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Classifier Classifier Systems Systems Christian Jacob Christian Jacob jacob@cpsc.ucalgary.ca Department of Computer Science University of Calgary

  1. Classifier Classifier Systems Systems —————————————— —————————————— Christian Jacob Christian Jacob jacob@cpsc.ucalgary.ca Department of Computer Science University of Calgary

  2. Cellular Automata Swarm Systems Random Boolean Networks Classifier Systems 2

  3. Classifier Systems Systems Classifier J. Holland (1975) J. Holland (1975) Learning syntactically simple simple Learning syntactically string rules ( (classifiers classifiers) to ) to guide guide string rules performance in an in an arbitrary arbitrary performance environment environment

  4. Objective: A Formal Framework for an Objective: A Formal Framework for an Operon-Operator Gene Regulation Model Operon -Operator Gene Regulation Model (Britten ( Britten-Davidson) -Davidson) 4 J. Holland: Adaptation in Natural and Artificial Systems

  5. First a Simple Example ... First a Simple Example ... F A classifier system to emulate a frog. A classifier system to emulate a frog. F The frog reacts to objects it sees. The frog reacts to objects it sees. Input: Output: On the Moving Large Far Striped Flee! Pursue! Ground 1 _ _ _ _ 1 0 1 0 0 0 _ 0 1 1 0 0 0 1 0 0

  6. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal _ 1 1 _ 101 Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x

  7. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal _ 1 0 1 1 1 _ Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x

  8. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal 1 1 1 _ 1 0 0 0 1 _ 111 Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x

  9. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal 0 0 0 _ 1 0 0 1 1 _ Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x

  10. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal _ 0 0 0 1 1 _ Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x

  11. Classifier System in Action Classifier System in Action Environ- Detectors Message List Effectors mental 1 1 Action Signal _ 0 0 0 1 1 _ Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x How can we adapt this rule set?

  12. Learning CS Architecture Learning CS Architecture Environ- Detectors Message List Effectors mental 1 1 0 1 1 Action Signal _ 0 0 0 1 1 1 1 1 _ 101 Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x Genetic Algorithm

  13. Genetic Algorithms Genetic Algorithms J. Holland (1975) J. Holland (1975) D. Goldberg (1989) D. Goldberg (1989) Simulated Genome- -based based Simulated Genome Evolution Evolution

  14. Genetic Algorithms Genetic Algorithms Representation of individuals Representation of individuals Binary vector Binary vector {1,0,1,1,0,1,0,0,1,0,1,1} {0,1,1,1,1,0,0,1,0,0,0,1} decoding {0,0,1,1,0,101,1,0,1,0,0} ... {1,1,0,0,0,1,0,1,0,1,0,0} ... {1,0,1,0,0,1,1,1,0,1,1,1} interpretation {0,0,1,1,0,1,1,1,0,1,0,0} {1,0,0,1,0,1,1,1,0,0,0,1}

  15. Ind. 1 Ind. 1 {1,0,1,1,0,1,0,0,1,0,1,1} Ind. 3 Ind. 3 {0,1,1,1,1,0,0,1,0,0,0,1} Ind. 5 Ind. 5 {1,1,0,0,0,1,0,1,0,1,0,0} Ind. 7 Ind. 7 ... evaluation selection selection {1,0,1,0,0,1,1,1,0,1,1,1} {0,0,1,1,0,1,1,1,0,1,0,0} Ind. 38 Ind. 38 {1,0,0,1,0,1,1,1,0,0,0,1} Ind. 40 Ind. 40 0 2 4 6 {0,0,1,1,0,1,1,1,0,1,0,0} {1,1,0,0,0,1,0,1,0,1,0,0} mutation mutation {0,1,1,1,0,0,1,1,0,1,1,0} {1,1,1,1,0,1,0,1,0,0,0,0} crossover crossover {1,0,1,1,0,1,0,0,1,0,1,1} {0,1,1,1,1,0,0,1,0,0,0,1} {1,1,0,0,0,1,0,1,0,1,0,0} {1,1,1,1,0,0,1,1,0,1,1,0} {0,1,1,1,0,1,0,1,0,0,0,0} ... interpretation {1,0,1,0,0,1,1,1,0,1,1,1} {0,0,1,1,0,1,1,1,0,1,0,0} {1,0,0,1,0,1,1,1,0,0,0,1}

  16. Learning CS Architecture Learning CS Architecture Environ- Detectors Message List Effectors mental 1 1 0 1 1 Action Signal _ 0 0 0 1 1 1 1 1 _ 101 Classifiers 1 0 _ : 1 1 1 0 0 _ : 0 0 0 1 x_ 1 : 0 0 x Genetic Algorithm

  17. How do Classifiers Receive How do Classifiers Receive their Fitnesses? ? their Fitnesses Apportionment of Credit Apportionment of Credit through through Bucket Brigades Bucket Brigades

  18. Bucket Brigade Algorithm Bucket Brigade Algorithm Index Rule Fitness Triggering Bid Message Rule _______________________________________________________ 1 0 1 _ _ : 0000 200 0 20 0000 2 0 0 _ 0 : 1100 200 1 3 1 1 _ _ : 1000 200 4 _ _ 0 0 : 0001 200 ––––––––––––––––––––––––––––––––––––––––––––––––––––––– 1 0 1 _ _ : 0000 180 2 0 0 _ 0 : 1100 200 1 20 1100 2 3 1 1 _ _ : 1000 200 4 _ _ 0 0 : 0001 200 1 20 0001 –––––––––––––––––––––––––––––––––––––––––––––––––––––––

  19. Bucket Brigade Algorithm Bucket Brigade Algorithm Index Rule Fitness Triggering Bid Message Rule _______________________________________________________ 1 0 1 _ _ : 0000 180 2 0 0 _ 0 : 1100 200 1 20 1100 2 3 1 1 _ _ : 1000 200 4 _ _ 0 0 : 0001 200 1 20 0001 ––––––––––––––––––––––––––––––––––––––––––––––––––––––– 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 180 3 3 1 1 _ _ : 1000 200 2 20 1000 4 _ _ 0 0 : 0001 180 2 18 0001 –––––––––––––––––––––––––––––––––––––––––––––––––––––––

  20. Bucket Brigade Algorithm Bucket Brigade Algorithm Index Rule Fitness Triggering Bid Message Rule _______________________________________________________ 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 180 3 3 1 1 _ _ : 1000 200 2 20 1000 4 _ _ 0 0 : 0001 180 2 18 0001 ––––––––––––––––––––––––––––––––––––––––––––––––––––––– 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 218 4 3 1 1 _ _ : 1000 180 4 _ _ 0 0 : 0001 162 3 16 0001 –––––––––––––––––––––––––––––––––––––––––––––––––––––––

  21. Bucket Brigade Algorithm Bucket Brigade Algorithm Index Rule Fitness Triggering Bid Message Rule _______________________________________________________ 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 218 4 3 1 1 _ _ : 1000 180 4 _ _ 0 0 : 0001 162 3 16 0001 ––––––––––––––––––––––––––––––––––––––––––––––––––––––– 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 218 5 3 1 1 _ _ : 1000 196 4 _ _ 0 0 : 0001 146 –––––––––––––––––––––––––––––––––––––––––––––––––––––––

  22. Bucket Brigade Algorithm Bucket Brigade Algorithm Index Rule Fitness Triggering Bid Message Rule _______________________________________________________ 1 0 1 _ _ : 0000 220 2 0 0 _ 0 : 1100 218 4 3 1 1 _ _ : 1000 180 4 _ _ 0 0 : 0001 162 3 16 0001 ––––––––––––––––––––––––––––––––––––––––––––––––––––––– 1 0 1 _ _ : 0000 220 Here are the 2 0 0 _ 0 : 1100 218 5 3 1 1 _ _ : 1000 196 fitnesses 4 _ _ 0 0 : 0001 146 –––––––––––––––––––––––––––––––––––––––––––––––––––––––

  23. The Broadcast Language The Broadcast Language J. Holland (1975) J. Holland (1975) A Formal Framework for Modeling Framework for Modeling A Formal Evolvable Gene Regulation Gene Regulation Evolvable Networks Networks

  24. Backing up again: A Formal Framework for an Backing up again: A Formal Framework for an Operon-Operator Gene Regulation Model Operon -Operator Gene Regulation Model (Britten ( Britten-Davidson) -Davidson) 48 J. Holland: Adaptation in Natural and Artificial Systems

  25. Broadcast Units Broadcast Units F BC[ BC[ S S 1 , S S 2 , S S 3 , S S 4 ] 1 , 2 , 3 , 4 ] F If at time at time t t a signal of type a signal of type S S 1 is present If 1 is present and no signal of type no signal of type S S 2 is present, and 2 is present, then at time at time t t +1 +1 then the signal S S 3 is broadcast the signal 3 is broadcast and the signal the signal S S 4 is deleted at time t t . . and 4 is deleted at time

  26. Gene Regulation with BC Units Gene Regulation with BC Units F Sensor-integrator gene complex Sensor-integrator gene complex SI SI 1 I 2 I 3 : 1 I 2 I 3 : F BC[ S S , _, { , _, { I I 1 , I I 2 , I I 3 }, _] BC[ 1 , 2 , 3 }, _] F Receptor-producer complex Receptor-producer complex R R 1 R 2 P : : 1 R 2 P F BC[{ R R 1 , R R 2 }, _, P P , _] , _] BC[{ 1 , 2 }, _,

  27. Broadcast Language Broadcast Language Example Example

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