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Genetic Programming: Hypothesis Evolution

Genetic Programming: Hypothesis Evolution. By Nam Nguyen Greg Nelson. Introduction To Evol. An attempt to more directly simulate the process of natural selection. The system is set up as a large board with agents and examples. Each agent executes on its own thread.

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Genetic Programming: Hypothesis Evolution

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  1. Genetic Programming:Hypothesis Evolution By Nam Nguyen Greg Nelson

  2. Introduction To Evol • An attempt to more directly simulate the process of natural selection. • The system is set up as a large board with agents and examples. • Each agent executes on its own thread. • Agents live on the board and wander around processing examples and interacting with other agents. • Two kinds of agents: helpers and fighters. • Examples act as food for the agents.

  3. Goal • Implement a new genetic algorithm that, rather than being based on probabilities, actually models the process of natural selection. • Build a dynamic, intuitive learning system that very closely emulates natural selection. • Find a balance in parameter settings (how much "life" an agent starts with, what the "age" threshold is in order to breed, how much an agent's "life" is boosted by eating an example, etc...); some settings cause agents to breed like rabbits and grow to doomsday, others lead to almost immediate extinction.

  4. Problem Representation • Environment is represented by a board. All entities live on this board. • Agents walk around, eat examples, breed, share information, and fight with other agents. E E

  5. Hierarchy Tree Entity Example Agent Fighter Helper

  6. Play Tennis Example • Example 1 (Outlook = Sunny) && (Temp = Hot) && (Humidity = High) && (Wind = Weak) Then (PlayTennis = Yes) • Bit string 10010010101 (First three bits for Outlook, etc…) • All hypotheses and examples are represented with this notation.

  7. Agents • The agents contain a genotype and phenotype. Both represent hypotheses. • The phenotype is used (and changed); the genotype is inherited from birth, never changed, and passed on to future generations. • Agents also interact by breeding, helping each other, or fighting.

  8. Examples Continued • Examples are created from the training data set and act as food for the agents. • When an agent comes upon an example it does one of two things: 1. If it classifies it correctly, it "eats" it, which increases its strength and likelihood for survival. 2. If not, it tries to learn something from it (change some of the phenotype bits to match the example’s).

  9. When Helpers Meet Other Helpers Helper 2 1110110 Helper 1 0110100

  10. You are too young. We should help each other. Okay, let’s be friends! Helper 2 1110110 Helper 1 0110100

  11. This is what I learned Here’s my knowledge Bit 5 is true Bit 6 is true Helper 2 1110110 Helper 1 0110100

  12. When Helpers Meet Other Helpers: part 2 Helper 1 Life 500 Helper 2 Life 800

  13. How old are you?

  14. I am above threshold

  15. Censored

  16. Several Cycles Later… Helper 3 (Baby) Life 500 Helper 1 Life 250 Helper 2 Life 550

  17. When Fighters Meet Other Agents Fighter 1

  18. Quit stealing my mates and examples

  19. No, you stop!

  20. Lightning Bolt

  21. AH HA HA HA! POW!

  22. When Fighters Meet Wise Helpers Fighter 1 Life 800 Helper 1 Life 900

  23. Must… fight!

  24. Hello! How are you? That agent is too cute to bully…

  25. Get over here!

  26. AHHHHH! Zip!

  27. Whoosh

  28. Smooch!

  29. Several Cycles Later….. Helper 1 Life 650 Fighter 2 Baby Life 500 Fighter 1 Life 550

  30. Learning Algorithm • Agents move around the board learning from examples and each other. • The more successful agents will breed more readily, while the less successful agents will be killed by starvation and combat.

  31. Performance • Seemed to learn simple tasks such as playTennis and mushroom. - classified 80% of the playTennis set correctly - classified 92% of mushroom test set correctly . This is because it “learned” to classify everything as edible. • Performed dismally on other tasks such as voting. • Varying parameters might improve stability and speedup convergence.

  32. Problems • Population control (doomsday and extinction, memory issues). • Too much interaction between agents, not enough eating! • Disturbing bug (fixed): agents would have a child and then immediately mate with it!

  33. Conclusions • Hard to control/predict a dynamic population of agents acting independently. • Nondeterministic: different results every time, although similar trends (above). • With lots of work, we think this could work very well.

  34. Sample log file . . . (Fighter3,9999692) at [12,3] ate (person161,99) at [12,3] (Fighter3,10499691) at [12,3] ate (person161,98) at [12,3] (Fighter0,14499692) at [20,1] is breeding with (Helper0,8999691) at [20,1]! (Fighter0,14499692) at [20,1] begat (Fighter20,500000) at [20,1]! (Fighter20,499999) at [19,1] beat up (Fighter19,499987) at [19,1]! (Fighter19,499987) at [19,1] is dying! (Fighter0,14499686) at [20,2] is breeding with (Helper0,8999685) at [20,2]! (Fighter0,14499686) at [20,2] begat (Fighter21,500000) at [20,2]! (Fighter21,500000) at [20,2] got beat up by (Fighter0,14499685) at [20,2] (Fighter21,500000) at [20,2] is dying! (Helper1,13999675) at [18,0] ate (person77,98) at [18,0] (Fighter0,14499676) at [21,4] is breeding with (Helper0,8999675) at [21,4]! (Fighter0,14499676) at [21,4] begat (Fighter22,500000) at [21,4]! (Fighter3,10999672) at [18,0] ate (person77,97) at [18,0] (Helper2,9499670) at [5,16] ate (person112,99) at [5,16] (Fighter3,11499670) at [18,24] ate (person52,95) at [18,24] (Helper2,9999665) at [6,17] ate (person130,100) at [6,17] . . .

  35. Sample output Time limit reached: terminating. The following 42 rules were learned from the training set: Helper1, RANK 2670: IF ((handicappedkids=y) OR (handicappedkids=n)) AND ... AND ((export_act_safrica=y)) THEN Democrat . . .

  36. Questions… ???

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