initializega.cpp

matlab的GA工具箱与vc混和实现遗传算法的源码

  #include "matlib.h"
  #pragma hdrstop
  
  #include "initializega.h"
  #include "calcbits.h"
  
  
  
  Mm initializega(Mm num, Mm bounds, Mm evalFN, Mm evalOps, Mm options) {
    begin_scope
    num.setname("num"); bounds.setname("bounds"); evalFN.setname("evalFN"); evalOps.setname("evalOps"); options.setname( \
      "options"); 
    dMm(pop); dMm(estr); dMm(numVars); dMm(rng); dMm(xZomeLength); dMm(bits); dMm(i_); dMm(i_initializega_v0); 
    
    call_stack_begin;
    // nargin, nargout entry code
    double old_nargin=nargin_val; if (!nargin_set) nargin_val=5.0;
    nargin_set=0;
    double old_nargout=nargout_val; if (!nargout_set) nargout_val=1.0;
    nargout_set=0;
    
    // translated code
    
    // function [pop]=initializega(populationSize, variableBounds,evalFN,
    //                           evalOps,options)
    //    initializega creates a matrix of random numbers with 
    //    a number of rows equal to the populationSize and a number
    //    columns equal to the number of rows in bounds plus 1 for
    //    the f(x) value which is found by applying the evalFN.
    //    This is used by the ga to create the population if it
    //    is not supplied.
    //
    // pop            - the initial, evaluated, random population 
    // populatoinSize - the size of the population, i.e. the number to create
    // variableBounds - a matrix which contains the bounds of each variable, i.e.
    //                  [var1_high var1_low; var2_high var2_low; ....]
    // evalFN         - the evaluation fn, usually the name of the .m file for 
    //                  evaluation
    // evalOps        - any options to be passed to the eval function defaults []
    // options        - options to the initialize function, ie. 
    //                  [type prec] where eps is the epsilon value 
    //                  and the second option is 1 for float and 0 for binary, 
    //                  prec is the precision of the variables defaults [1e-6 1]
    
    // Binary and Real-Valued Simulation Evolution for Matlab GAOT V2 
    // Copyright (C) 1998 C.R. Houck, J.A. Joines, M.G. Kay 
    //
    // C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function
    // optimization: A Matlab implementation. ACM Transactions on Mathmatical
    // Software, Submitted 1996.
    //
    // This program is free software; you can redistribute it and/or modify
    // it under the terms of the GNU General Public License as published by
    // the Free Software Foundation; either version 1, or (at your option)
    // any later version.
    //
    // This program 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 General Public License for more details. A copy of the GNU 
    // General Public License can be obtained from the 
    // Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
    
    if (istrue(nargin()<5.0)) {
      options = (BR(1e-06),1.0);
    }
    if (istrue(nargin()<4.0)) {
      evalOps = nop_M;
    }
    
    if (istrue(any(evalFN<48.0))) {
      //Not a .m file
      if (istrue(options(2.0)==1.0)) {
        //Float GA
        estr = (BR(TM("x=pop(i,1); pop(i,xZomeLength)=")),evalFN,TM(";"));
        
      } else {
        //Binary GA
        estr = (BR(TM("x=b2f(pop(i,:),bounds,bits); pop(i,xZomeLength)=")),evalFN,TM(";"));
        
      }
    } else {
      //A .m file
      if (istrue(options(2.0)==1.0)) {
        //Float GA
        estr = (BR(TM("[ pop(i,:) pop(i,xZomeLength)]=")),evalFN,TM("(pop(i,:),[0 evalOps]);"));
        
      } else {
        //Binary GA
        estr = (BR(TM("x=b2f(pop(i,:),bounds,bits);[x v]=")),evalFN,TM("(x,[0 evalOps]); pop(i,:)=[f2b(x,bounds,bits) v];") \
          );
        
      }
    }
    
    
    numVars = size(bounds,1.0);
    //Number of variables
    rng = ctranspose((bounds(c_p,2.0)-bounds(c_p,1.0)));
    //The variable ranges'
    
    if (istrue(options(2.0)==1.0)) {
      //Float GA
      xZomeLength = numVars+1.0;
      //Length of string is numVar + fit
      pop = zeros(num,xZomeLength);
      //Allocate the new population
      pop(c_p,colon(1.0,1.0,numVars)) = times((ones(num,1.0)*rng),(rand(num,numVars)))+(ones(num,1.0)*ctranspose( \
        bounds(c_p,1.0)));
    } else {
      //Binary GA
      bits = calcbits(bounds,options(1.0));
      xZomeLength = sum(bits)+1.0;
      //Length of string is numVar + fit
      pop = round(rand(num,sum(bits)+1.0));
    }
    
    i_initializega_v0 = colon(1.0,1.0,num); int i_initializega_i0;
    for (i_initializega_i0=0;i_initializega_i0<i_initializega_v0.cols();i_initializega_i0++) {
      forelem(i_,i_initializega_v0,i_initializega_i0);
      eval(estr);
    }
    
    call_stack_end;
    
    // nargin, nargout exit code
    nargin_val=old_nargin; nargout_val=old_nargout;
    
    // function exit code
    num.setname(NULL); bounds.setname(NULL); evalFN.setname(NULL); evalOps.setname(NULL); options.setname(NULL);  \
      
    return pop;
    end_scope
  }
  
  
  Mm initializega() {
    begin_scope
    double old_nargin=nargin_val; nargin_val=0.0; nargin_set=1;
    dMm(num); dMm(bounds); dMm(evalFN); dMm(evalOps); dMm(options); 
    dMm(ret1a);
    ret1a=initializega(num, bounds, evalFN, evalOps, options);
    nargin_val=old_nargin;
    return ret1a;
    end_scope
  }
  
  Mm initializega(Mm num) {
    begin_scope
    double old_nargin=nargin_val; nargin_val=1.0; nargin_set=1;
    dMm(bounds); dMm(evalFN); dMm(evalOps); dMm(options); 
    dMm(ret1a);
    ret1a=initializega(num, bounds, evalFN, evalOps, options);
    nargin_val=old_nargin;
    return ret1a;
    end_scope
  }
  
  Mm initializega(Mm num, Mm bounds) {
    begin_scope
    double old_nargin=nargin_val; nargin_val=2.0; nargin_set=1;
    dMm(evalFN); dMm(evalOps); dMm(options); 
    dMm(ret1a);
    ret1a=initializega(num, bounds, evalFN, evalOps, options);
    nargin_val=old_nargin;
    return ret1a;
    end_scope
  }
  
  Mm initializega(Mm num, Mm bounds, Mm evalFN) {
    begin_scope
    double old_nargin=nargin_val; nargin_val=3.0; nargin_set=1;
    dMm(evalOps); dMm(options); 
    dMm(ret1a);
    ret1a=initializega(num, bounds, evalFN, evalOps, options);
    nargin_val=old_nargin;
    return ret1a;
    end_scope
  }
  
  Mm initializega(Mm num, Mm bounds, Mm evalFN, Mm evalOps) {
    begin_scope
    double old_nargin=nargin_val; nargin_val=4.0; nargin_set=1;
    dMm(options); 
    dMm(ret1a);
    ret1a=initializega(num, bounds, evalFN, evalOps, options);
    nargin_val=old_nargin;
    return ret1a;
    end_scope
  }