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Problem Set 4

Problem Set 4. Genetic Programming and LIL-GP. Where we’ve been. Problem Sets 1 and 2: Theory. Where we’ve been. Problem Sets 1 and 2: Theory Combinatorics. Where we’ve been. Problem Sets 1 and 2: Theory Combinatorics

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Problem Set 4

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  1. Problem Set 4 Genetic Programming and LIL-GP

  2. Where we’ve been • Problem Sets 1 and 2: Theory

  3. Where we’ve been • Problem Sets 1 and 2: Theory • Combinatorics

  4. Where we’ve been • Problem Sets 1 and 2: Theory • Combinatorics • Simple Genetic Algorithms (a.k.a. How to run an effective hamburger chain)

  5. Where we’ve been • Problem Sets 1 and 2: Theory • Combinatorics • Simple Genetic Algorithms (a.k.a. How to run an effective hamburger chain) • Fundamental Theorem of Genetic Algorithms

  6. Where we’ve been • Problem Sets 1 and 2: Theory • Combinatorics • Simple Genetic Algorithms (a.k.a. How to run an effective hamburger chain) • Fundamental Theorem of Genetic Algorithms • Hyperplane madness

  7. Where we’ve been • Problem Set 3: Genetic Algorithms and the GENESIS system

  8. Where we’ve been • Problem Set 3: Genetic Algorithms and the GENESIS system • Optimization of the function x10

  9. Where we’ve been • Problem Set 3: Genetic Algorithms and the GENESIS system • Optimization of the function x10 • Minimization of the De Jong function

  10. Where we’re going • Problem Set 4: Genetic Programming and LIL-GP

  11. Where we’re going • Problem Set 4: Genetic Programming and LIL-GP • Evolve LISP-like programs without writing one line of LISP! (sorry, John)

  12. Where we’re going • Problem Set 4: Genetic Programming and LIL-GP • Evolve LISP-like programs without writing one line of LISP! (sorry, John) • Solve two simple problems: symbolic regression of a quadratic function, and symbolic regression of a boolean even-3-parity function.

  13. Getting the software • Change to your cs426 directory.cd cs426

  14. Getting the software • Change to your cs426 directory.cd cs426 • Copy the software to your AFS space.cp –r /afs/ir/class/cs426/lilgp .This will create a directory called lilgp under your cs426 directory.

  15. Uncompressing the code • Change to your new lilgp directory.cd lilgpYou should have one file: lilgp.tar

  16. Uncompressing the code • Change to your new lilgp directory.cd lilgpYou should have one file: lilgp.tar • Uncompress with the following command:tar –xvf lilgp.tar

  17. Uncompressing the code You should now have a directory called lilgp1.1. Under this directory you only need to deal with the following subdirectories: • 1.1: All the code. • htmlMan: All the documentation. Don’t worry about the others.

  18. Where to start • Browse through the documentation. It is indexed and easy to follow. If you want to download it yourself, you can get it at:http://garage.cps.msu.edu/software/software-index.html#lilgp • Start with: lil-gp.contents.htm

  19. Where to start • Try running one of the samples. They’re located in the app directory. After compiling and building (just type make), they’re ready to run. Try the regression sample. To run, type:gp –f input.fileIf the program does not start running, there’s something wrong with your installation.

  20. Warnings: • Don’t change anything in the kernel directory.

  21. Warnings: • Don’t change anything in the kernel directory. • Don’t change anything in the kernel directory.

  22. Warnings: • Don’t change anything in the kernel directory. • Don’t change anything in the kernel directory. • Don’t modify the makefiles. Even though they’re called GNUmakefile, typing make still works.

  23. Implementing a problem • Fill in a tableau, deciding on terminal sets, function sets, fitness determination, etc.

  24. Implementing a problem • Fill in a tableau, deciding on terminal sets, function sets, fitness determination, etc. • Write the code.

  25. Implementing a problem • Fill in a tableau, deciding on terminal sets, function sets, fitness determination, etc. • Write the code. • Create a parameter file for the run.

  26. Implementing a problem • Fill in a tableau, deciding on terminal sets, function sets, fitness determination, etc. • Write the code. • Create a parameter file for the run. • Run the code and examine the output files.

  27. Tableau • Terminal set: The leaves of the expression tree.

  28. Tableau • Terminal set: The leaves of the expression tree. • Function set: The internal nodes.

  29. Tableau • Terminal set: The leaves of the expression tree. • Function set: The internal nodes. • ex: symbolic regressionterminal set = {x}function set = {+, -, *, %}

  30. Tableau • Fitness • Raw fitness – sum of error over fitness cases, number of hits, etc.

  31. Tableau • Fitness • Raw fitness – sum of error over fitness cases, number of hits, etc. • Standardized fitness – all positive; 0 is best

  32. Tableau • Fitness • Raw fitness – sum of error over fitness cases, number of hits, etc. • Standardized fitness – all positive; 0 is best • Adjusted fitness – in between 0 and 1, with 1 being the most fit individual

  33. Tableau • In general, LIL-GP was designed after what is described in Genetic Programming (Koza, 1992), so all terminology is described in the book. See chapters 6 and 7 for full explanations of all terms used in the program.

  34. Writing the code • Files you need to provide: • app.h • app.c • appdef.h • function.h • function.c

  35. Writing the code • Files you need to provide: • app.h – Defines the global data structure used to pass information back and forth between files. • app.c – Defines all the user callbacks – initialization, fitness evaluation, etc.

  36. Writing the code • Files you need to provide: • appdef.h – Two #defines: MAX_ARGS, and DATATYPE. • MAX_ARGS: Maximum number of arguments your functions will take • DATATYPE: Type that all of your functions will return (ex: double, int, etc.)

  37. Writing the code • Files you need to provide: • function.h – Prototypes for all of the functions in your function set. • function.c – Implementations of the functions listed in function.h.

  38. Writing the code • In general, all of the tricky code has been written for you. In fact, two of the sample applications are: • Symbolic regression of a function • Boolean-11 multiplexer Both are explained in GP. You do not need to rewrite the functions in app.c, just modify them.

  39. Creating a parameter file • The parameter files included with the samples are called input.file. Just modify these for the homework problems. If you want to learn more about the extensive parameters that can be set, look in the documentation.

  40. Creating a parameter file • The only confusion about parameter files will come in specifying crossover rates for the first problem. To avoid this confusion, here is what you need to put in your parameter file to meet the problem criteria:

  41. Creating a parameter file • Parameters for proper breeding: breed_phases = 4 breed[1].operator = crossover, select=fitness, internal=0.0 breed[1].rate = 0.1 breed[2].operator = crossover, select=fitness, internal=1.0 breed[2].rate = 0.8

  42. Creating a parameter file • Parameters for proper breeding: breed[3].operator = reproduction, select=fitness breed[3].rate = 0.1 breed[4].operator = mutation, select=fitness breed[4].rate = 0.00

  43. Creating a parameter file • Aside from this, all you need to change is pop_size, max_generations, random_seed, and perhaps output.basename. The parameter output.basename sets the file prefix for all of the output files.

  44. Creating a parameter file • For problems 2 and 3, the default crossover parameters work fine. No need to change them unless you’re curious.

  45. Running the code • Very simple to run the code: • Compile and build with the command make. If there are compile errors, it will tell you now – fix them before continuing.

  46. Running the code • Very simple to run the code: • Compile and build with the command make. If there are compile errors, it will tell you now – fix them before continuing. • Run the code with the command:gp –f input.file

  47. Output files • LIL-GP generates lots of good stuff: • .sys – general info about the run • .gen – stats on tree size and depth • .prg – stats on fitness and hits • .bst – info about current best individual • .his – history of the .bst file • .stt – unreadable version of all stats You will want the .bst and .his files.

  48. Problem 1 • This problem deals with symbolic regression of the function x2/2+2x+2. There is a sample app called “regression” provided. All you need to do is modify the input.file as described, delete a bunch of lines in app.c and function.h/c, change a few numbers in app.c, and it will run. Guaranteed.

  49. Problem 1 • Hint: With the provided random seed, it will not find a close solution even after 151 generations. Fiddle with the random seed and you will quickly find a perfect solution in much fewer than 151 generations.

  50. Problem 2 • This problem deals with symbolic regression of a boolean function that performs even-3-parity. Even-3-parity returns true given three inputs if an even number of inputs are true (i.e., 0 true, or 2 true).

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