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Genetic programming

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Dr. C.V. Suresh Babu
Dr. C.V. Suresh Babu

This presentation discusses about Genetic Programming

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Department of Information Technology 1Soft Computing (ITC4256 ) Dr. C.V. Suresh Babu Professor Department of IT Hindustan Institute of Science & Technology Genetic Programming
Department of Information Technology 2Soft Computing (ITC4256 ) What is Genetic Programming? • Genetic programming is a model of programming which uses the ideas (and some of the terminology) of biological evolution to handle a complex problem. … Genetic programming can be viewed as an extension of the genetic algorithm, a model for testing and selecting the best choice among a set of results, each represented by a string.
Department of Information Technology 3Soft Computing (ITC4256 ) Outline of the Genetic Algorithm • Randomly generate a set of possible solutions to a problem, representing each as a fixed length character string • Test each possible solution against the problem using a fitness function to evaluate each solution • Keep the best solutions, and use them to generate new possible solutions • Repeat the previous two steps until either an acceptable solution is found, or until the algorithm has iterated through a given number of cycles (generations)
Department of Information Technology 4Soft Computing (ITC4256 ) Why Genetic Programming? • “It is difficult, unnatural, and overly restrictive to attempt to represent hierarchies of dynamically varying size and shape with fixed length character strings.” “For many problems in machine learning and artificial intelligence, the most natural representation for a solution is a computer program.” [Koza, 1994] • A parse tree is a good representation of a computer program for Genetic Programming
Department of Information Technology 5Soft Computing (ITC4256 ) Using Trees To Represent Computer Programs (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y Functions Terminals
Department of Information Technology 6Soft Computing (ITC4256 ) Genetic Operations • Random Generation of the initial population of possible solutions • Mutation of promising solutions to create new possible solutions • Genetic Crossover of two promising solutions to create new possible solutions
Department of Information Technology 7Soft Computing (ITC4256 ) Randomly Generating Programs • Randomly generate a program that takes two arguments and uses basic arithmetic to return an answer – Function set = {+, -, *, /} – Terminal set = {integers, X, Y} • Randomly select either a function or a terminal to represent our program • If a function was selected, recursively generate random programs to act as arguments
Department of Information Technology 8Soft Computing (ITC4256 ) Randomly Generating Programs (+ …) +
Department of Information Technology 9Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 …) + 2
Department of Information Technology 10Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 …) + 2 3
Department of Information Technology 11Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* …) …) + 2 3 *
Department of Information Technology 12Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* X 7) (/ …)) + 2 3 * / X 7
Department of Information Technology 13Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y
Department of Information Technology 14Soft Computing (ITC4256 ) Mutation (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y
Department of Information Technology 15Soft Computing (ITC4256 ) Mutation (+ 2 3 (* X 7) (/ Y 5)) + 2 3 / 5Y * X 7 First pick a random node
Department of Information Technology 16Soft Computing (ITC4256 ) Mutation (+ 2 3 (+ (* 4 2) 3) (/ Y 5)) + 2 3 + / * 3 5Y Delete the node and its children, and replace with a randomly generated program 24
Department of Information Technology 17Soft Computing (ITC4256 ) Crossover (+ X (* 3 Y)) + X * 3 Y (- (/ 25 X) 7) - / 7 25 X
Department of Information Technology 18Soft Computing (ITC4256 ) Crossover (+ X (* 3 Y)) + X * Pick a random node in each program 3 Y (- (/ 25 X) 7) - / 7 25 X
Department of Information Technology 19Soft Computing (ITC4256 ) Crossover (+ X (* (/ 25 X) Y)) + X * Swap the two nodes 3 Y (- 3 7) - / 7 25 X
Department of Information Technology 20Soft Computing (ITC4256 ) What About Just Randomly Generating Programs? • Is Genetic Programming really better than just randomly creating new functions? • Yes! – Pete Angeline compared the result of evolving a tic-tac-toe algorithm for 200 generations, with a population size of 1000 per generation, against 200,000 randomly generated algorithms – The best evolved program was found to be significantly superior to the best randomly generated program [Genetic Programming FAQ, 2002] • The key lies in using a fitness measure to determine which functions survive to reproduce in each generation
Department of Information Technology 21Soft Computing (ITC4256 ) Building a Better Mouse • Apply Genetic Programming to the problem of navigating a maze • What are our terminal and function sets? – Function Set = {If-Movement-Blocked, While-Not-At-Cheese*} – Terminal Set = {Move-Forward, Turn-Left, Turn-Right} * While-Not-At-Cheese will be used exclusively as the root node of the parse tree
Department of Information Technology 22Soft Computing (ITC4256 ) Building a Better Mouse How to get the starving mouse to the cheese? Is there a better solution for this maze? How good is this solution? One possible solution: While not at the cheese If the way ahead is blocked Turn left 90 degrees Otherwise Move forward Turn right 90 degreesCheese? Blocked?
Department of Information Technology 23Soft Computing (ITC4256 ) Building a Better Mouse A fitness function: Cheese? Blocked? • Each function and terminal other than the root node shall cost one unit to execute • If the mouse spends more than 100 units, it dies of hunger • The fitness measure for a program is determined be executing the program, then squaring the sum of the total units spent and the final distance from the exit • A lower fitness measure is preferable to a higher fitness measure Our mouse will die 10 moves from the exit after spending 100 units, so the fitness measure for our program is 12100
Department of Information Technology 24Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees
Department of Information Technology 25Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Turn left 90 degrees Mutation:
Department of Information Technology 26Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees While not at the cheese (11664) If the way ahead is blocked Move forward one space Turn right 90 degrees Otherwise Move forward one space Crossover: While not at the cheese (12996) Turn left 90 degrees Turn left 90 degrees
Department of Information Technology 27Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese If the way ahead is blocked Turn right 90 degrees Move forward one space Move forward one space Move forward one space Otherwise Move forward one space Turn right 90 degrees Move forward one space Move forward one space Turn left 90 degrees If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space (after 4202 generations, with 1000 programs per generation) Is this better? Fitness measure: 2809
Department of Information Technology 28Soft Computing (ITC4256 ) Real World Applications • Lockheed Martin Missiles and Space Co. - Near-Minimum-Time Spacecraft Maneuvers [Howley, 96] • GP applied to the problem of rest-to-rest reorientation maneuvers for satellites • Optimal time solution is a vector of nonlinear differential equations, which are difficult to solve • An approximate solution is necessary for a real-time controller • Results: Rest-to-Rest Maneuver Times (8 test cases) – Optimal Solution: 287.93 seconds – Expert Solution: 300.3 seconds – GP Solution: 292.8 seconds
Department of Information Technology 29Soft Computing (ITC4256 ) Real World Applications • Symbolic Regression • Problem: Given a set of data points, find a mathematical model
Department of Information Technology 30Soft Computing (ITC4256 ) Real World Applications • Neural Network Optimization [Zhang, Mühlenbein, 1993] • Image Analysis [Poli, 1996a] • Generation of a knowledge base for expert systems [Bojarczuk, Lopes, Freitas, 2000] • Fuzzy Logic Control [Akbarzadeh, Kumbla, Tunstel, Jamshidi, 2000] • Hardware Evolution (Field-Programmable Gate Array) [Thompson, 1997]

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Genetic programming

  • 1. Department of Information Technology 1Soft Computing (ITC4256 ) Dr. C.V. Suresh Babu Professor Department of IT Hindustan Institute of Science & Technology Genetic Programming
  • 2. Department of Information Technology 2Soft Computing (ITC4256 ) What is Genetic Programming? • Genetic programming is a model of programming which uses the ideas (and some of the terminology) of biological evolution to handle a complex problem. … Genetic programming can be viewed as an extension of the genetic algorithm, a model for testing and selecting the best choice among a set of results, each represented by a string.
  • 3. Department of Information Technology 3Soft Computing (ITC4256 ) Outline of the Genetic Algorithm • Randomly generate a set of possible solutions to a problem, representing each as a fixed length character string • Test each possible solution against the problem using a fitness function to evaluate each solution • Keep the best solutions, and use them to generate new possible solutions • Repeat the previous two steps until either an acceptable solution is found, or until the algorithm has iterated through a given number of cycles (generations)
  • 4. Department of Information Technology 4Soft Computing (ITC4256 ) Why Genetic Programming? • “It is difficult, unnatural, and overly restrictive to attempt to represent hierarchies of dynamically varying size and shape with fixed length character strings.” “For many problems in machine learning and artificial intelligence, the most natural representation for a solution is a computer program.” [Koza, 1994] • A parse tree is a good representation of a computer program for Genetic Programming
  • 5. Department of Information Technology 5Soft Computing (ITC4256 ) Using Trees To Represent Computer Programs (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y Functions Terminals
  • 6. Department of Information Technology 6Soft Computing (ITC4256 ) Genetic Operations • Random Generation of the initial population of possible solutions • Mutation of promising solutions to create new possible solutions • Genetic Crossover of two promising solutions to create new possible solutions
  • 7. Department of Information Technology 7Soft Computing (ITC4256 ) Randomly Generating Programs • Randomly generate a program that takes two arguments and uses basic arithmetic to return an answer – Function set = {+, -, *, /} – Terminal set = {integers, X, Y} • Randomly select either a function or a terminal to represent our program • If a function was selected, recursively generate random programs to act as arguments
  • 8. Department of Information Technology 8Soft Computing (ITC4256 ) Randomly Generating Programs (+ …) +
  • 9. Department of Information Technology 9Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 …) + 2
  • 10. Department of Information Technology 10Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 …) + 2 3
  • 11. Department of Information Technology 11Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* …) …) + 2 3 *
  • 12. Department of Information Technology 12Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* X 7) (/ …)) + 2 3 * / X 7
  • 13. Department of Information Technology 13Soft Computing (ITC4256 ) Randomly Generating Programs (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y
  • 14. Department of Information Technology 14Soft Computing (ITC4256 ) Mutation (+ 2 3 (* X 7) (/ Y 5)) + 2 3 * / X 7 5Y
  • 15. Department of Information Technology 15Soft Computing (ITC4256 ) Mutation (+ 2 3 (* X 7) (/ Y 5)) + 2 3 / 5Y * X 7 First pick a random node
  • 16. Department of Information Technology 16Soft Computing (ITC4256 ) Mutation (+ 2 3 (+ (* 4 2) 3) (/ Y 5)) + 2 3 + / * 3 5Y Delete the node and its children, and replace with a randomly generated program 24
  • 17. Department of Information Technology 17Soft Computing (ITC4256 ) Crossover (+ X (* 3 Y)) + X * 3 Y (- (/ 25 X) 7) - / 7 25 X
  • 18. Department of Information Technology 18Soft Computing (ITC4256 ) Crossover (+ X (* 3 Y)) + X * Pick a random node in each program 3 Y (- (/ 25 X) 7) - / 7 25 X
  • 19. Department of Information Technology 19Soft Computing (ITC4256 ) Crossover (+ X (* (/ 25 X) Y)) + X * Swap the two nodes 3 Y (- 3 7) - / 7 25 X
  • 20. Department of Information Technology 20Soft Computing (ITC4256 ) What About Just Randomly Generating Programs? • Is Genetic Programming really better than just randomly creating new functions? • Yes! – Pete Angeline compared the result of evolving a tic-tac-toe algorithm for 200 generations, with a population size of 1000 per generation, against 200,000 randomly generated algorithms – The best evolved program was found to be significantly superior to the best randomly generated program [Genetic Programming FAQ, 2002] • The key lies in using a fitness measure to determine which functions survive to reproduce in each generation
  • 21. Department of Information Technology 21Soft Computing (ITC4256 ) Building a Better Mouse • Apply Genetic Programming to the problem of navigating a maze • What are our terminal and function sets? – Function Set = {If-Movement-Blocked, While-Not-At-Cheese*} – Terminal Set = {Move-Forward, Turn-Left, Turn-Right} * While-Not-At-Cheese will be used exclusively as the root node of the parse tree
  • 22. Department of Information Technology 22Soft Computing (ITC4256 ) Building a Better Mouse How to get the starving mouse to the cheese? Is there a better solution for this maze? How good is this solution? One possible solution: While not at the cheese If the way ahead is blocked Turn left 90 degrees Otherwise Move forward Turn right 90 degreesCheese? Blocked?
  • 23. Department of Information Technology 23Soft Computing (ITC4256 ) Building a Better Mouse A fitness function: Cheese? Blocked? • Each function and terminal other than the root node shall cost one unit to execute • If the mouse spends more than 100 units, it dies of hunger • The fitness measure for a program is determined be executing the program, then squaring the sum of the total units spent and the final distance from the exit • A lower fitness measure is preferable to a higher fitness measure Our mouse will die 10 moves from the exit after spending 100 units, so the fitness measure for our program is 12100
  • 24. Department of Information Technology 24Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees
  • 25. Department of Information Technology 25Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Turn left 90 degrees Mutation:
  • 26. Department of Information Technology 26Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese (12996) If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space While not at the cheese (12996) Move forward one space Turn right 90 degrees Turn left 90 degrees While not at the cheese (11664) If the way ahead is blocked Move forward one space Turn right 90 degrees Otherwise Move forward one space Crossover: While not at the cheese (12996) Turn left 90 degrees Turn left 90 degrees
  • 27. Department of Information Technology 27Soft Computing (ITC4256 ) Building a Better Mouse While not at the cheese If the way ahead is blocked Turn right 90 degrees Move forward one space Move forward one space Move forward one space Otherwise Move forward one space Turn right 90 degrees Move forward one space Move forward one space Turn left 90 degrees If the way ahead is blocked Turn left 90 degrees Otherwise Move forward one space (after 4202 generations, with 1000 programs per generation) Is this better? Fitness measure: 2809
  • 28. Department of Information Technology 28Soft Computing (ITC4256 ) Real World Applications • Lockheed Martin Missiles and Space Co. - Near-Minimum-Time Spacecraft Maneuvers [Howley, 96] • GP applied to the problem of rest-to-rest reorientation maneuvers for satellites • Optimal time solution is a vector of nonlinear differential equations, which are difficult to solve • An approximate solution is necessary for a real-time controller • Results: Rest-to-Rest Maneuver Times (8 test cases) – Optimal Solution: 287.93 seconds – Expert Solution: 300.3 seconds – GP Solution: 292.8 seconds
  • 29. Department of Information Technology 29Soft Computing (ITC4256 ) Real World Applications • Symbolic Regression • Problem: Given a set of data points, find a mathematical model
  • 30. Department of Information Technology 30Soft Computing (ITC4256 ) Real World Applications • Neural Network Optimization [Zhang, Mühlenbein, 1993] • Image Analysis [Poli, 1996a] • Generation of a knowledge base for expert systems [Bojarczuk, Lopes, Freitas, 2000] • Fuzzy Logic Control [Akbarzadeh, Kumbla, Tunstel, Jamshidi, 2000] • Hardware Evolution (Field-Programmable Gate Array) [Thompson, 1997]