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Genetic Programming

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  1. Genetic Programming

  2. Genetic Algorithms • 1. Randomly initialize a population of chromosomes. • 2. While the terminating criteria have not been satisfied: • a. Evaluate the fitness of each chromosome: • i. Construct the phenotype (e.g. simulated robot) corresponding to the encoded genotype (chromosome). • ii. Evaluate the phenotype (e.g. measure the simulated robot’s walking abilities), in order to determine its fitness. • b. Remove chromosomes with low fitness. • c. Generate new chromosomes, using certain selection schemes and genetic operators.

  3. Genetic Algorithms • Fitness function • Selection scheme • Genetic operators • Crossover • Mutation

  4. Crossover and mutation.

  5. Agenda • What is Genetic Programming? • Background/History. • Why Genetic Programming? • How Genetic Principles are Applied. • Examples of Genetic Programs. • Future of Genetic Programming.

  6. Genetic Programming • John Koza, 1992 • Evolve program instead of bitstring • Lispprogram structure is best suited • Genetic operators can do simple replacements of sub-trees • All generated programs can be treated as legal (no syntax errors) Please review about Behavioral systems, Genetic Algorithms and Genetic Programming from the book. Chapters 20, 21, 22, 23.

  7. What is Genetic Programming(GP)? ROBOTICS Artificial Intelligence Machine learning evolutionary GP EP GA ES Artificial Intelligence

  8. Most widely used Robust uses 2 separate spaces search space - coded solution (genotype) solution space - actual solutions (phenotypes) Genetic Algorithms Genotypes must be mapped to phenotypes before the quality or fitness of each solution can be evaluated

  9. Like GP no distinction between search and solution space Individuals are represented as real-valued vectors. Simple ES one parent and one child Child solution generated by randomly mutating the problem parameters of the parent. Susceptible to stagnationat local optima Evolutionary Strategies

  10. Evolutionary Strategies (cont’d) • Slow to converge to optimal solution • More advanced ES • have pools of parents and children • Unlike GA and GP, ES • Separates parent individuals from child individuals • Selects its parent solutions deterministically

  11. Evolutionary Programming • Resembles ES, developed independently • Early versions of EP applied to the evolution of transition table of finite state machines • One population of solutions, reproduction is by mutation only • Like ES operates on the decision variable of the problem directly (ie Genotype = Phenotype) • Tournament selection of parents • better fitness more likely a parent • children generated until population doubled in size • everyone evaluated and the half of population with lowest fitness deleted.

  12. General Architecture of Evolutionary Algorithms

  13. Specialized form of GA Manipulates a very specific type of solution using modified genetic operators Original application was to design computer program Now applied in alternative areas eg. Analog Circuits Does not make distinction between search and solution space. Solution represented in very specific hierarchical manner. Genetic Programming

  14. Background/History • By John R. Koza, Stanford University. • 1992, Genetic Programming Treatise - “Genetic Programming. On the Programming of Computers by Means of Natural Selection.” - Origin of GP. • Combining the idea of machine learning and evolved tree structures.

  15. Why Genetic Programming? • It saves time by freeing the human from having to design complex algorithms. • Not only designing the algorithms but creating ones that give optimal solutions. • Again, Artificial Intelligence.

  16. What Constitutes a Genetic Program? • Starts with "What needs to be done" • Agent figures out "How to do it" • Produces a computer program - “Breeding Programs” • Fitness Test • Code reuse • Architecture Design - Hierarchies • Produce results that are competitive with human produced results

  17. How are Genetic Principles Applied? • “Breeding” computer programs. • Crossovers. • Mutations. • Fitness testing.

  18. Computer Programs as Trees • Infix/Postfix • (2 + a)*(4 - num) * - + 2 a 4 num

  19. “Breeding” Computer Programs Hmm hmm heh. Hey butthead. Do computer programs actually score?

  20. “Breeding” Computer Programs • Start off with a large “pool” of random computer programs. • Need a way of coming up with the best solution to the problem using the programs in the “pool” • Based on the definition of the problem and criteria specified in the fitness test, mutations and crossovers are used to come up with new programs which will solve the problem.

  21. The Fitness Test • Identifying the way of evaluating how good a given computer program is at solving the problem at hand. • How good can a program cope with its environment. • Can be measured in many ways, i.e. error, distance, time, etc…

  22. Fitness Test Criteria • Time complexity a good criteria. • i.e. n2vs.nlogn. • Accuracy - Values of variables. • Combinations of criteria may also be tested.

  23. Mutations in Nature Properties of mutations • Ultimate source of genetic variation. • Radiation, chemicals change genetic information. • Causes new genes to be created. • One chromosome. • Asexual. • Very rare. Before: acgtactggctaa After: acatactggctaa

  24. Genetic Programming • 1. Randomly generate a combinatorial set of computer programs. • 2. Perform the following steps iteratively until a termination criterion is satisfied • a. Execute each program and assign a fitness value to each individual. • b. Create a new population with the following steps: • i. Reproduction: Copy the selected program unchanged to the new population. • ii. Crossover: Create a new program by recombining two selected programs at a random crossover point. • iii. Mutation: Create a new program by randomly changing a selected program. • 3. The best sets of individuals are deemed the optimal solution upon termination

  25. Mutations in Programs • Single parental program is probabilistically selected from the population based on fitness. • Mutation point randomly chosen. • the subtree rooted at that point is deleted, and • a new subtree is grown there using the same random growth process that was used to generate the initial population. • Asexual operations (mutation) are typically performed sparingly: • with a low probability of, • probabilistically selected from the population based on fitness.

  26. Crossovers in Nature • Two parental chromosomes exchange part of their genetic information to create new hybrid combinations (recombinant). • No loss of genes, but an exchange of genes between two previous chromosomes. • No new genes created, preexisting old ones mixed together.

  27. Crossovers in Programs • Two parental programs are selected from the population based on fitness. • A crossover point is randomly chosen in the first and second parent. • The first parent is called receiving • The second parent is called contributing • The subtree rooted at the crossover point of the first parent is deleted • It is replaced by the subtree from the second parent. • Crossoveris the predominant operation in genetic programming (and genetic algorithm) research • It is performed with a high probability (say, 85% to 90%).

  28. Applications of GP in robotics • Wall-following robot – Koza • Behaviors of subsumption architecture of Brooks. Evolved a new behavior. • Box-moving robot – Mahadevon • Evolving behavior primitives and arbitrators • for subsumption architecture • Motion planning for hexapod – Fukuda, Hoshino, Levy PSU. • Evolving communication agents Iba, Ueda. • Mobile robot motion control – Walker. • for object tracking • Soccer • Car racing Population sizes from 100 to 2000

  29. This is a very very small subset of Lisp

  30. More functions • Atom, list, cons, car, cdr, numberp, arithmetic, relations. Cond. • Copying example

  31. A very important concept – Lisp does not distinguish data and programs

  32. Programs in Lisp are trees that are evaluated (calculated) Tree-reduction semantics

  33. Lisp allows to define special languages in itself

  34. This subset of Lisp was defined for a mobile robot • You can define similar subsets for a humanoid robot, robot hand, robot head or robot theatre

  35. As an exercise, you can think about behaviors of all Braitenberg Vehicles described by such programs

  36. You can define much more sophisticated mutations that are based on constraints and in general on your knowledge and good guesses (heuristics)

  37. EyeSim simulator allows to simulate robots, environments and learning strategies together with no real robot In our project with teens the feedback is from humans who evaluate the robot theatre performance (subjective) In our previous project with hexapod the feedback was from real measurement in environment (objective) Evolution here takes feedback from simulated environment (simulated)

  38. Evolution principles are the same for all evolutionary algorithms. • Each individual (Lisp program) is executed on the EyeSim simulator for a limited number of program steps. • The program performance is rated continually or when finished.

  39. Sample Problem: Ball Tracking A single robot is placed at a random position and orientation in a rectangular driving area closed by walls. A colored ball is also placed at a random position. Using its camera, the robot has to detect the ball, drive towards it, and stop close to it. The robot camera is positioned at an angle so the robot can see the wall ahead from any position in the field. However, the ball is not always visible. • Find ball in environment • Drive towards it • Stop when close to ball • Similar other tasks that we solved: • Collect cans • Shoot goal in soccer • Robot sumo • Robot fencing

  40. Idea for solving • In the loop, grab an image and analyze it as follows: • Convert the image from RGB to HSV. • Use the histogram ball detection routine from section 8.6 of the book (this returns a ball position in the range [0..79] (left.. Right) or no ball and a ball size in pixels [0..60]) • If the ball height is 20 pixels or more, then stop and terminate (the robot is close enough to the ball) • Otherwise • If no ball is detected or the ball position is less than 20, turn slowly left. • If the ball position is between 20 and 40, drive slowly straight • If the ball position is greater than 40, turn slowly right.

  41. ColSearch returns the x-position of the ball or -1 if not detected and the ball height in pixels. • The statement VWDriveWait following either VWDriveTurn or a VWDriveStraight command suspends execution driving or rotation of the requested distance or angle has finished.

  42. The evolution can start from random data or from good hand-coded solutions from humans Bear in mind that obj_size and obj_pose are in fact calls to the image processing subroutine.

  43. Evolution of tracking behavior • Koza suggests the following steps for setting up a genetic programming system: • 1. Establish an objective. • 2. Identify the terminals and functions used in the inductive programs • 3. Establish the selection scheme and its evolutionary operations • 4. Finalize the number of fitness cases • 5. Determine the fitness function and hence the range of raw fitness values • 6. Establish the generation gap G, and the population M • 7. Finalize all control parameters. • 8. Execute the Genetic Paradigm.

  44. EyeSim simulator allows to simulate robots, environments and learning strategies together with no real robot In our project with teens the feedback is from humans who evaluate the robot theatre performance (subjective) In our previous project with hexapod the feedback was from real measurement in environment (objective) Evolution here takes feedback from simulated environment (simulated)

  45. Summary on GP • Field of study in Machine Learning. • Created by John Koza in 1992. • Save time while creating better programs. • Based on the principles of genetics. • Symbolic Regression/Circuit Design.

  46. Example of using Genetic Programming in Robotics • Use of simulation

  47. Robot’s view and driving path in EyeSim simulator Robot trajectory

  48. You want to maximize this fitness function