The Push programming language and the PushGP genetic programming system implemented in Clojure.
|src||Fixed problems with latest simplification changes.|
|test||Moved stress-test to test/stress_test.clj|
|.gitattributes||Changed .gitattributes to make line endings correct for everyone (CRL…|
|.gitignore||Refactors breed function to improve readability and clutter.|
|LICENSE||Added Eclipse Public License.|
|README.txt||Updated readme to reflect new argmap scheme.|
|cluster_launcher.py||Added more to the description files.|
|old-version-history.txt||Cleaned up documentation; version bump.|
|project.clj||thelmuth/fix-crlf and simplification fix|
README.txt Lee Spector (email@example.com), started 20100227 See version history at https://github.com/lspector/Clojush/commits/master Older version history is in old-version-history.txt. This is the README file accompanying Clojush, an implementation of the Push programming language and the PushGP genetic programming system in the Clojure programming language. Among other features this implementation takes advantage of Clojure's facilities for multi-core concurrency. AVAILABILITY https://github.com/lspector/Clojush/ REQUIREMENTS To use this code you must have a Clojure programming environment; see http://clojure.org/. The current version of Clojush requires Clojure 1.3. Clojure is available for most OS platforms. A good starting point for obtaining and using Clojure is http://dev.clojure.org/display/doc/Getting+Started. QUICKSTART Using Leiningen (from https://github.com/technomancy/leiningen) you can run an example from the OS command line (in the Clojush directory) with a call like: lein run clojush.examples.simple-regression If you would like to change a parameter, you may do so at the command line. For example, to change the default population size from 1000 to 50, call: lein run clojush.examples.simple-regression :population-size 50 Additional parameters may also be specified. The above calls will load everything and run PushGP on a simple symbolic regression problem (symbolic regression of y=x^3-2x^2-x). Although the details will vary from run to run, and it's possible that it will fail, this usually succeeds in a few generations. Another option is to evaluate in the leinigen REPL (Read Eval Print Loop): sh> lein repl ... clojush.core=> (use 'clojush.examples.simple-regression) ... clojush.core=> (pushgp argmap) Arguments to pushgp are specified in the argmap var in the problem's namespace. To run the examples in an IDE (Integrated Development Environment) for Clojure such as Clooj or Eclipse/Counterclockwise, load one of the files in src/clojush/examples into the IDE's REPL, type "(pushgp argmap)" into the REPL's input area, and hit the enter key. For large-scale runs you may want to provide additional arguments to Java in order to allow access to more memory and/or to take maximal advantage of Clojure's concurrency support in the context of Clojush's reliance on garbage collection. For example, you might want to provide arguments such as -Xmx2000m and -XX:+UseParallelGC. Details will depend on the method that you use to launch your code. DESCRIPTION Clojush is a version of the Push programming language for evolutionary computation, and the PushGP genetic programming system, implemented in clojure. More information about Push and PushGP can be found at http://hampshire.edu/lspector/push.html. Clojush derives mainly from Push3 (for more information see http://hampshire.edu/lspector/push3-description.html, http://hampshire.edu/lspector/pubs/push3-gecco2005.pdf) but it is not intended to be fully compliant with the Push3 standard and there are a few intentional differences. It was derived most directly from the Scheme implementation of Push/PushGP (called Schush). There are several differences between Clojush and other versions of Push3 -- for example, almost all of the instruction names are different because the "." character has special significance in Clojure -- and these are listed below. If you want to understand the motivations for the development of Push, and the variety of things that it can be used for, you should read a selection of the documents listed at http://hampshire.edu/lspector/push.html, probably starting with the 2002 Genetic Programming and Evolvable Machines article that can be found at http://hampshire.edu/lspector/pubs/push-gpem-final.pdf. But bear in mind that Push has changed over the years, and that Clojush is closest to Push3 (references above). Push can be used as the foundation of many evolutionary algorithms, not only PushGP (which is more or less a standard GP system except that it evolves Push programs rather than Lisp-style function trees -- which can make a big difference!). It was developed primarily for "meta-genetic-programming" or "autoconstructive evolution" experiments, in which programs and genetic operators co-evolve or in which programs produce their own offspring while also solving problems. But it turns out that Push has a variety of uniquely nice features even within a more traditional genetic programming context; for example it makes it unusually easy to evolve programs that use multiple data types, it provides novel and automatic forms of program modularization and control structure co-evolution, and it allows for a particularly simple form of automatic program simplification. Clojush can serve as the foundation for other evolutionary algorithms, but only the core Push interpreter and a version of PushGP are provided here. USAGE Example calls to PushGP are provided in other accompanying files. Push programs are run calling run-push, which takes as arguments a Push program and a Push interpreter state that can be made with make-push-state. If you are planning to use PushGP then you will want to use this in the error function (a.k.a. fitness function) that you pass to the pushgp function. Here is a simple example of a call to run-push, adding 1 and 2 and returning the top of the integer stack in the resulting interpreter state: (top-item :integer (run-push '(1 2 integer_add) (make-push-state))) If you want to see every step of execution you can pass an optional third argument of true to run-push. This will cause a representation of the interpreter state to be printed at the start of execution and after each step. Here is the same example as above but with each step printed: (top-item :integer (run-push '(1 2 integer_add) (make-push-state) true)) See the "parameters" section of the code for some parameters that will affect execution, e.g. whether code is pushed onto and/or popped off of the code stack prior to/after execution, along with the evaluation limits (which can be necessary for halting otherwise-infinite loops, etc.). Run-push returns the Push state that results from the program execution; this is a Clojure map mapping type names to data stacks. In addition, the map returned from run-push will map :termination to :normal if termination was normal, or :abnormal otherwise (which generally means that execution was aborted because the evaluation limit was reached. Random code can be generated with random-code, which takes a size limit and a list of "atom generators." Size is calculated in "points" -- each atom and each pair of parentheses counts as a single point. Each atom-generator should be a constant, or the name of a Push instruction (in which case it will be used literally), or a Clojure function that will be called with no arguments to produce a constant or a Push instruction. This is how "ephemeral random constants" can be incorporated into evolutionary systems that use Clojush -- that is, it is how you can cause random constants to appear in randomly-generated programs without including all possible constants in the list of elements out of which programs can be constructed. Here is an example in which a random program is generated, printed, and run. It prints a message indicating whether or not the program terminated normally (which it may not, since it may be a large and/or looping program, and since the default evaluation limit is pretty low) and it returns the internal representation of the resulting interpreter state: (let [s (make-push-state) c (random-code 100 ;; size limit of 100 points (concat @registered-instructions ;; all registered instrs (list (fn  (rand-int 100)) ;; random integers from 0-99 (fn  (rand)))))] ;; random floats from 0.0-1.0 (printf "\n\nCode: %s\n\n" (apply list c)) (run-push c s)) If you look at the resulting interpreter state you will see an "auxiliary" stack that is not mentioned in any of the Push publications. This exists to allow for auxiliary information to be passed to programs without using global variables; in particular, it is used for the "input instructions" in some PushGP examples. One often passes data to a Push program by pushing it onto the appropriate stacks before running the program, but in many cases it can also be helpful to have an instruction that re-pushes the input whenever it is needed. The auxiliary stack is just a convenient place to store the values so that they can be grabbed by input instructions and pushed onto the appropriate stacks when needed. Perhaps you will find other uses for it as well, but no instructions are provided for the auxiliary stack in Clojush (aside from the problem-specific input functions in the examples). The pushgp function is used to run PushGP. It takes all of its parameters as keyword arguments, and provides default values for any parameters that are not provided. See the pushgp defn in pushgp/pushgp.clj for details. The single argument that must be provided (actually it too has a default, but it makes no sense to rely on that) is :error-function, which should be a function that takes a Push program and returns a list of errors. Note that this assumes that you will be doing single-objective evolution with the objective being thought of as an error to be minimized. This assumption not intrinsic to Push or PushGP; it's just the simplest and most standard thing to do, so it's what I've done here. One could easily hack around that. In the most generic applications you'll want to have your error function run through a list of inputs, set up the interpreter and call run-push for each, calculate an error for each (potentially with penalties for abnormal termination, etc.), and return a list of the errors. Not all of the default arguments to pushgp will be reasonable for all problems. In particular, the default list of atom-generators -- which is ALL registered instructions, a random integer generator (in the range from 0-99) and a random float generator (in the range from 0.0 to 1.0) -- will be overkill for many problems and is so large that it may make otherwise simple problems quite difficult because the chances of getting the few needed instructions together into the same program will be quite low. But on the other hand one sometimes discovers that interesting solutions can be formed using unexpected instructions (see the Push publications for some examples of this). So the set of atom generators is something you'll probably want to play with. The registered-for-type function can make it simpler to include or exclude groups of instructions. This is demonstrated in some of the examples. Other pushgp arguments to note include those that control genetic operators (mutation, crossover, and simplification). The specified operator probabilities should sum to 1.0 or less -- any difference between the sum and 1.0 will be the probability for "straight" (unmodified) reproduction. The use of simplification is also novel here. Push programs can be automatically simplified -- to some extent -- in a very straightforward way: because there are almost no syntax constraints you can remove anything (one or more atoms or sub-lists, or a pair of parentheses) and still have a valid program. So the automatic simplification procedure just iteratively removes something, checks to see what that does to the error, and keeps the simpler program if the error is the same (or lower!). Automatic simplification is used in this implementation of PushGP in three places: 1. There is a genetic operator that adds the simplified program to the next generation's population. The use of the simplification genetic operator will tend to keep programs smaller, but whether this has benificial or detrimental effects on search performance is a subject for future research. 2. A specified number of simplification iterations is performed on the best program in each generation. This is produced only for the sake of the report, and the result is not added to the population. It is possible that the simplified program that is displayed will actually be better than the best program in the population. Note also that the other data in the report concerning the "best" program refers to the unsimplified program. 3. Simplification is also performed on solutions at the ends of runs. Note that the automatic simplification procedure will not always find all possible simplifications even if you run it for a large number of iterations, but in practice it does often seem to eliminate a lot of useless code (and to make it easier to perform further simplification by hand). If you've read this far then the best way to go further is probably to read and run the examples. IMPLEMENTATION NOTES A Push interpreter state is represented here as a Clojure map that maps type names (keywords) to stacks (lists, with the top items listed first). Push instructions are names of Clojure functions that take a Push interpreter state as an argument and return it modified appropriately. The define-registered macro is used to establish the definitions and also to record the instructions in the global list registered-instructions. Most instructions that work the same way for more than one type are implemented using a higher-order function that takes a type and returns a function that takes an interpreter state and modifies it appropriately. For example there's a function called popper that takes a type and returns a function -- that function takes a state and pops the right stack in the state. This allows us to define integer_pop with a simple form: (define-registered integer_pop (popper :integer)) In many versions of Push RUNPUSH takes initialization code or initial stack contents, along with a variety of other parameters. The implementation of run-schush here takes only the code to be run and the state to modify. Other parameters are set globally in the parameters section below. At some point some of these may be turned into arguments to run-push so that they aren't global. Miscellaneous differences between clojush and Push3 as described in the Push3 specification: - Clojush instruction names use "_" instead of "." since the latter has special meaning when used in Clojure symbols. - Equality instructions use "eq" rather than "=" since the latter in not explicitly allowed in clojure symbols. - for similar reasons +, -, *, /, %, <, and > are replaced with add, sub, mult, div, mod, lt, and gt. - Boolean literals are true and false (instead of TRUE and FALSE in the Push3 spec). The original design decision was based on the fact that Common Lisp's native Boolean literals couldn't used without conflating false and the empty list (both NIL in Common Lisp). - Clojush adds exec_noop (same as code_noop). - Clojush includes an execution time limit (via the parameter evalpush-time-limit) that may save you from exponential code growth or other hazards. But don't forget to increase it if you expect legitimate programs to take a long time. Push3 stuff not (yet) implemented: - NAME type/stack/instructions - Other missing instructions: *.DEFINE, CODE.DEFINITION, CODE.INSTRUCTIONS - The configuration code and configuration files described in the Push3 spec have not been implemented here. The approach here is quite different, so this may never be implemented TO DO (SOMETIME, MAYBE) - Implement remaining instructions in the Push3 specification. - Add more examples. - Add support for seeding the random number generator. - Add improved genetic operators, e.g. fair mutation/crossover. - Improve the automatic simplification algorithm. - Possibly rename the auxiliary stack the "input" stack if no other uses are developed for it. - Write a "sufficient-args" fn/macro to clean up Push instruction definitions. ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grant No. 1017817. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.