xcsmacros.h

Simple GA code (Pascal code from Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization,

/*
/       (XCS-C 1.2)
/	------------------------------------
/	Learning Classifier System based on accuracy
/
/     by 
/     Martin V. Butz
/     Illinois Genetic Algorithms Laboratory (IlliGAL)
/     University of Illinois at Urbana/Champaign
/     [e-mail]
/
/     Last modified: 09-30-2003
/
/     The main header with all macro values which can be considered as default values.
/     Parameter settings can be changed here however it is encouraged to read in a separate macro file.
*/

#define TAB_OUT_FILE "XCS_TEST" /* the output file for the results */

#define NR_EXPS 1 /* the number of experiments to take */
#define MAX_NR_STEPS 20000 /* the number of problems to solve in one experiment */
#define TEST_FREQUENCY 200 /* the frequency knowledge tests are executed */

#define MAX_POP_SIZE 2000 /* default=2000 (highly problem dependent); the maximum size of the population */

#define INITIALIZE_POPULATION 0 /* default=0; Boolean to check if population should be initialized */

#define ALPHA 0.1  /* default=0.1; decreases the accuracy value if classifier's prediction error is */
		   /* higher than EPSILON_0*PAYMENT_RANGE.
		    * This constant results in a gap between accurate and not accurate classifiers */
#define BETA 0.2   /* default=0.2; Learning Rate parameter */
#define GAMMA 0.95 /* default=0.95; Discounting factor that discounts the maximum of the prediction array */
#define EPSILON_0 0.01 /* default=0.01; TOLERATED ERROR in prediction error*/

#define NU 5. /* default=5; exponent of the power function */

#define THETA_GA 25 /* default=25; run GA if the average of the last GA application in the set is greater than THETA_GA */
#define FITNESS_REDUCTION 1 /* default=1; reduce the fitness of the offspring classifier */
#define TOURNAMENT_SIZE 0.4 /* default=0.4; percentage of action set that takes part in tournament; if set to zero, proportionate selection is used; if set greater than one, tournament with fixed size is used */
#define FORCE_DIFFERENT_IN_TOURNAMENT 0 /* default =0; the probability that the two participants must be different */
#define SELECT_TOLERANCE 0.001 /* default=.001; the tolerance with which classifier fitness/error is considered similar */
#define CROSSOVER_TYPE 0 /* default=0; 0=uniform, 1=one-point, 2=two-point */
#define CHI_GA 1.0 /* default=1.0; the probability to do crossover */
#define MU_GA 0.01 /* default=0.01 (problem dependent); probability of mutating one bit */
#define DO_GENERALIZATION_MUTATION 0 /* default=0; flag for only generalization in mutation */
#define DO_NICHE_MUTATION 0 /* default=0; flag for applying niche mutation instead of a general mutation */

#define DO_MAM 1 /* default=1; determines if Moyenne Adaptive Modifee should be applied */
#define DO_GA_ERROR_BASED_SELECT 0 /* default=0; GA selection based on error */

#define DELTA 0.1 /* default=0.1; used to enforce the deletion of low fitness classifiers (= second deletion method) once they reached DELETION_EXPERIENCE */
#define THETA_DEL 20 /* default=20; The experience required to use second deletion method */

#define DELETION_TYPE 1 /* default=1; 0=random deletion, 1=fitness and |[A]| bias, 2=fitness bias only, 3=|[A]| bias only ; 4=error bias only; 5=error and |[A]| bias */

#define DONT_CARE_PROB 0.66 /* default=0.66 (problem dependent); Probability of choosing DONT_CARE in a newly generated classifier */

#define DO_GA_SUBSUMPTION 1 /* default=1; flag for applying GA subsumption */
#define DO_ACTION_SET_SUBSUMPTION 0 /* default=0; flag for applying action set subsumption */
#define THETA_SUB 20 /* default=20; the experience required before can be subsumed */

#define EXPLORE_PROB 1.0 /* default=1; The probability of choosing a random action while learning */
#define TELETRANSPORTATION 50 /* default=50; possible teletransportation in multi step environments (trial ends if counter exeeds this number) */

/* The following parameters cannot be altered with the parameter file */

#define PRE_INI 10.0 /* default=10; prediction initialization in new classifiers */
#define PREER_INI 0.0 /* default=0; prediction error initialization in new classifiers */
#define FIT_INI 0.01 /* default=0.01; fitness initialization in new classifiers */

#define DO_FITNESSUPDATE_FIRST 0 /* default=0; flag detemines when the fitness is updated */
#define UPDATEORDER_PRED_PREDERROR 0 /* default=0; determines if the prediction should be updated before the prediction error */

#define DONT_CARE '#' /* the don't care symbol */

#define _M 2147483647     /* modulus of PMMLCG (default=2147483647=2^31 - 1)*/
#define _A 16807          /* default=16807*/

double urand();
double nrand();

long setSeed(long newSeed);
void randomize(void);

int getIntValue(char *string);
double getDoubleValue(char *string);