nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/encode/EvalStruct.java

   1 /**
   2  * Description: provide the information for evaluating a set of targets values
   3  * into encoded information (For formation the goodness landscape by comparing)
   4  *
   5  * @ Author        Create/Modi     Note
   6  * Xiaofeng Xie    Mar 1, 2003
   7  * Xiaofeng Xie    May 11, 2004
   8  *
   9  * This library is free software; you can redistribute it and/or
  10  * modify it under the terms of the GNU Lesser General Public
  11  * License as published by the Free Software Foundation; either
  12  * version 2.1 of the License, or (at your option) any later version.
  13  *
  14  * This library is distributed in the hope that it will be useful,
  15  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  16  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  17  * Lesser General Public License for more details.
  18  *
  19  * Please acknowledge the author(s) if you use this code in any way.
  20  *
  21  * @References:
  22  * [1] Deb K. An efficient constraint handling method for genetic algorithms.
  23  * Computer Methods in Applied Mechanics and Engineering, 2000, 186(2-4): 311-338
  24  */
  25
  26 package net.adaptivebox.encode;
  27
  28 public class EvalStruct {
  29   // The information for evaluating all the responses
  30   public EvalElement[] evalElems = null;
  31
  32   public EvalStruct(int elemsNum) {
  33     evalElems = new EvalElement[elemsNum];
  34   }
  35   public int getSize() {
  36     return evalElems.length;
  37   }
  38
  39   public void setElemAt(EvalElement dim, int index) {
  40     evalElems[index] = dim;
  41   }
  42
  43   //convert response values into encoded information double[2]
  44   public void evaluate(double[] evalRes, double[] targetValues) {
  45     evalRes[0] = evalRes[1] = 0;
  46     for(int i=0; i<evalElems.length; i++) {
  47       if (evalElems[i].isOptType()) {
  48         //The objectives (OPTIM type)
  49         //The multi-objective will be translated into single-objective
  50         evalRes[1] += evalElems[i].evaluateOPTIM(targetValues[i]);
  51       } else {
  52         //The constraints (CONS type)
  53         //If evalRes[0] equals to 0, then be a feasible point, i.e. satisfies
  54         // all the constraints
  55         evalRes[0] += evalElems[i].evaluateCONS(targetValues[i]);
  56       }
  57     }
  58   }
  59 }
  60