遗传算法c源代码一.doc

遗传算法c源代码破菜筐 2006-03-22 21:45 发表/*-* | | | Robotics 95 - Final Course Project | | | | By : Ziv Pollack & Omri Weisman | | | | NNUGA - Neural Network Using Genetic Algorithms | | | *-*/ /* * File name : nnuga.c * * This program is an implementation of a Neural Network (NN) which learns u sing * Genetic Algorithms (GA). It runs from a Tk shell. * * It reads points from a file and creates an output file which describes * points that correspond to lines that seperate the plain into several * regions, regions where the NNs output will be true, and a regions * where the NNs output will be false. * */ #include #include #include /* NN related */ #define NUM 2 /* Number of input nodes */ #define LIMIT 150 /* Maximum number of inputs the system can handle */ #define SESSIONS 500 /* Number of training sessions that well put the syst em through */ /* GA related */ #define POPS 10 /* Number of populations */ #define SIZE 25 /* Size of vector in the genetic algorithms */ #define MAXPOP 60 /* Size of population */ #define BESTPOP 4 /* Number of individuals taken from the best */ #define SELPOP 8 /* SELPOP-BESTPOP = Number of people selected randomly on each gen. */ #define NEWPOP 18 /* NEWPOP-SELPOP = Number of new people, created rando mly on each gen. */ #define MUT1 25 /* MUT1-NEWPOP = Number of mutations in the first muta tion group */ #define MIXGEN 10 /* Number of generations between population mixing */ typedef struct float pNUM; vector; /* NN related */ vector testLIMIT, w1, w2, w3, w4, w5, w6; int hitsLIMIT, total; float w76; int b1, b2, b3, b4, b5, b6, b7; /* GA related */ float popPOPSMAXPOPSIZE; int scorePOPSMAXPOP; /*-* | | | Randomize | | | *-*/ randomize() struct timeval tp; struct timezone tzp; /* Use time of day to feed the random number generator seed */ gettimeofday( &tp, &tzp); srandom( tp.tv_sec ); /*-* | | | irand( range ) - return a random integer in the range 0.(range-1) | | | *-*/ int irand( range ) int range; return( random() % range ); /*-* | | | scalar_mult - multiply two vectors | | | *-*/ float scalar_mult( x, y ) vector x, y; int i; float s = 0.0; for ( i = 0 ; i 0; /* hardlim transfer function */ int a4 = ( scalar_mult( w4, x ) + b4 ) 0; /* hardlim transfer function */ float a5 = scalar_mult( w5, x ) + b5 ; /* linear transfer function */ float a6 = scalar_mult( w6, x ) + b6 ; /* linear transfer function */ /* Second layer */ float a7 = ( a1*w70 + a2*w71 + a3*w72 + a4*w73 + a5*w74 + a6*w75 + b7 ) 0.0; /* hardlim transfer function */ return(a7); /*-* | | | pop_swap( p, a, b ) - swap two vectors and scores in the population p| | | *-*/ pop_swap( p, a, b ) int p, a, b; int t, i; /* Swap vector */ for ( i = 0 ; i SIZE ; i+ ) t = poppa; poppa = popp; popp = t; /* Swap score */ t = scorepa; scorepa = scorep; scorep = t; /*-* | | | apply( p, i ) - apply the i vector of the population p on the NN | | | *-*/ apply( p, i ) int p, i; /* Get the weights and biases of the neurons from the GA vector */ w1.p0 = popp0; w1.p1 = popp1; b1 = popp2; w2.p0 = popp3; w2.p1 = popp4; b2 = popp5; w3.p0 = popp6; w3.p1 = popp7; b3 = popp8; w4.p0 = popp9; w4.p1 = popp10; b4 = popp11; w5.p0 = popp12; w5.p1 = popp13; b5 = popp14; w6.p0 = popp15; w6.p1 = popp16; b6 = popp17; w70 = popp18; w71 = popp19; w72 = popp20; w73 = popp21; w74 = popp22; w75 = popp23; b7 = popp24; /*-* | | | pop_copy( p1, a, p2, b ) - copy the vector b in the population p2 into | | the vector a in the population p1. | | | *-*/ pop_copy( p1, a, p2, b) int p1, a, p2, b; int i; for ( i = 0 ; i SIZE ; i+ ) popp1a = popp2; /*-* | | | Initialize the populations | | | *-*/ make_initial_population() int p, i, j; for ( p = 0 ; p POPS ; p+ ) /* Half population gets values from -1 to 1 */ for ( i = 0 ; i (MAXPOP/2) ; i+ ) for ( j = 0 ; j SIZE ; j+ ) poppj = (random()&1048575) / 1000000.0 - 0.5) * 2; /* Half population gets values from -100 to 100 */ for ( i = (MAXPOP/2) ; i MAXPOP ; i+ ) for ( j = 0 ; j SIZE ; j+ ) poppj = (random()&1048575) / 10000.0 - 50) * 2; /*-* | | | Calculate the scores of all the vectors in all the populations | | | *-*/ calc_score() int p, i; for ( p = 0 ; p POPS ; p+ ) for ( i = 0 ; i MAXPOP ; i+ ) apply( p, i ); scorep = check_performance(); /*-* | | | Sort the populations | | | *-*/ sort_population() int p, i, j, k, best; /* Use insert sort */ for ( p = 0 ; p POPS ; p+ ) for ( i = 0 ; i (MAXPOP-1) ; i+ ) best = scorep; for ( j = (i+1) ; j best ) best = scorepj; k = j; if ( best scorep ) pop_swap( p, i, k ); /*-* | | | Show (on the standard output) the best scores of all populations | | | *-*/ statistics( generation ) int generation; int p; if ( generation % MIXGEN = 0 ) printf(-n); printf( %4d) First are: , generation); for ( p = 0 ; p POPS ; p+ ) printf(%3d , scorep0 ); printf( (from %d)n,total); /*-* | | | Generate the next generation in all populations | | | *-*/ make_next_generation( generation ) int generation; int p, i, j, k1, k2, m; float dev; for ( p = 0 ; p POPS ; p+ ) /* keep best - BESTPOP */ /* add another group, randomly - (SELPOP-BESTPOP) */ for ( i = BESTPOP ; i SELPOP ; i+ ) pop_swap( p, i, (irand( MAXPOP - i ) + i) ); /* create new individuals */ for ( i = SELPOP ; i NEWPOP ; i+ ) for ( j = 0 ; j SIZE ; j+ ) poppj = (random()&1048575) / 100000.0 - 5) * 2; /* SELPOP to MUT1 will be severe mutations */ for ( i = NEWPOP ; i MUT1 ; i+ ) pop_copy( p, i, p, irand(NEWPOP) ); dev = 1 + (irand(2000) - 1000 )/ 5000); poppirand(SIZE) *= dev; dev = 1 + (irand(2000) - 1000 )/ 5000); poppirand(SIZE) *= dev; /* MUT2 to MAXPOP will be crossovers */ for ( i = MUT1 ; i MAXPOP ; i+ ) /* Every several generations (set by MIXGEN) there is a cross-over