1 files changed, 56 insertions, 0 deletions
diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/encode/EvalStruct.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/encode/EvalStruct.java
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+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/encode/EvalStruct.java
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+/**
+ * Description: provide the information for evaluating a set of targets values
+ * into encoded information (For formation the goodness landscape by comparing)
+ *
+ * Author          Create/Modi     Note
+ * Xiaofeng Xie    Mar 1, 2003
+ * Xiaofeng Xie    May 11, 2004
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * Please acknowledge the author(s) if you use this code in any way.
+ *
+ * @References:
+ * [1] Deb K. An efficient constraint handling method for genetic algorithms.
+ * Computer Methods in Applied Mechanics and Engineering, 2000, 186(2-4): 311-338
+ */
+
+package net.adaptivebox.encode;
+
+public class EvalStruct {
+  // The information for evaluating all the responses
+  private EvalElement[] evalElems = null;
+
+  public EvalStruct(int elemsNum) {
+    evalElems = new EvalElement[elemsNum];
+  }
+
+  public void setElemAt(EvalElement dim, int index) {
+    evalElems[index] = dim;
+  }
+
+  // convert response values into encoded information double[2]
+  public void evaluate(double[] evalRes, double[] targetValues) {
+    evalRes[0] = evalRes[1] = 0;
+    for (int i = 0; i < evalElems.length; i++) {
+      if (evalElems[i].isOptType()) {
+        // The objectives (OPTIM type)
+        // The multi-objective will be translated into single-objective
+        evalRes[1] += evalElems[i].evaluateOPTIM(targetValues[i]);
+      } else {
+        // The constraints (CONS type)
+        // If evalRes[0] equals to 0, then be a feasible point, i.e. satisfies
+        // all the constraints
+        evalRes[0] += evalElems[i].evaluateCONS(targetValues[i]);
+      }
+    }
+  }
+}