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王小平版遗传算法的光盘源代码 SGPC: Simple Genetic Programming in C by Walter Alden Tackett and Aviram Carmi (gpc@ipld01.hac.com) Version 1.1 (c) 1993 by Walter Alden Tackett and Aviram Carmi This code and documentation is copyrighted and is not in the public domain. All rights reserved. Genetic Programming is a method of "Adaptive Automatic Program Induction" originally created by John Koza and James Rice of Stanford University. SGPC is a C implementation of Genetic Programming: it is a C program which writes LISP programs. These programs are tailored by the system to solve a problem specified by the user. Koza and Rice have provided to the public a version of Genetic Programming which is written in LISP. SGPC offers greater portability and about 25-50 times improvement in execution speed due to a highly optimized C implementation. For further information on Genetic Programming See: _Genetic_Programming_ by John R. Koza, MIT Press 1992 "Genetic Programming for Feature Discovery and Image Discrimination" by Walter Alden Tackett in _Genetic_Algorithms:_Proceedings_of_the_ Fifth_International_Conference_ (GA93), S. Forrest Ed., Morgan- Kaufman 1993 To participate in our on-line Internet e-mail forum send your subscription request to: genetic-programming-request@cs.stanford.edu Basically, the code does the same things that Koza & Rice s simple LISP does and is set up to handle multiple populations as well (e.g., for co-evolution). You need to provide three modules, setup.c, fitness.c, and prob.h, in a subdirectory named PROBNAME, where PROBNAME is some descriptive name of the problem. E.G., in the version we ship we include REGRESSION/setup.c and REGRESSION/fitness.c, Which do Koza s simple regression problem. setup.c contains functions to setup the function table, the terminals table, and code for the functions in the function table. prob.h contains prototypes for the user defined functions. fitness.c contains functions to evaluate and validate populations and trees, early termination, and definition of the fitness (training and test) cases. As a second example, for your enlightenment, we include the ADF problem which shows you how to build a simple 2-class "dendritic" classifier (see my paper in the sfi account). You should not need to modify any of the other myriad files. COMPILING Source code for the kernel system is in the sub-directory `lib . Source code for problem depended modules are in sub-directories named after the problem. There is a Makefile in the top level directory which invokes the Makefile in the problem specific directory, which invokes the Makefile in the `lib directory (if needed). The source in the lib directory is compiled into an object library file, which is then linked with the problem specific objects. Read the notes in the Makefiles in all the sub-directories, make any necessary changes to fit your system, and then type: make depend PROBLEM=PROBNAME make PROBLEM=PROBNAME where PROBNAME is the name of the subdirectory where the problem specific setup.c and fitness.c files are. EXECUTING the executable is called gpcPROBNAME and will be in the PROBNAME directory. gpcPROBNAME [-d nrow ncol] npops ngen (pop0_pfile|`none )... [seed] NOTE: you must have a parameters file name for each population. EXAMPLE gpcREGRESSION 2 100 none pop1_file 1234 will run gpcREGRESSION with 2 pops 100 gens with pop0 using the default values, pop1 using values from pop1_file, and 12345 as the initial random seed. On startup default values for the runtime parameters are assigned from hard-coded values, then if a file called "default.in" exists in the current directory, values are read from it and override the hard-coded values. If a parameters file name for a population is present on the command line, values from this file override the previous values for that population, else if `none is specified on the command line, the parameters will retain the default values. Remember you must specify either a population params file or `none on the command line for each population. Note: since the random seed value is not a population parameter but it can be specified in the population parameters file, if there are multiple populations, and no seed value was specified on the command line, the value read from the last population parameters file will be used. Some salient points about the modules which are provided for you: The main() is located in lib/gpc.c The top-level loop which iterates over the generations is in generations.c The data structures which you must work with are in the file gpc.h Expressions (trees) are evaluated via calls to eval(tree *t) in eval.c. Trees are operated on using routines in treeops.c and can be read/written using operations in treeio.c. You will get some tutorial feel for using the data structures involved by examining the initializations which take place in populations.c and generations.c. In general, the modules are descriptively named: e.g., selection.c, crossover.c, mutation.c. CREATING YOUR OWN PROBLEMS First create a sub-directory under the top-level sgpc directory and name it a descriptive name. Copy the Makefile and optionally the fitness.c setup.c and prob.h files to the new directory. FITNESS.C Here are the functions you must include in the fitness.c module: /* assigns standardized fitness values to all members of each population pop[0]...pop[numpops-1] */ void evaluate_fitness_of_populations ( int numpops, /* number of populations */ int numgens, /* number of generations */ pop_struct *pop /* the population array */ ); /* returning != 0 from this routine halts SGPC at the end of the generation, eg, if a "perfect" (ha!) individual is found */ int terminate_early ( int numpops, int numgens, pop_struct *pop ); /* creates the array of fitness cases, or does other startup peculiar to your problem */ void define_fitness_cases ( int numpops, int numgens, pop_struct *pop ); /* this routine is used to test the best-of-generation individual against a separate set of test cases which were not used in training (i.e., in generating the fitness values which drive selection). The best-of-run individual is determined using the figure returned from this function */ float validate_fitness_of_tree( int numpops, int numgens, pop_struct *pop, tree *t ) SETUP.C Here are the functions you must include in the setup.c module: /* assigns function pointers, print names, arity, macro-flag, etc, to function table entries for all populations */ void make_function_table ( pop_struct *pop ); /* assigns function pointers, print names, arity, macro-flag, etc, to terminal table entries for all populations */ void make_terminal_table ( pop_struct *pop ); In addition, you declare all of the actual function code for you function set in setup.c. A declared function can either be a function or a macro: a function has an array of values (eg float *args;) passed into it as arguments, whereas a macro has an array of unevaluated expressions passed in, so that some of them may remain unevaluated to prevent side-effects. Here is how the properties of a function are assigned in make_function_table(pop_struct *pop): pop[0].function_table[0].arity = 2; pop[0].function_table[0].macro = FALSE; pop[0].function_table[0].enabled = TRUE; pop[0].function_table[0].printname = "+"; pop[0].function_table[0].code = plus; Here is the assignment of a macro: pop[0].function_table[4].arity = 4; pop[0].function_table[4].macro = TRUE; pop[0].function_table[4].enabled = TRUE; pop[0].function_table[4].printname = "IFLTE"; pop[0].function_table[4].code = iflte; Here is how the functions they point to are declared: For the function plus: GENERIC plus(GENERIC *args) { return args[0]+args[1]; } For the macro iflte: /* note that args are unevaluated trees, not GENERIC values */ GENERIC iflte(tree **args) { return ((eval(args[0]) < eval(args[1])) ? eval(args[2]) : eval(args[3])); } Note that in the code above, the type generic can be set by the TYPE definition set to the make file: default is #define GENERIC float. TERMINAL TABLE: We play some games here. Here is the definition of a terminal from make_terminal_set(pop_struct *pop): pop[0].terminal_table[0].val = 0; pop[0].terminal_table[0].printname = "X"; pop[0].terminal_table[0].constant_generator = random_constant; If we declare that there are N terminals in the terminal set, then there a total of N+1 entries 0...N, where entry N is the randomly generated constant, which should always be declared as follows: pop[0].terminal_table[pop[0].terminal_table_size].val = 0; pop[0].terminal_table[pop[0].terminal_table_size].printname = FORMAT; pop[0].terminal_table[pop[0].terminal_table_size].constant_generator = random_constant; Here, FORMAT is #defined to be the format string associated with the GENERIC type, eg if GENERIC == float then FORMAT == "%f". SAMPLE POPULATION PARAMETERS FILE: seed = 11287 # use this if no value for seed on command line population_size = 100 max_depth_for_new_trees = 6 max_depth_after_crossover = 17 max_mutant_depth = 4 grow_method = RAMPED # FULL GROW selection_method = FITNESSPROP # TOURNAMENT tournament_K = 6 # used only if TOURNAMENT is selected crossover_func_pt_fraction = 0.2 crossover_any_pt_fraction = 0.2 fitness_prop_repro_fraction = 0.1 parsimony_factor = 0.00000 CHECKPOINTING: To enable checkpointing, a non zero value for checkpoint_frequency must be specified in the default parameters file, e.g., checkpoint_frequency = 10 Remember that if you specify value in the population s parameters file, the value read from the last parameters file will be used as the checkpoint frequency for all the populations. The generated ckptfile is named gpc_`hostid _`pid .ckpt.Z, i.e. it is compressed using the compression utility specified in the makefile (Note: ignore ckpt files that are not compressed, the process could have crashed while writing the uncompressed file and it is probably corrupted. Of course, this is assuming that you have `compress on your system.) To recover from a crash, just uncompress the ckpt file and type in: gpc -r ckpt_file_name As a bonus you can now extend a run for more generations (only if checkpointing is enabled, i.e. checkpoint_frequency is greater than zero. Set it to a large value if you do not want checkpointing to slow you down, but do want a checkpoint file for the last generation). To do this, just edit the ckpt file and change the value ONLY for the following line (it should be the fourth line in the file): number_of_generations = 100 ^^^ to the new total number of generations, i.e. to extend the run by 10 gens replace 100 by 110. **************************************************************************** FAQ S FROM OUR EMAIL ARCHIVES: > First, do you use mutation? I couldnt find a mutation parameter that easily accessible... Yes. It is implicit, like in Koza & Rice s code: it is 1.0 - (crossover_func_pt_fraction + crossover_any_pt_fraction + fitness_prop_repro_fraction) > > Second, do you have a facilty for inserting pre-created individuals into the > population? Yes- the load_from_file keyword in the parameter file, e.g.: load_from_file = foo.lsp ...in your input parameter file will load the N lisp expressions in file foo.lsp as the first N elements of the population. Also, *don t* include the load_from_file keyword unless you are actually loading a file. > Thanks for the info...I was also curious how I could prohibit the > random constants from being terminals...ie I do not wish/need to use > random constants and I was wondering how best to remove them...The solution > is somewhat obvious maybe (dont include them in the terminal table) but then > I was a bit concerned given all of the warnings that there are N+1 > terminals.. > Yet another undocumented feature (YAUF :), in lib/gpc.h change: #define ALLOW_CONST 1 to: #define ALLOW_CONST 0 You will need to recompile the entire libgpc object library. i.e., you cannot have this set to different values for different problems w/o recompiling the whole damn thing. SGPC 1.0 is shipped with three makefiles: Makefile - sun-specific makefile: slick, cryptic, non-portable. gMakefile - gnu makefile: you can find gnu make for most machines. Ask your local systems hacker to help you find it on the net and install it. make.script - a shell script for compiling SGPC, may require some editing if you have changed directory names or the PROBLEM you are working on. Basically, if you can t run this you re not running Unix. The c-code itself should be portable to standard Unix systems. The only portability issues should surround the use of the system qsort routine (we provide one ourselves, anyway) the timing routines (you don t need this unless you re anal, anyway) and mallopt (you don t *have* to use mallopt, but it helps to ensure locality and reduce fragmentation). Of course this is only an estimate and your actual mileage may vary.
