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Overview Overview of of Evolutionary Systems Systems Evolutionary Christian Jacob AI AI Department of Computer Science University of Calgary CPSC 565 Winter 2003 Emergent Computing CPSC 565 Winter 2003 1 Christian Jacob,

  1. Overview Overview of of Evolutionary Systems Systems Evolutionary Christian Jacob AI AI Department of Computer Science University of Calgary CPSC 565 — Winter 2003 Emergent Computing — CPSC 565 — Winter 2003 1 Christian Jacob, University of Calgary

  2. In Search for Better “ “Solutions Solutions” ” In Search for Better global maximum local maxima local maxima Emergent Computing — CPSC 565 — Winter 2003 2 Christian Jacob, University of Calgary

  3. Evolutionary Optimization Evolutionary Optimization • Knowledge Reservoir Set of possible solutions – Gleaning a reservoir of knowledge from interactions with the environment. • Selection Fitness-dependent number of offspring – The sieve of selection culls out incorrect / unuseful “knowledge”. • Variation Variations of individual solutions – The learning system invents new variants of its old ideas that are tested against environmental demands. Emergent Computing — CPSC 565 — Winter 2003 3 Christian Jacob, University of Calgary

  4. Genetic Algorithms in Action … … Genetic Algorithms in Action Simulated Genome-Inspired Evolution J. Holland (1975), D. Goldberg (1989) Emergent Computing — CPSC 565 — Winter 2003 4 Christian Jacob, University of Calgary

  5. Dualism in Nature in Nature Dualism Genotype Phenotype Genotype Phenotype Transcription Translation Development Morphogenesis Emergent Computing — CPSC 565 — Winter 2003 5 Christian Jacob, University of Calgary

  6. Sidenote: DNA Is Structured Hierarchically Sidenote: DNA Is Structured Hierarchically Levels of Structure • Double Helix • Histones / Nucleosomes • Solenoid Supercoil • Chromatin • Chromosomes Emergent Computing — CPSC 565 — Winter 2003 6 Christian Jacob, University of Calgary

  7. Evolutionary Computing— —Geno- & Phenotype? Geno- & Phenotype? Evolutionary Computing E Population of organisms Phenotypical feature and behaviour space Gene pool ... S ... Genotypical structure space General genotype-phenotype distinction in evolutionary algorithms Emergent Computing — CPSC 565 — Winter 2003 7 Christian Jacob, University of Calgary

  8. Evolution: Adaptation of Structures Evolution: Adaptation of Structures Environment p (t) E p Structures I E (t) 1 s(t+1) s(t) w (t) ... S 2 5 Environmental signals s(t) a m E (t) w (t) 4 j E (p(s(t))) Adaptive plan 3 (1) Expression, (2) Interaction with the environment, (3) Evaluation, (4) Selection, (5) Variation. Emergent Computing — CPSC 565 — Winter 2003 8 Christian Jacob, University of Calgary

  9. Evolution: Adaptation of Structures Evolution: Adaptation of Structures Environment p (t+2) p (t+1) p (t) E Structures p I E (t) 1 s(t+1) s(t+2) ... s(t) w (t) w (t+1) ... S 2 5 Environmental signals s(t) a m E (t) w (t) 4 j E (p(s(t))) Adaptive plan 3 (1) Expression, (2) Interaction with the environment, (3) Evaluation, (4) Selection, (5) Variation. Emergent Computing — CPSC 565 — Winter 2003 9 Christian Jacob, University of Calgary

  10. Examples of Simple Simple Examples of Evolutionary Processes Evolutionary Processes Cumulative Selection Cumulative Selection Evolutionary Creativity Evolutionary Creativity Emergent Computing — CPSC 565 — Winter 2003 10 Christian Jacob, University of Calgary

  11. Drip by Drip— —Cumulative Selection Cumulative Selection Drip by Drip • A simplified version of the evolutionary principle of adaptation is used to search for a predefined string – starting from an initially random sequence of characters and – Using iterated mutation and cumulative selection. • Random strings are compared to an objective sentence: EVOLUTION OF STRUCTURE, STEP BY STEP (O) (a) ,LPYJK,ZPBGXWKTEKSQ,KLVCFZSJFGVZQWG ETTLXTKOL RF STRZGPURE CSYEPYBY SQEP (b) EVOLUDION OF STRUKTURE STEP BZ,STEB (c) Emergent Computing — CPSC 565 — Winter 2003 11 Christian Jacob, University of Calgary

  12. Algorithm for Selection and Mutation Algorithm for Selection and Mutation 1. Initialization: Generate an initial set S = {s 1 ,…,s n } of n individuals. 2. Initial evaluation: Evaluate all individuals and calculate their fitnesses (using Hamming distance). 3. Selection: Choose the best individual s best Œ S . 4. Mutation: From the best individual, generate a set of n -1 mutants: M = {s i ’ := mut(s best ) | i = 1 ,…,n -1} . 5. Evaluation: Evaluate all mutants and calculate their fitnesses. 6. Termination check: If at least one of the individuals has achieved the maximum fitness, STOP. Otherwise, generate a new selection set: S = {s best } » M. 7. Continue with step 3. Emergent Computing — CPSC 565 — Winter 2003 12 Christian Jacob, University of Calgary

  13. Mutation on Strings Mutation on Strings • We define string mutation on a string s = s 1 …s N as follows: mut(s, r, p) = s 1 ’…s N ’ where s i ’ = s i , if c real (0,1) > p. s i ’ = m(s i , r), otherwise. m( x , r) = t -1 ( t ( x ) + c int (-r, r)). • c (y, z) returns a uniformly distributed, random number from the interval [y, z]. • The character x is translated into its number encoding t ( x ). Emergent Computing — CPSC 565 — Winter 2003 13 Christian Jacob, University of Calgary

  14. String Mutations String Mutations s: EVOLUTION OF STRUCTURE, STEP BY STEP EVOLUTION OF STRUCTURE, STEP BY STEP s: mut(s, 1, 0.1) mut (s,1,0.2): EVNLVTION OF SURUCTURE, STEP BY STEP DVOLUTIONZOF STRUDSUQE, SSEP,CY SSEP : EVOLUTION OF STRUCTURE, STEP BY STEP s: EVOLUTION OF STRUCTURE, STEP BY STEP s: mut(s, 1, 0.2) mut (s,2,0.2): EVOLUTIOM OF STRVCTURE. STEP BZ STEP FVOLUTIONYOF STTUCTURE, QTEP BY STEP : EVOLUTION OF STRUCTURE, STEP BY STEP s: EVOLUTION OF STRUCTURE, STEP BY STEP s: mut(s, 1, 0.5) mut (s,5,0.2): EWNLVUHON,OE SSSUCUVRD.ZSUEP,CY,STEQ EVOLUTNON OFCOTRYFTUME, STEPBB STFP : • Mutation on strings with • Mutation on strings with a mutation radius 1 and different constant mutation rate of 0.2 mutation rates. and varying mutation radii. Emergent Computing — CPSC 565 — Winter 2003 14 Christian Jacob, University of Calgary

  15. String Evolution Examples String Evolution Examples Mutation radius: 1; mutation rate: 0.1 Mutation radius: 1; mutation rate: 0.5 Mutation radius: 5; mutation rate: 0.1 Mutation radius: 5; mutation rate: 0.5 Emergent Computing — CPSC 565 — Winter 2003 15 Christian Jacob, University of Calgary

  16. String Evolution— — Mut String Evolution Mut. Radius: 2, . Radius: 2, Mut Mut. Rate: 0.1 . Rate: 0.1 Emergent Computing — CPSC 565 — Winter 2003 16 Christian Jacob, University of Calgary

  17. String Evolution— —Hamming Distance Plots Hamming Distance Plots String Evolution Mutation radius: 2 Mutation rate: 0.1 Emergent Computing — CPSC 565 — Winter 2003 17 Christian Jacob, University of Calgary

  18. String Evolution— —Hamming Distance Plots (2) Hamming Distance Plots (2) String Evolution Mutation radius: 4 Mutation rate: 0.1 Emergent Computing — CPSC 565 — Winter 2003 18 Christian Jacob, University of Calgary

  19. String Evolution— —Hamming Distance Plots (3) Hamming Distance Plots (3) String Evolution Mutation radius: 2 Mutation rate: 0.2 Emergent Computing — CPSC 565 — Winter 2003 19 Christian Jacob, University of Calgary

  20. String Evolution — — Comparing Results Comparing Results String Evolution Mutation radius: 2 Mutation radius: 4 Mutation radius: 2 Mutation rate: 0.1 Mutation rate: 0.1 Mutation rate: 0.2 Emergent Computing — CPSC 565 — Winter 2003 20 Christian Jacob, University of Calgary

  21. Examples of Simple Simple Examples of Evolutionary Processes Evolutionary Processes Cumulative Selection Cumulative Selection Evolutionary Creativity Evolutionary Creativity Emergent Computing — CPSC 565 — Winter 2003 21 Christian Jacob, University of Calgary

  22. A Biomorph Biomorph … … and Some of Its Mutants and Some of Its Mutants A 3 | 2 3 2 4 7 | 2 2 2 4 7 | 2 2 2 4 7 | 2 2 2 4 7 Emergent Computing — CPSC 565 — Winter 2003 22 Christian Jacob, University of Calgary

  23. A Biomorph Biomorph … … and Some of Its Mutants and Some of Its Mutants A 3 | 2 3 2 4 7 | 2 2 2 4 7 | 2 2 2 4 7 | 2 2 2 4 7 1+ 2+ 3+ 7+ 12+ 13+ 17+ 1- 2- 7- 8- 13- Emergent Computing — CPSC 565 — Winter 2003 23 Christian Jacob, University of Calgary

  24. Evolution of Biomorphs Biomorphs Evolution of Emergent Computing — CPSC 565 — Winter 2003 24 Christian Jacob, University of Calgary

  25. Evolution of Biomorphs Biomorphs Evolution of Emergent Computing — CPSC 565 — Winter 2003 25 Christian Jacob, University of Calgary

  26. Evolved Biomorphs Biomorphs Evolved Gen. 0 Gen. 5 Gen. 10 Gen. 19 Emergent Computing — CPSC 565 — Winter 2003 26 Christian Jacob, University of Calgary

  27. Evolved 3D Biomorphs Biomorphs Evolved 3D Emergent Computing — CPSC 565 — Winter 2003 27 Christian Jacob, University of Calgary

  28. Biomorph Examples Biomorph Examples Emergent Computing — CPSC 565 — Winter 2003 28 Christian Jacob, University of Calgary

  29. ArtFlower Examples ArtFlower Examples Emergent Computing — CPSC 565 — Winter 2003 29 Christian Jacob, University of Calgary

  30. General Evolutionary Algorithm Scheme General Evolutionary Algorithm Scheme Emergent Computing — CPSC 565 — Winter 2003 30 Christian Jacob, University of Calgary

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