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Genetic Programming

Genetic Programming. Part 2 Jay Shaffstall. Genetic Programming. Review of Part 1 Program graphing Program breeding Program mutation Fitness functions Development life cycle Real world applications Conclusion. Review of Part 1.

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Genetic Programming

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  1. Genetic Programming Part 2 Jay Shaffstall

  2. Genetic Programming • Review of Part 1 • Program graphing • Program breeding • Program mutation • Fitness functions • Development life cycle • Real world applications • Conclusion

  3. Review of Part 1 • Genetic programming takes advantage of the power of evolution to grow a program • The fitness function determines which programs from any generation will survive to breed • The best programs out of every generation create the next generation

  4. Review of Part 1 • After millions of generations, we end up with a program that can solve the problem • But how does it all work?

  5. Program Graphing • In order to breed programs, we have to represent them in some fashion. Genetic programming represents a program as a graph. • Programs are represented using a lisp like syntax, with the operation coming before the arguments

  6. Program Graphing

  7. Program Breeding • So we have two programs represented as program graphs. How do these two programs combine to form children? • We randomly choose a node in each program. • We delete the subtree from the first program and replace it with the subtree from the second program

  8. Program Breeding -- Parents

  9. Program Breeding -- Children

  10. Program Mutation • Breeding is not the only way to change a program. We can also mutate a program. The program is changed in a random way, and the change will be passed on to any children.

  11. Program Mutation • Types of mutations • Replace a random subtree with another randomly generated program • Duplicate a subroutine, and randomly half the calls to use the new subroutine • Duplicate arguments to a subroutine, and randomly change half the references to use the new argument

  12. Program Mutation • Types of mutations (continued) • Create a new subroutine • Delete an existing subroutine • Delete an existing argument

  13. Fitness Functions • Eventually, between breeding and mutation, we can end up with some complex programs. • So how do we pick the ones that live and the ones that die?

  14. Fitness Functions • The basic purpose of a fitness function is to determine the distance between one program’s solution and the ideal solution. • This allows us to pick the “closest” solutions for breeding.

  15. Fitness Functions • An easy example is a program that must calculate the square of a number. • The fitness function can be something like this:square (x) – program (x)

  16. Fitness Functions • Those programs that score the lowest in the fitness function are those that are closest to the ideal solution. • Those are the programs that we choose to live and breed.

  17. Fitness Functions • So how do we develop a fitness function for a complex problem, without running into the automated oracle problem? • For example, the problem of writing an order entry system?

  18. Fitness Functions • The simple answer is that we cannot reliably create a fitness function for complex problems. • Does this mean that genetic programming is useless for more complex problems? • Not necessarily.

  19. Fitness Functions • One possibility is to break the problem down into simpler modules that can be represented by a less complex fitness function. • We then evolve all the modules, which will work together to create the final product.

  20. Fitness Functions • So this approach would be a combination of designing the system by a human, and evolving the pieces of the system by a computer.

  21. Development Life Cycle • At first glance, genetic programming seems to require a waterfall method of development. • We’ve all seen that waterfall development only works for simple projects. • What life cycle do we use for complex projects using genetic programming?

  22. Development Life Cycle • The best fit seems to be an iterative cycle, where a human designs the architecture down to simple modules that are then evolved to meet requirements. • As requirements change, only those modules that are affected need to be re-evolved.

  23. Development Life Cycle • Since the modules are relatively simple, re-evolving them will not take as much time as re-evolving the entire program. • We also reduce the chance of getting unexpected features in the program, since the user-visible features will be specified in the human created design of the modules.

  24. Development Life Cycle • In this type of life cycle, we use genetic programming as an entry level programmer, leaving humans free to do the higher level design work.

  25. Real World Applications • So, how closely can genetic programming approach the results of a program written by a human? • John Koza, regarded as the inventor of genetic programming, took a number of problems in circuit design and evolved programs to see how competitive genetic programming was.

  26. Real World Applications • The hardware he used to do this was a Beowulf style cluster of 1,000 Pentium II processors. • Clearly, for anything more than trivial problems, significant computing power is needed.

  27. Real World Applications • What Koza found is that genetic programming does indeed produce results competitive with human designed circuits. • In fact, in several cases genetic programming produced results that were better than human created results

  28. Real World Applications • Unfortunately, several of the genetically programmed designs also infringed on exiting U.S. patents. • This has led Koza to the idea that genetic programming can be used as an invention creator. Give it a problem you would like to solve, but have no idea how, and let it run.

  29. Conclusion • We’ve seen how genetic programming works, and how it can produce competitive results for well defined problems. • Most of our problems in the business arena are not well defined, but I’ve suggested a way in which genetic programming could be a useful tool for implementing well defined pieces of a program.

  30. Conclusion • While I don’t expect business to start using genetic programming, after doing the research for this presentation, I think it could very well have a place in a business environment.

  31. References • Koza, John, et al. (1999) Genetic Programming III Morgan Kaufmann Publishers • John Koza's Home Page, includes basic information on genetic programming, available on the web at http://www.genetic-programming.com/johnkoza.html  • Koza, John, et al Human Competitive Machine Intelligence available on the web at http://www.genetic-programming.com/humancompetitive.html 

  32. References • Qureshi, Adil GPsys 2b: Java Genetic Programmin System available on the web at http://www.cs.ucl.ac.uk/staff/A.Qureshi/gpsys.html  • Genetic Programming Conference homepage, available on the web at http://www.genetic-programming.org/ • Genetic Programming Tutorial, available on the web at http://www.geneticprogramming.com/Tutorial/

  33. References • Genetic Programming, available on the web at http://www2.informatik.uni-erlangen.de/~jacob/Evolvica/GP/Java/html/genprog/gp.html • Fundamentals in Genetic Programming, available on the web at http://www.dd.chalmers.se/~f96edfa/

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