AI-CODE.rar_ai

#include<iostream.h> using namespace std; #define DUP 0x00 #define SWAP 0x01 #define MUL 0x02 #define ADD 0x03 #define OVER 0x04 #define NOP 0x05 #define MAX_INSTRUCTION (NOP+1) #define NONE 0 #define STACK_VIOLATION 1 #define MATH_VIOLATION 2 #define STACK_DEPTH 25 int stack[STACK_DEPTH]; int stackPointer; #define ASSERT_STACK_ELEMENTS(x) \ if (stackPointer < x) { error = STACK_VIOLATION ; break; } #define ASSERT_STACK_NOT_FULL \ if (stackPointer == STACK_DEPTH) { error = STACK_VIOLATION ;break; } #define SPUSH(x) (stack[stackPointer++] = x) #define SPOP (stack[--stackPointer]) #define SPEEK (stack[stackPointer-1]) /* * interpretSTM * * program - sequence of simple instructions * progLength - length of program * args - arguments on the virtual stack * argsLength - number of arguments on the virtual stack * */ int interpretSTM(const int *program, int progLength, const int *args, int argsLength) { int pc = 0; int i, error = NONE; int a, b; stackPointer = 0; /* Load the arguments onto the stack */ for (i = argsLength-1 ; i >= 0 ; i--) { SPUSH(args[i]); } /* Execute the program */ while ((error == NONE) && (pc < progLength)) { switch(program[pc++]) { case DUP: ASSERT_STACK_ELEMENTS(1); ASSERT_STACK_NOT_FULL; SPUSH(SPEEK); break; case SWAP: ASSERT_STACK_ELEMENTS(2); a = stack[stackPointer-1]; stack[stackPointer-1] = stack[stackPointer-2]; stack[stackPointer-2] = a; break; case MUL: ASSERT_STACK_ELEMENTS(2); a = SPOP; b = SPOP; SPUSH(a * b); break; case ADD: ASSERT_STACK_ELEMENTS(2); a = SPOP; b = SPOP; SPUSH(a + b); break; case OVER: ASSERT_STACK_ELEMENTS(2); SPUSH(stack[stackPointer-2]); break; } /* Switch opcode */ } /* Loop */ return(error); } typedef struct population { float fitness; int MAX_PROGRAM; int progSize; int program[MAX_PROGRAM]; } POPULATION_TYPE; POPULATION_TYPE populations[2][MAX_CHROMS]; int curPop; void initMember(pop,index ) { int progIndex; populations[pop][index].fitness = 0.0; populations[pop][index].progSize = MAX_PROGRAM-1; /* Randomly create a new program */ progIndex = 0; while (progIndex < MAX_PROGRAM) { populations[pop][index].program[progIndex++] = getRand(MAX_INSTRUCTION); } } void initPopulation( void ) { int index; /* Initialize each member of the population */ for (index = 0 ; index < MAX_CHROMS ; index++) { initMember(curPop, index); } } float maxFitness; float avgFitness; float minFitness; extern int stackPointer; extern int stack[]; static int x = 0; float totFitness; int performFitnessCheck( FILE *outP ) { int chrom, result, i; int args[10], answer; maxFitness = 0.0; avgFitness = 0.0; minFitness = 1000.0; for ( chrom = 0 ; chrom < MAX_CHROMS ; chrom++ ) { populations[curPop][chrom].fitness = 0.0; for ( i = 0 ; i < COUNT ; i++ ) { args[0] = (rand() & 0x1f) + 1; args[1] = (rand() & 0x1f) + 1; args[2] = (rand() & 0x1f) + 1; /* Sample Problem: x^3 + y^2 + z */ answer = (args[0] * args[0] * args[0]) + (args[1] * args[1]) + args[2]; /* Call the virtual stack machine to check the program */ result = interpretSTM(populations[curPop][chrom].program, populations[curPop][chrom].progSize, args, 3); /* If no error occurred, add this to the fitness value */ if (result == NONE) { populations[curPop][chrom].fitness += TIER1; } /* If only one element is on the stack, add this to the fitness * value. */ if (stackPointer == 1) { populations[curPop][chrom].fitness += TIER2; } /* If the stack contains the correct answer, add this to the * fitness value. */ if (stack[0] == answer) { populations[curPop][chrom].fitness += TIER3; } } /* If this chromosome exceeds our last highest fitness, update * the statistics for this new record. */ if (populations[curPop][chrom].fitness > maxFitness) { maxFitness = populations[curPop][chrom].fitness; } else if (populations[curPop][chrom].fitness < minFitness) { minFitness = populations[curPop][chrom].fitness; } /* Update the total fitness value (sum of all fitnesses) */ totFitness += populations[curPop][chrom].fitness; } /* Calculate our average fitness */ avgFitness = totFitness / (float)MAX_CHROMS;' if (outP) { /* Emit statistics if we have an output file pointer */ fprintf(outP, "%d %6.4f %6.4f %6.4f\n", x++, minFitness, avgFitness, maxFitness); } return 0; } int performSelection( void ) { int par1, par2; int child1, child2; int chrom; /* Walk through the chromosomes, two at a time */ for (chrom = 0 ; chrom < MAX_CHROMS ; chrom+=2) { /* Select two parents, randomly */ par1 = selectParent(); par2 = selectParent(); /* The children are loaded at the current index points */ child1 = chrom; child2 = chrom+1; /* Recombine the parents to the children */ performReproduction( par1, par2, child1, child2 );] } return 0; } int selectParent( void ) { static int chrom = 0; int ret = -1; float retFitness = 0.0; /* Roulette-wheel selection process */ do { /* Select the target fitness value */ retFitness = (populations[curPop][chrom].fitness / maxFitness); if (chrom == MAX_CHROMS) chrom = 0; /* If we've walked through the population and have reached our * target fitness value, select this member. */ if (populations[curPop][chrom].fitness > minFitness) { if (getSRand() < retFitness) { ret = chrom++; retFitness = populations[curPop][chrom].fitness; break; } } chrom++; } while (1); return ret; } int performReproduction( int parentA, int parentB, int childA, int childB ) { int crossPoint, i; int nextPop = (curPop == 0) ? 1 : 0; int mutate( int ); /* If we meet the crossover probability, perform crossover of the * two parents by selecting the crossover point. */ if (getSRand() > XPROB) { crossPoint = getRand(MAX(populations[curPop][parentA].progSize-2, populations[curPop][parentB].progSize-2))+1; curCrossovers++; } else { crossPoint = MAX_PROGRAM; } /* Perform the actual crossover, in addition to random mutation */ for (i = 0 ; i < crossPoint ; i++) { populations[nextPop][childA].program[i] = mutate(populations[curPop][parentA].program[i]); populations[nextPop][childB].program[i] = mutate(populations[curPop][parentB].program[i]); } for ( ; i < MAX_PROGRAM ; i++) { populations[nextPop][childA].program[i] = mutate(populations[curPop][parentB].program[i]); populations[nextPop][childB].program[i] = mutate(populations[curPop][parentA].program[i]); } /* Update the program sizes for the children (based upon the * parents). */ populations[nextPop][childA].progSize = populations[curPop][parentA] progSize; populations[nextPop][childB].progSize = populations[curPop][parentB].progSize; return 0; } int mutate(int gene) { float temp = getSRand(); /* If we've met the mutation probability, randomly muta