Merge pull request #445 from cicirello/patch-deprecate

Deprecated shifted fitness variations of fitness weighed selection operators

CHANGELOG.md

@@ -14,6 +14,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Changed
 
 ### Deprecated
+* ShiftedFitnessProportionalSelection, replaced by combination of FitnessShifter with FitnessProportionalSelection.
+* ShiftedStochasticUniversalSampling, replaced by combination of FitnessShifter with StochasticUniversalSampling.
+* BiasedShiftedFitnessProportionalSelection, replaced by combination of FitnessShifter with BiasedFitnessProportionalSelection.
+* BiasedShiftedStochasticUniversalSampling, replaced by combination of FitnessShifter with BiasedStochasticUniversalSampling.
 
 ### Removed
 

src/main/java/org/cicirello/search/evo/BiasedFitnessProportionalSelection.java

@@ -36,7 +36,8 @@
  *
  * <p><b>This selection operator requires positive fitness values. Behavior is undefined if any 
  * fitness values are less than or equal to 0.</b> If your fitness values may be negative,
- * use {@link BiasedShiftedFitnessProportionalSelection} instead.</p>
+ * you can use {@link FitnessShifter}, which transforms fitness values such that minimum fitness
+ * equals 1.</p>
  *
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M lg N), assuming the bias function has a constant runtime.</p>

src/main/java/org/cicirello/search/evo/BiasedShiftedFitnessProportionalSelection.java

@@ -46,9 +46,13 @@
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M lg N), assuming the bias function has a constant runtime.</p>
  *
+ * @deprecated Instead of this class, you should use a combination of {@link FitnessShifter} and 
+ * {@link BiasedFitnessProportionalSelection}. This class is scheduled for removal in release 6.0.0.
+ *
  * @author <a href=https://www.cicirello.org/ target=_top>Vincent A. Cicirello</a>, 
  * <a href=https://www.cicirello.org/ target=_top>https://www.cicirello.org/</a>
  */
+@Deprecated
 public final class BiasedShiftedFitnessProportionalSelection extends FitnessProportionalSelection {
 
 	private final FitnessBiasFunction bias;

src/main/java/org/cicirello/search/evo/BiasedShiftedStochasticUniversalSampling.java

@@ -58,9 +58,13 @@
  * O(N + M), which includes the need to generate only a single random double, and O(M) ints. This assumes
  * that the bias function has a constant runtime.</p>
  *
+ * @deprecated Instead of this class, you should use a combination of {@link FitnessShifter} and 
+ * {@link BiasedStochasticUniversalSampling}. This class is scheduled for removal in release 6.0.0.
+ *
  * @author <a href=https://www.cicirello.org/ target=_top>Vincent A. Cicirello</a>, 
  * <a href=https://www.cicirello.org/ target=_top>https://www.cicirello.org/</a>
  */
+@Deprecated
 public final class BiasedShiftedStochasticUniversalSampling extends StochasticUniversalSampling {
 
 	private final FitnessBiasFunction bias;

src/main/java/org/cicirello/search/evo/BiasedStochasticUniversalSampling.java

@@ -48,7 +48,8 @@
  *
  * <p><b>This selection operator requires positive fitness values. Behavior is undefined if any 
  * fitness values are less than or equal to 0.</b> If your fitness values may be negative,
- * use {@link BiasedShiftedStochasticUniversalSampling} instead.</p>
+ * you can use {@link FitnessShifter}, which transforms fitness values such that minimum fitness
+ * equals 1.</p>
  *
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M), which includes the need to generate only a single random double, and O(M) ints. This assumes

src/main/java/org/cicirello/search/evo/FitnessProportionalSelection.java

@@ -33,7 +33,8 @@
  *
  * <p><b>This selection operator requires positive fitness values. Behavior is undefined if any 
  * fitness values are less than or equal to 0.</b> If your fitness values may be negative,
- * use {@link ShiftedFitnessProportionalSelection} instead.</p>
+ * you can use {@link FitnessShifter}, which transforms fitness values such that minimum fitness
+ * equals 1.</p>
  *
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M lg N).</p>

src/main/java/org/cicirello/search/evo/ShiftedFitnessProportionalSelection.java

@@ -42,9 +42,13 @@
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M lg N).</p>
  *
+ * @deprecated Instead of this class, you should use a combination of {@link FitnessShifter} and 
+ * {@link FitnessProportionalSelection}. This class is scheduled for removal in release 6.0.0.
+ *
  * @author <a href=https://www.cicirello.org/ target=_top>Vincent A. Cicirello</a>, 
  * <a href=https://www.cicirello.org/ target=_top>https://www.cicirello.org/</a>
  */
+@Deprecated
 public final class ShiftedFitnessProportionalSelection extends FitnessProportionalSelection {
 
 	/**

src/main/java/org/cicirello/search/evo/ShiftedStochasticUniversalSampling.java

@@ -51,9 +51,13 @@
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M), which includes the need to generate only a single random double, and O(M) ints.</p>
  *
+ * @deprecated Instead of this class, you should use a combination of {@link FitnessShifter} and 
+ * {@link StochasticUniversalSampling}. This class is scheduled for removal in release 6.0.0.
+ *
  * @author <a href=https://www.cicirello.org/ target=_top>Vincent A. Cicirello</a>, 
  * <a href=https://www.cicirello.org/ target=_top>https://www.cicirello.org/</a>
  */
+@Deprecated
 public final class ShiftedStochasticUniversalSampling extends StochasticUniversalSampling {
 
 	/**

src/main/java/org/cicirello/search/evo/StochasticUniversalSampling.java

@@ -45,7 +45,8 @@
  *
  * <p><b>This selection operator requires positive fitness values. Behavior is undefined if any 
  * fitness values are less than or equal to 0.</b> If your fitness values may be negative,
- * use {@link ShiftedStochasticUniversalSampling} instead.</p>
+ * you can use {@link FitnessShifter}, which transforms fitness values such that minimum fitness
+ * equals 1.</p>
  *
  * <p>The runtime to select M population members from a population of size N is
  * O(N + M), which includes the need to generate only a single random double, and O(M) random ints.</p>