
+              new JSSPUnaryOperatorNSwap()));
+
+    }
   };
 
   /** the instances to be used */