1 / 36

Genetic Programming Primer ami hauptman

Genetic Programming Primer ami hauptman. Outline. Intro GP definition Human Competitive Results Representation Advantages GP operators The GP Individual The GP experiment Example – symbolic regression Additional Concepts. Intro. Representation is a crucial issue in problem-solving

tuyen
Download Presentation

Genetic Programming Primer ami hauptman

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Genetic Programming Primerami hauptman

  2. Outline • Intro • GP definition • Human Competitive Results • Representation • Advantages • GP operators • The GP Individual • The GP experiment • Example – symbolic regression • Additional Concepts

  3. Intro • Representation is a crucial issue in problem-solving • Part of the solution • Generic GAs • Linear (bit-vector) solutions • Fixed length • Problems: • Often difficult and unnatural • Limited search; Length may be unknown • Examples – TSP; AI; … • Very few “real world” implementations

  4. Towards a Variant • A : Computer programs fundamental models in CS • B : “Computer programs are the best model for computer programs” • Conclusion… (AΛB) • Why not implement GA individuals as programs? • Implemented in…

  5. Genetic Programming [GP] • Evolutionary search in program space • Tree-structured representation • Non tree-based program representations exist (e.g. Machine-Code GA) • Introduced by Cramer [1985] • Developed by Koza [1992] • Active and highly successful field • Conferences • Patents • And especially…

  6. Human-Competitive Results

  7. Human-Competitive Results • Increasing in the field of genetic and evolutionary computation • An automatically created result is “human-competitive” if (www.human-competitive.org): • Patent (invention) • New scientific result • Solution to long-standing problem • Wins / Holds its own in regulated competition with human contestant (= live player or human-written program)

  8. Human-Competitive Results • Over 40 to date • Examples: • Evolved antenna for use by NASA (Lohn, 2004) • Automatic quantum computer programming (Spector, 2004) • Several analog electronic circuits (Koza et al., mid 90s till today): amplifiers, computational circuits, … • MAJOR motivation: As of 2004, yearly contest • WITH CASH PRIZES!

  9. Outline • Intro • GP definition • Human Competitive Results • Representation • Advantages • GP operators • The GP Individual • The GP experiment • Example – symbolic regression • Additional Concepts

  10. Representations • Individuals as LISP programs (S-expressions) • Why trees?

  11. Advantages of trees • Before Genetic operators • Powerful representation • (+ 1 2 (IF (> TIME 10) 3 4)) • Simple • No: local vars, types … • Individual  Solution • Saves precious time • Recursive - size may vary • Main advantage:

  12. Outline • Intro • GP definition • Human Competitive Results • Representation • Advantages • GP operators • The GP Individual • The GP experiment • Example – symbolic regression • Additional Concepts

  13. Genetic Operators • Reproduction – as before • Crossover, Mutation - • now tree operators • S-expressions closed under most (tree) operators • Generate VALID individuals • Unlike C programs…

  14. GP Operators - Crossover • BINARY 1) Randomly select 2 nodes 2) Swap underlying sub-trees • S.t. depth constraints (if applicable) FATHERMOTHER OFFSPRING1 OFFSPRING2

  15. GP Operators - Mutation • UNARY 1) Select mutation point 2) Remove entire sub-tree and grow a new one • Again – depth constraints

  16. Genome • What’s inside the tree… • Genome contains functions and terminals • Functions - Internal nodes • Varying complexity • Examples: +, -, And, Or, If, IFLTE, ADFs • Terminals - Leaves • Constants or “sensors”; Actions • Generally more domain dependant • Examples: Variables, Constants, 0-params functions, ERCs

  17. Example 1 • Logic expression AND OR X NOT AND Y Z X

  18. Example 2 • Series of instructions Progn2 IF Rotate_Right Wall_is_near Advance_5 Advance_10 90

  19. More realistic trees… • GP-EndChess Tree 0: (If3 (Or2 (Not (Or2 (And2 OppPieceAttUnprotected NotMyKingInCheck) (Or2 NotMyPieceAttUnprotected 100*Increase))) (And2 (Or3 (And2 OppKingStuck NotMyPieceAttUnprotected) (And2 OppPieceAttUnprotected OppKingStuck) (And3 -1000*MateInOne OppKingInCheckPieceBehind NotMyKingStuck)) (Or2 (Not NotMyKingStuck) OppKingInCheck))) NumMyPiecesUNATT (If3 (< (If3 (Or2 NotMyPieceAttUnprotected NotMyKingInCheck) (If3 NotMyPieceAttUnprotected #NotMovesOppKing OppKingInCheckPieceBehind) (If3 OppKingStuck OppKingInCheckPieceBehind -1000*MateInOne)) (If3 (And2 100*Increase 1000*Mate?) (If3 (< NumMyPiecesUNATT (If3 NotMyPieceAttUnprotected -1000*MateInOne OppKingProxEdges)) (If3 (< MyKingDistEdges #NotMovesOppKing) (If3 -1000*MateInOne -1000*MateInOne NotMyPieceATT) (If3 100*Increase #MovesMyKing OppKingInCheckPieceBehind)) NumOppPiecesATT) (If3 NotMyKingStuck -100.0 OppKingProxEdges))) (If3 OppKingInCheck (If3 (Or2 NotMyPieceAttUnprotected NotMyKingInCheck) (If3 (< MyKingDistEdges #NotMovesOppKing) (If3 -1000*MateInOne -1000*MateInOne NotMyPieceATT) (If3 100*Increase #MovesMyKing OppKingInCheckPieceBehind)) NumOppPiecesATT) (If3 (And3 -1000*MateInOne NotMyPieceAttUnprotected 100*Increase) (If3 (< NumMyPiecesUNATT (If3 NotMyPieceAttUnprotected -1000*MateInOne OppKingProxEdges)) (If3 (< MyKingDistEdges #NotMovesOppKing) (If3 -1000*MateInOne -1000*MateInOne NotMyPieceATT) (If3 100*Increase #MovesMyKing OppKingInCheckPieceBehind)) NumOppPiecesATT) -1000*MateInOne)) (If3 (< (If3 100*Increase MyKingDistEdges 100*Increase) (If3 OppKingStuck OppKingInCheckPieceBehind -1000*MateInOne)) -100.0 (If3 (And2 NotMyPieceAttUnprotected -1000*MateInOne) (If3 (< NumMyPiecesUNATT (If3 NotMyPieceAttUnprotected -1000*MateInOne OppKingProxEdges)) (If3 (< MyKingDistEdges #NotMovesOppKing) (If3 -1000*MateInOne -1000*MateInOne NotMyPieceATT) (If3 100*Increase #MovesMyKing OppKingInCheckPieceBehind)) NumOppPiecesATT) (If3 OppPieceAttUnprotected NumMyPiecesUNATT MyFork)))))

  20. Outline • Intro • GP definition • Human Competitive Results • Representation • Advantages • GP operators • The GP Individual • The GP experiment • Example – symbolic regression • Additional Concepts

  21. The GP experiment • Preparatory steps: • Determine set of terminals & functions • Determine fitness measure • Determine run parameters • Population size • Operator probabilities • Tree depths • … • Set up end of run criteria • Run the experiment • start with initial population:

  22. Creating the Initial Population • 3 Methods • Depth=m • Grow • F.e. node – randomly select function or terminal •  varying depths • Full • All of same depth • Ramped-half-and-half • Divide population to max_depth-1 parts • For each part: half by grow; half by full • Preferred by Koza

  23. Flowchart of GP

  24. Example - Symbolic regression

  25. Preparatory Steps

  26. Notes • Complexity of function = ? • Size of solution unknown • More points  more difficult • Depth constraints • Can limit complexity • Needed anyway • Typically size of pop >= 50 • “%” function • is “/” excluding zero in 2nd argument • Needed to avoid BAD individuals

  27. Generation 0 • 4 Random Individuals (later: creation) • More complex than actual • transformed

  28. Fitness x + 1 x2 + 1 2 x 4.4 6.00 9.48 15.4 • f(x) = x2 +x+ 1

  29. Generation 1 First offspring of crossover of (a) and (b)  picking “+” of parent (a) and left-most “x” of parent (b) as crossover points Second offspringof crossover of (a) and (b)  picking “+” of parent (a) and left-most “x” of parent (b) as crossover points Mutant of (c) picking “2” as mutation point Copy of (a)

  30. Generation 1 - Fitness 15.4 0 - IDEAL 6.0 4.4

  31. Outline • Intro • GP definition • Human Competitive Results • Representation • Advantages • GP operators • The GP Individual • GP experiments • Example – symbolic regression • Additional Concepts

  32. Additional Concepts • Competitive Evaluation • Ephemeral Random constants (ERCs) • Strongly Typed GP (STGP)

  33. Competitive Evaluation • (not only in GP) • Important in games ! • Fitness depends on peers; relative • E.g. compete against k peers • Optional – count score for both • Easier progress at start – weak opps • Avoid over-generalization and early convergence • Still need absolute measure

  34. ERCs • Sometimes constants not known in advance • E.g. symbolic regression • ERC (terminal) node – • init to a random constant • stay • Mutate-ERC operator (low prob.) • Change to other constant • Typically-range for constants

  35. Strongly Typed Genetic Programming (STGP) • Montana [1995] • Node data types may vary; casting not enough • Impose structural constraints • Assigning (pseudo) types to functions & terminals • Sometimes more than one • Typically actual (implementation) types are all real numbers

  36. STGP Example • Include both Boolean and Float terminals • True, False, zero-arg Predicates • ERCs, float zero-arg functions • Some used as both – “Query” • Important predicate • Returns a large float if true • If function • Children: Boolean, Float, Float • Query can be at each child location of If

More Related