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diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/DEGTBehavior.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/DEGTBehavior.java
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+/**
+ * Description: The description of differential evolution Generate-and-Test Behavior.
+
+  #Supported parameters:
+  NAME    VALUE_type   Range      DefaultV        Description
+  FACTOR   real        (0, 1.2]   0.5  	        DEAgent: scale constant
+  CR       real        [0, 1]     0.9  	        DEAgent: crossover constant
+  //Other choices for FACTOR and CR: (0.5, 0.1)
+
+ *
+ * @ Author        Create/Modi     Note
+ * Xiaofeng Xie    May 11, 2004
+ * Xiaofeng Xie    Jul 01, 2008
+ *
+ * @version 1.0
+ * @Since MAOS1.0
+ *
+ * @References:
+ * [1] Storn R, Price K. Differential evolution - a simple and efficient
+ *     heuristic for global optimization over continuous spaces. Journal of
+ *     Global Optimization, 1997, 11: 341�C359
+ *     @ The original differential evolution idea
+ * [2] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
+ *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
+ *     Seattle, WA, USA, 2004: 238-250
+ *     -> a generate-and-test behavior
+ */
+
+package net.adaptivebox.deps.behavior;
+
+import net.adaptivebox.goodness.*;
+import net.adaptivebox.global.*;
+import net.adaptivebox.knowledge.*;
+import net.adaptivebox.problem.*;
+import net.adaptivebox.space.*;
+
+public class DEGTBehavior extends AbsGTBehavior implements ILibEngine {
+  public int DVNum = 2;  //Number of differential vectors, normally be 1 or 2
+  public double FACTOR = 0.5; //scale constant: (0, 1.2], normally be 0.5
+  public double CR = 0.9;     //crossover constant: [0, 1], normally be 0.1 or 0.9
+
+  //the own memory: store the point that generated in last learning cycle
+  protected SearchPoint pbest_t;
+
+  public void setPbest(SearchPoint pbest) {
+    pbest_t = pbest;
+  }
+
+  public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
+    SearchPoint gbest_t = socialLib.getGbest();
+    
+    BasicPoint[] referPoints = getReferPoints();
+    int DIMENSION = problemEncoder.getDesignSpace().getDimension();
+    int rj = RandomGenerator.intRangeRandom(0, DIMENSION-1);
+    for (int k=0; k<DIMENSION; k++) {
+      if (Math.random()<CR || k == DIMENSION-1) {
+        double Dabcd = 0;
+        for(int i=0; i<referPoints.length; i++) {
+          Dabcd += Math.pow(-1, i%2)*referPoints[i].getLocation()[rj];
+        }
+        trailPoint.getLocation()[rj] = gbest_t.getLocation()[rj]+FACTOR*Dabcd;
+      } else {
+        trailPoint.getLocation()[rj] = pbest_t.getLocation()[rj];
+      }
+      rj = (rj+1)%DIMENSION;
+    }
+  }
+
+  public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
+    Library.replace(qualityComparator, trailPoint, pbest_t);
+  }
+
+  protected SearchPoint[] getReferPoints() {
+    SearchPoint[] referPoints = new SearchPoint[DVNum*2];
+    for(int i=0; i<referPoints.length; i++) {
+      referPoints[i] = socialLib.getSelectedPoint(RandomGenerator.intRangeRandom(0, socialLib.getPopSize()-1));
+    }
+    return referPoints;
+  }
+}
+