1. GENETIC ALGORITHMS AND GENETIC PROGRAMMING

2. John R. Koza Consulting Professor (Medical Informatics) Department of Medicine School of Medicine Consulting Professor Department of Electrical Engineering School of Engineering Stanford University Stanford, California 94305 koza@stanford.edu http://www.smi.stanford.edu/people/koza/

3. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

4. GENETIC ALGORITHM (GA) Generation 0Generation 1 IndividualsFitnessOffspring 011$3111 001$1010 110$6110 010$2010

5. HAMBURGER RESTAURANT PROBLEM Price 1 = $ 0.50 price 0 = $10.00 price Drink 1 = Coca Cola 0 = Wine Ambiance 1 = Fast snappy service 0 = Leisurely service with tuxedoed waiter

6. CHROMOSOME (GENOME) OF THE GLOBAL OPTIMUM McDONALD's 111

7. THE SEARCH SPACE Alphabet size K=2, Length L=3 Size of search space: K L =2 L =2 3 =8 1000 2001 3010 4011 5100 6101 7110 8111

8. IMPRACTICALITY OF RANDOM OR ENUMERATIVE SEARCH 81-bit problems are very small for GA However, even if L is as small as 81, 2 81 ~ 10 27 = number of nanoseconds since the beginning of the universe 15 billion years ago

9. GA FLOWCHART

10. GENERATION 0 Generation 0 10113 20011 31106 40102 Total Worst Average Best

11. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

12. PROBABILISTIC SELECTION BASED ON FITNESS Better individuals are preferred Best is not always picked Worst is not necessarily excluded Nothing is guaranteed Mixture of greedy exploitation and adventurous exploration Similarities to simulated annealing (SA)

13. PROBABILISTIC SELECTION BASED ON FITNESS

14. DARWINIAN FITNESS PROPORTIONATE SELECTION Generation 0Mating pool 10113.250113 20011.081106 3 6.501106 40102.170102 Total1217 Worst12 Average3.004.5 Best66

15. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

16. MUTATION OPERATION Parent chosen probabilistically based on fitness Mutation point chosen at random One offspring Parent 010 Parent --0 Offspring 011

17. AFTER MUTATION OPERATION Generation 0Mating poolGeneration 1 10113.250113 20011.081106 3 6.501106 40102.170102---0113 Total1217 Worst12 Average3.004.5 Best66

18. CROSSOVER OPERATION 2 parents chosen probabilistically based on fitness Parent 1Parent 2 011110

19. CROSSOVER (CONTINUED) Interstitial point picked at random 2 remainders 2 offspring produced by crossover Remainder 1Remainder 2 - - 1- - 0 Offspring 1Offspring 2 111010 Fragment 1Fragment 2 01-11-

20. AFTER CROSSOVER OPERATION Generation 0Mating poolGeneration 1 10113.25011321117 20011.08110620102 31106.501106 40102.170102 Total1217 Worst12 Average3.004.5 Best66

21. AFTER REPRODUCTION OPERATION Generation 0Mating poolGeneration 1 10113.25 20011.08 31106.501106---1106 40102.17 Total1217 Worst12 Average3.004.5 Best66

22. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

23. GENERATION 1 Generation 0Mating poolGeneration 1 10113.25011321117 20011.08110620102 31106.501106---1106 40102.170102---0113 Total121718 Worst122 Average3.004.5 Best667

24. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

25. DEFINITION OF THE GENETIC ALGORITHM (GA) The genetic algorithm is a probabalistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation.

26. PROBABILISTIC STEPS The initial population is typically random Probabilistic selection based on fitness - Best is not always picked - Worst is not necessarily excluded Random picking of mutation and crossover points Often, there is probabilistic scenario as part of the fitness measure

27. ANTENNA DESIGN

28. The problem (Altshuler and Linden 1998) is to determine the x-y-z coordinates of the 3- dimensional position of the ends (X1, Y1, Z1, X2, Y2, Z2,…, X7, Y7, Z7) of 7 straight wires so that the resulting 7-wire antenna satisfies certain performance requirements The first wire starts at feed point (0, 0, 0) in the middle of the ground plane The antenna must fit inside the 0.5 cube

29. ANTENNA GENOME 105-bit chromosome (genome) Each x-y-z coordinate is represented by 5 bits (4-bit granularity for data plus a sign bit) Total chromosome is 3  7  5 = 105 bits X1X1 Y1Y1 Z1Z1 X2X2 Y2Y2 Z2Z2 … +0010-1110+0001+0011-1011+0011…

30. ANTENNA FITNESS Antenna is for ground-to-satellite communications for cars and handsets We desire near-uniform gain pattern 10  above the horizon Fitness is measured based on the antenna's radiation pattern. The radiation pattern is simulated by National Electromagnetics Code (NEC)

31. ANTENNA FITNESS Fitness is sum of the squares of the difference between the average gain and the antenna's gain Sum is taken for angles  between -90  and +90  and all azimuth angles  from 0  to 180  The smaller the value of fitness, the better

32. GRAPH OF ANTENNA FITNESS

33. U. S. PATENT 5,719,794

34. 10-MEMBER TRUSS

35. Prespecified topological arrangement of the 10 members, the load, and the wall (Goldberg and Samtani 1986) Truss has 10 members (6 are length of 30 feet and 4 are length 30√2 = 41 feet) The problem is to determine the cross-sectional areas (A1, …, A10) of each of the 10 members so as to minimize weight of the material for a truss that supports the 2 loads The weight is based on volume (i.e., cross- sectional area  length)

36. TRUSS GENOME 40-bit chromosome (genome) 4-bit granularity for truss diameters 0000 = smallest diameter 1111 = largest diameter Total chromosome is 4  10 = 40 bits A1A2A3A4A5A6A7A8A9A10 00101110000100111011001111110011 1010

37. TRUSS FITNESS Two-part (multiobjective) fitness measure –First, fitness is computed by taking the sum, over the 10 members, of the cross-sectional area of each member times the length of each member (30 feet or 30√2 = 41 feet). –Second, a penalty (up to 10%) is imposed for violating the stress constraints. Stresses are computed using standard mechanical engineering techniques. The smaller the total fitness, the better

38. CELLULAR AUTOMATA

39. STATE TRANSITION TABLE #WWWWWWXEEEEEERule 00000001a0a0 10000010a1a1 20000100a2a2 30000110a3a3 40011000a4a4 ……………………… 1271111111a 127

40. CELLULAR AUTOMATA 128-bit chromosome (genome) A0A0 A1A1 A2A2 …A 127 a0a0 a1a1 a2a2 …a 127

41. PROBLEM-SPECIFIC GENOMES N  M GENOME 1101111 1111011 1011100 1111111 1101111 1111011 1101110

42. GENETIC ALGORITHM USING VARIABLE-LENGTH STRINGS 5-WIRE ANTENNA (5  15 = 75 bits) 4-WIRE ANTENNA (4  15 = 60 bits) X1Y1Z1…X5Y5Z5 +0010-1110+0001…+0010-1110+0001 X1Y1Z1…X4Y4Z4 +1010-0110+1101…+1010-0110+1001

43. GENETIC PROGRAMMING

44. THE CHALLENGE "How can computers learn to solve problems without being explicitly programmed? In other words, how can computers be made to do what is needed to be done, without being told exactly how to do it?"  Attributed to Arthur Samuel (1959)

45. CRITERION FOR SUCCESS "The aim [is]... to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence.“  Arthur Samuel (1983)

46. REPRESENTATIONS Decision trees If-then production rules Horn clauses Neural nets Bayesian networks Frames Propositional logic Binary decision diagrams Formal grammars Coefficients for polynomials Reinforcement learning tables Conceptual clusters Classifier systems

47. A COMPUTER PROGRAM

48. GENETIC PROGRAMMING (GP) GP applies the approach of the genetic algorithm to the space of possible computer programs Computer programs are the lingua franca for expressing the solutions to a wide variety of problems A wide variety of seemingly different problems from many different fields can be reformulated as a search for a computer program to solve the problem.

49. GP  MAIN POINTS Genetic programming now routinely delivers high-return human-competitive machine intelligence. Genetic programming is an automated invention machine. Genetic programming has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time.

50. DEFINITION OF “HIGH- RETURN” The AI ratio (the “artificial-to-intelligence” ratio) of a problem-solving method as the ratio of that which is delivered by the automated operation of the artificial method to the amount of intelligence that is supplied by the human applying the method to a particular problem

51. DEFINITION OF “ROUTINE” A problem solving method is routine if it is general and relatively little human effort is required to get the method to successfully handle new problems within a particular domain and to successfully handle new problems from a different domain.

52. CRITERIA FOR “HUMAN-COMPETITIVENESS” Previously patented, an improvement over a patented invention, or patentable today Publishable in its own right as a new scientific result  independent of the fact that the result was mechanically created Holds it own in regulated competition against humans (or programs) 5 other similar criteria that are “arms-length” from the fields of AI, ML, GP

53. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

54. GP FLOWCHART

55. A COMPUTER PROGRAM IN C int foo (int time) { int temp1, temp2; if (time > 10) temp1 = 3; else temp1 = 4; temp2 = temp1 + 1 + 2; return (temp2); }

56. OUTPUT OF C PROGRAM TimeOutput 06 16 26 36 46 56 66 76 86 96 106 117 127

57. PROGRAM TREE (+ 1 2 (IF (> TIME 10) 3 4))

58. CREATING RANDOM PROGRAMS

59. Available functions F = { +, -, *, %, IFLTE } Available terminals T = { X, Y, Random-Constants } The random programs are: –Of different sizes and shapes –Syntactically valid –Executable

60. GP GENETIC OPERATIONS Reproduction Mutation Crossover (sexual recombination) Architecture-altering operations

61. MUTATION OPERATION

62. Select 1 parent probabilistically based on fitness Pick point from 1 to NUMBER-OF-POINTS Delete subtree at the picked point Grow new subtree at the mutation point in same way as generated trees for initial random population (generation 0) The result is a syntactically valid executable program Put the offspring into the next generation of the population

63. CROSSOVER OPERATION

64. Select 2 parents probabilistically based on fitness Randomly pick a number from 1 to NUMBER-OF- POINTS for 1 st parent Independently randomly pick a number for 2 nd parent The result is a syntactically valid executable program Put the offspring into the next generation of the population Identify the subtrees rooted at the two picked points

65. REPRODUCTION OPERATION Select parent probabilistically based on fitness Copy it (unchanged) into the next generation of the population

66. FIVE MAJOR PREPARATORY STEPS FOR GP Determining the set of terminals Determining the set of functions Determining the fitness measure Determining the parameters for the run Determining the method for designating a result and the criterion for terminating a run

67. ILLUSTRATIVE GP RUN

68. SYMBOLIC REGRESSION Independent variable X Dependent variable Y 1.00 -0.800.84 -0.600.76 -0.400.76 -0.200.84 0.001.00 0.201.24 0.401.56 0.601.96 0.802.44 1.003.00

69. PREPARATORY STEPS Objective:Find a computer program with one input (independent variable X ) whose output equals the given data 1Terminal set: T = {X, Random-Constants} 2Function set: F = {+, -, *, %} 3Fitness:The sum of the absolute value of the differences between the candidate program’s output and the given data (computed over numerous values of the independent variable x from –1.0 to +1.0) 4Parameters:Population size M = 4 5Termination:An individual emerges whose sum of absolute errors is less than 0.1

70. SYMBOLIC REGRESSION POPULATION OF 4 RANDOMLY CREATED INDIVIDUALS FOR GENERATION 0

71. SYMBOLIC REGRESSION x 2 + x + 1 FITNESS OF THE 4 INDIVIDUALS IN GEN 0 x + 1x 2 + 12x 0.671.001.702.67

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

73. CLASSIFICATION

74. GP TABLEAU – INTERTWINED SPIRALS Objective:Create a program to classify a given point in the x-y plane to the red or blue spiral 1Terminal set: T = {X,Y,Random-Constants} 2Function set: F = {+,-,*,%,IFLTE,SIN,COS} 3Fitness:The number of correctly classified points (0 – 194) 4Parameters:M = 10,000. G = 51 5Termination:An individual program scores 194

75. WALL-FOLLOWER

76. FITNESS

77. BEST OF GENERATION 57

78. BOX MOVER – BEST OF GEN 0

79. BOX MOVER GEN 45 – FITNESS CASE 1

80. TRUCK BACKER UPPER

81. 4-Dimensional control problem –horizontal position, x –vertical position, y –angle between trailer and horizontal,  t –angle between trailer and cab,  d One control variable (steering wheel turn angle) State transition equations map the 4 state variables into 1 output (the control variable) Simulation run over many initial conditions and over hundreds of time steps

82. GENETIC PROGRAMMING: ON THE PROGRAMMING OF COMPUTERS BY MEANS OF NATURAL SELECTION (Koza 1992)

83. 2 MAIN POINTS FROM 1992 BOOK Virtually all problems in artificial intelligence, machine learning, adaptive systems, and automated learning can be recast as a search for a computer program. Genetic programming provides a way to successfully conduct the search for a computer program in the space of computer programs.

84. SOME RESULTS FROM 1992 BOOK Intertwined Spirals Truck Backer Upper Broom Balancer Wall Follower Box Mover Artificial Ant Differential Games Inverse Kinematics Central Place Foraging Block Stacking Randomizer Cellular Automata Task Prioritization Image Compression Econometric Equation Optimization Boolean Function Learning Co-Evolution of Game- Playing Strategies

85. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

86. COMPUTER PROGRAMS Subroutines provide one way to REUSE code  possibly with different instantiations of the dummy variables (formal parameters) Loops (and iterations) provide a 2 nd way to REUSE code Recursion provide a 3 rd way to REUSE code Memory provides a 4 th way to REUSE the results of executing code

87. SYMBOLIC REGRESSION Fitness caseL0L0 W0W0 H0H0 L1L1 W1W1 H1H1 Dependent variable D 134725354 27109 31600 31094816312 4395164111 5432761-18 6331954-171 7599176363 8129392-36 92682610-24 1081 75145

88. EVOLVED SOLUTION (- (* (* W0 L0) H0) (* (* W1 L1) H1))

89. DIFFERENCE IN VOLUMES D = L 0 W 0 H 0 – L 1 W 1 H 1

90. AUTOMATICALLY DEFINED FUNCTION volume

91. (progn (defun volume (arg0 arg1 arg2) (values (* arg0 (* arg1 arg2)))) (values (- (volume L0 W0 H0) (volume L1 W1 H1))))

92. AUTOMATICALLY DEFINED FUNCTIONS ADFs provide a way to REUSE code Code is typically reused with different instantiations of the dummy variables (formal parameters)

93. ADDITION OF V 0 AND V 1 Fitness caseL0L0 W0W0 H0H0 L1L1 W1W1 H1H1 V0V0 V1V1 D 1347253843054 27109 3163030600 3109481636048312 439516413524111 54327612442-18 63319549180-171 759917640542363 81293921854-36 9268261096120-24 1081 751803545

94. DIVIDE AND CONQUER

95. Decompose a problem into sub-problems Solve the sub-problems Assemble the solutions of the sub- problems into a solution for the overall problem

96. CHANGE OF REPRESENTATION

97. Identify regularities Change the representation Solve the overall problem

98. ADF IMPLEMENTATION Each overall program in population includes –a main result-producing branch ( RPB ) and –function-defining branch (i.e., automatically defined function, ADF ) In generation 0, create random programs with different ingredients for the RPB and the ADF –Terminal set for ADF typically contains dummy arguments (formal parameters), such as ARG0, ARG1, … –Function set of the RPB contains ADF0 –ADF s are private and associated with a particular individual program in the population

99. ADF MUTATION Select parent probabilistically on the basis of fitness Pick a mutation point from either RPB or an ADF Delete sub-tree rooted at the picked point Grow a new sub-tree at the picked point composed of the allowable ingredients appropriate for the picked point The offspring is a syntactically valid executable program

100. ADF CROSSOVER Select parent probabilistically on the basis of fitness Pick a crossover point from either RPB or an ADF of the FIRST patent The choice of crossover point in the SECOND parent is RESTRICTED to the picked RPB or to the picked ADF The sub-trees are swapped The offspring are syntactically valid executable programs

101. GENETIC PROGRAMMING II: AUTOMATIC DISCOVERY OF REUSABLE PROGRAMS (Koza 1994)

102. MAIN POINTS OF 1994 BOOK Scalability is essential for solving non-trivial problems in artificial intelligence, machine learning, adaptive systems, and automated learning Scalability can be achieved by reuse Genetic programming provides a way to automatically discover and reuse subprograms in the course of automatically creating computer programs to solve problems

103. COMPUTER PROGRAMS Subroutines provide one way to REUSE code  possibly with different instantiations of the dummy variables (formal parameters) Loops (and iterations) provide a 2 nd way to REUSE code Recursion provide a 3 rd way to REUSE code Memory provides a 4 th way to REUSE the results of executing code

104. MEMORY Settable (named) variables Indexed vector memory Matrix memory Relational memory

105. LANGDON'S DATA STRUCTURES Stacks Queues Lists Rings

106. COMPUTER PROGRAMS Subroutines provide one way to REUSE code  possibly with different instantiations of the dummy variables (formal parameters) Loops (and iterations) provide a 2 nd way to REUSE code Recursion provide a 3 rd way to REUSE code Memory provides a 4 th way to REUSE the results of executing code

107. AUTOMATICALLY DEFINED ITERATION (ADI) The overall program includes an iteration- performing branch ( IPB ) in addition to a result-producing branch ( RPB ) and function- defining branches ( ADF ) There are no infinite loops because the iteration is performed over a known, fixed set –protein or DNA sequence (of varying length) –time-series data –two-dimensional array of pixels Memory is usually involved and is used to communicate between IPB, RPB, and ADF

108. TRANSMEMBRANE SEGMENT IDENTIFICATION PROBLEM Goal is to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein

109. TRANSMEMBRANE SEGMENT IDENTIFICATION PROBLEM (progn (defun ADF0 () (ORN (ORN (ORN (I?) (H?)) (ORN (P?) (G?))) (ORN (ORN (ORN (Y?) (N?)) (ORN (T?) (Q?))) (ORN (A?) (H?)))))) (defun ADF1 () (values (ORN (ORN (ORN (A?) (I?)) (ORN (L?) (W?))) (ORN (ORN (T?) (L?)) (ORN (T?) (W?)))))) (defun ADF2 () (values (ORN (ORN (ORN (ORN (ORN (D?) (E?)) (ORN (ORN (ORN (D?) (E?)) (ORN (ORN (T?) (W?)) (ORN (Q?) (D?)))) (ORN (K?) (P?)))) (ORN (K?) (P?))) (ORN (T?) (W?))) (ORN (ORN (E?) (A?)) (ORN (N?) (R?)))))) (progn (loop-over-residues (SETM0 (+ (- (ADF1) (ADF2)) (SETM3 M0)))) (values (% (% M3 M0) (% (% (% (- L -0.53) (* M0 M0)) (+ (% (% M3 M0) (% (+ M0 M3) (% M1 M2))) M2)) (% M3 M0))))))

110. TRANSMEMBRANE SEGMENT IDENTIFICATION PROBLEM in-sample correlation of 0.976 out-of-sample correlation of 0.968 out-of-sample error rate 1.6%

111. AUTOMATICALLY DEFINED LOOP (ADL) loop initialization branch, LIB loop condition branch, LCB loop body branch, LBB loop update branch, LUB

112. ADL

113. COMPUTER PROGRAMS Subroutines provide one way to REUSE code  possibly with different instantiations of the dummy variables (formal parameters) Loops (and iterations) provide a 2 nd way to REUSE code Recursion provide a 3 rd way to REUSE code Memory provides a 4 th way to REUSE the results of executing code

114. AUTOMATICALLY DEFINED RECURSION (ADR) recursion condition branch, RCB recursion body branch, RBB recursion update branch, RUB recursion ground branch, RGB

115. ADR

116. HUMAN-COMPETITIVE RESULTS (NOT RELATED TO PATENTS) Transmembrane segment identification problem for proteins Motifs for D–E–A–D box family and manganese superoxide dismutase family of proteins Cellular automata rule for Gacs-Kurdyumov-Levin (GKL) problem Quantum algorithm for the Deutsch-Jozsa “early promise” problem Quantum algorithm for Grover’s database search problem Quantum algorithm for the depth-two AND/OR query problem Quantum algorithm for the depth-one OR query problem Protocol for communicating information through a quantum gate Quantum dense coding Soccer-playing program that won its first two games in the 1997 Robo Cup competition Soccer-playing program that ranked in the middle of field in 1998 Robo Cup competition Antenna designed by NASA for use on spacecraft Sallen-Key filter

117. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

118. GENETIC PROGRAMMING III: DARWINIAN INVENTION AND PROBLEM SOLVING (Koza, Bennett, Andre, Keane 1999)

119. SUBROUTINE DUPLICATION

120. SUBROUTINE CREATION

121. SUBROUTINE DELETION

122. ARGUMENT DUPLICATION

123. ARGUMENT DELETION

124. 16 ATTRIBUTES OF A SYSTEM FOR AUTOMATICALLY CREATING COMPUTER PROGRAMS Starts with "What needs to be done" Tells us "How to do it" Produces a computer program Automatic determination of program size Code reuse Parameterized reuse Internal storage Iterations, loops, and recursions Self-organization of hierarchies Automatic determination of program architecture Wide range of programming constructs Well-defined Problem-independent Wide applicability Scalable Competitive with human- produced results

125. GENETIC PROGRAMMING PROBLEM SOLVER (GPPS)

126. AUTOMATIC SYNTHESIS OF BOTH THE TOPOLOGY AND SIZING OF ANALOG ELECTRICAL CIRCUITS BY MEANS OF DEVELOPMENTAL GENETIC PROGRAMMING

127. AUTOMATED CIRCUIT SYNTHESIS The topology of a circuit includes specifying the gross number of components in the circuit, the type of each component (e.g., a capacitor), and a netlist specifying where each lead of each component is to be connected. Sizing involves specifying the values (typically numerical) of each of the circuit's components.

128. COMPONENT-CREATING FUNCTIONS Resistor R function Capacitor C function Inductor L function Diode D function Transistor Q function (3-leaded)

129. COMPONENT-CREATING FUNCTIONS

130. TOPOLOGY-MODIFYING FUNCTIONS SERIES division PARALLEL division VIA FLIP

131. TOPOLOGY-MODIFYING FUNCTIONS

132. DEVELOPMENT-CONTROLLING FUNCTIONS END function NOP (No Operation) function SAFE_CUT function

133. THE INITIAL CIRCUIT

134. DEVELOPMENTAL GP (LIST (C (– 0.963 (– (– -0.875 -0.113) 0.880)) (series (flip end) (series (flip end) (L -0.277 end) end) (L (– - 0.640 0.749) (L -0.123 end)))) (flip (nop (L -0.657 end)))))

135. CAPACITOR-CREATING FUNCTION (LIST (C (– 0.963 (– (– -0.875 -0.113) 0.880)) (series (flip end) (series (flip end) (L – 0.277 end) end) (L (– -0.640 0.749) (L -0.123 end)))) (flip (nop (L -0.657 end)))))

136. CAPACITOR-CREATING FUNCTION

137. SERIES DIVISION FUNCTION (LIST (C (– 0.963 (– (– -0.875 -0.113) 0.880)) (series (flip end) (series (flip end) (L – 0.277 end) end) (L (– -0.640 0.749) (L -0.123 end)))) (flip (nop (L -0.657 end)))))

138. SERIES DIVISION

139. DEVELOPMENTAL GP

140. EVALUATION OF FITNESS

141. DESIRED BEHAVIOR OF A LOWPASS FILTER

142. EVOLVED CAMPBELL FILTER U. S. patent 1,227,113 George Campbell American Telephone and Telegraph 1917

143. EVOLVED ZOBEL FILTER U. S. patent 1,538,964 Otto Zobel American Telephone and Telegraph Company 1925

144. EVOLVED SALLEN-KEY FILTER

145. EVOLVED DARLINGTON EMITTER- FOLLOWER SECTION U. S. patent 2,663,806 Sidney Darlington Bell Telephone Laboratories 1953

146. NEGATIVE FEEDBACK

147. HAROLD BLACK’S RIDE ON THE LACKAWANNA FERRY Courtesy of Lucent Technologies

148. 20 th -CENTURY PATENTS Campbell ladder topology for filters Zobel “M-derived half section” and “constant K” filter sections Crossover filter Negative feedback Cauer (elliptic) topology for filters PID and PID-D2 controllers Darlington emitter-follower section and voltage gain stage Sorting network for seven items using only 16 steps 60 and 96 decibel amplifiers Analog computational circuits Real-time analog circuit for time-optimal robot control Electronic thermometer Voltage reference circuit Philbrick circuit NAND circuit Simultaneous synthesis of topology, sizing, placement, and routing

149. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

150. SIX POST-2000 PATENTED INVENTIONS

151. EVOLVED HIGH CURRENT LOAD CIRCUIT

152. REGISTER-CONTROLLED CAPACITOR CIRCUIT

153. LOW-VOLTAGE CUBIC CIRCUIT

154. VOLTAGE-CURRENT-CONVERSION CIRCUIT

155. LOW-VOLTAGE BALUN CIRCUIT

156. TUNABLE INTEGRATED ACTIVE FILTER

157. 21 st -CENTURY PATENTED INVENTIONS Low-voltage balun circuit Mixed analog-digital variable capacitor circuit High-current load circuit Voltage-current conversion circuit Cubic function generator Tunable integrated active filter

158. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

159. NOVELTY-DRIVEN EVOLUTION Two factors in fitness measure –Circuit’s behavior in the frequency domain –Largest number of nodes and edges (circuit components) of a subgraph of the given circuit that is isomorphic to a subgraph of a template representing the prior art. Graph isomorphism algorithm with the cost function being based on the number of shared nodes and edges (instead of just the number of nodes).

160. NOVELTY-DRIVEN EVOLUTION For circuits not scoring the maximum number of hits (101), the fitness of a circuit is the product of the two factors. For circuits scoring 101 hits (100%- compliant individuals), fitness is the number of shared nodes and edges divided by 10,000.

161. PRIOR ART TEMPLATE

162. NON-INFRINGING SOLUTION NO. 1

163. NON-INFRINGING SOLUTION NO. 5

164. GP AS AN INVENTION MACHINE

165. CIRCUIT-CONSTRUCTING PROGRAM TREE WITH ADFs

166. LOWPASS FILTER WITH ADFs

167. ADF0

168. AUTOMATIC SYNTHESIS OF CIRCUIT LAYOUT INCLUDING THE PLACEMENT OF COMPONENTS AND ROUTING OF WIRES ALONG WITH THE TOPOLOGY AND SIZING

169. CIRCUIT LAYOUT Circuit placement involves the assignment of each of the circuit's components to a particular physical location on a printed circuit board or silicon wafer. Routing involves the assignment of a particular physical location to the wires between the leads of the circuit's components.

170. LAYOUT

171. LAYOUT — GENERATION 0

172. 100%-COMPLIANT LOWPASS FILTER GENERATION 25 WITH 5 CAPACITORS AND 11 INDUCTORS  AREA OF 1775.2

173. 100%-COMPLIANT LOWPASS FILTER GENERATION 30 WITH 10 INDUCTORS AND 5 CAPACITORS  AREA OF 950.3

174. 100%-COMPLIANT LOWPASS FILTER BEST-OF-RUN CIRCUIT OF GENERATION 138 WITH 4 INDUCTORS AND 4 CAPACITORS  AREA OF 359.4

175. LAYOUT  60 DB AMPLIFIER

176. AUTOMATIC SYNTHESIS OF BOTH THE TOPOLOGY AND TUNING OF CONTROLLERS

177. PROGRAM TREE FOR A CONTROLLER

178. CONTROLLER BLOCKS gain integrator differentiator adder subtractor multiplier differential input integrators inverter lead lag two-parameter lag absolute value limiter divider delay conditional operators (switches)

179. FUNCTION SET FOR CONTROLLER SYNTHESIS F = { GAIN,INVERTER,LEAD,LAG,LAG2, DIFFERENTIAL_INPUT_INTEGRATOR, DIFFERENTIATOR, ADD_SIGNAL, SUB_SIGNAL,ADD_3_SIGNAL,ADF0, ADF1,ADF2,ADF3,ADF4 }

180. TERMINAL SET FOR CONTROLLER SYNTHESIS T = { REFERENCE_SIGNAL, CONTROLLER_OUTPUT, PLANT_OUTPUT }

181. CONSTRAINED SYNTACTIC STRUCTURE A grammar that specifies what functions and terminals are allowed as arguments to particular functions For example, the first argument of the GAIN function must be a value-setting subtree whereas the second can be from the general pool of functions Also known as strong typing

182. TWO-LAG PLANT

183. 8 FITNESS CASES 8 elements of the fitness measure represent 2  2  2 choices: –2 different values of the plant's internal gain, K (1.0 and 2.0), –2 different values of the plant's time constant  (0.5 and 1.0), –2 different values for the height of the reference signal (rising from 0 to 1 volts or from 0 to 1 microvolts at t = 100 milliseconds

184. FITNESS MEASURE For each of these 8 fitness cases, a transient analysis (time domain) is performed using the SPICE simulator. The contribution to fitness for the 8 elements is –Integral of time-weighted absolute error (ITAE) –e(t) is difference between plant output and reference signal. –Multiplication by B (10 6 or 1) makes both reference signals equally influential. –Additional weighting function, A, heavily penalizes non-compliant amounts of overshoot. A weights all variations up to 2% above the reference signal by 1.0, but bigger variations by 10.0.

185. EVOLVED CONTROLLER FOR TWO-LAG PLANT

186. LESS ITAE AND OVERSHOOT

187. BETTER DISTURBANCE REJECTION

188. REVERSE ENGINEERING OF METABOLIC PATHWAYS

189. EVOLVED PATHWAY

190. ANTENNA SYNTHESIS USING GP (PROGN3 (TURN-RIGHT 0.125) (LANDMARK (REPEAT 2 (PROGN2 (DRAW 1.0 HALF-MM-WIRE) (DRAW 0.5 NO-WIRE))) (TRANSLATE-RIGHT 0.125 0.75))

191. USING A TURTLE TO DRAW TWO- DIMENSIONAL ANTENNA

192. BEST-OF-RUN ANTENNA FROM GENERATION 90

193. 3-DIMENSIONAL ANTENNA

194. NASA EVOLVED ANTENNA To be on satellite to be launched in 2004

195. OTHER STRUCTURES

196. GENETIC NETWORK FOR lac operon

197. EVOLVED NETWORK (IF (< LACTOSE_LEVEL 9.139 ) (IF (< REPRESSOR_LEVEL 6.270 ) (IF (> GLUCOSE_LEVEL 5.491 ) 2.02 (IF (< CAP_LEVEL 0.639 ) 2.033 (IF ( LACTOSE_LEVEL 2.511 ) (IF (> CAP_LEVEL 7.807 ) 5.586 (IF (> LACTOSE_LEVEL 2.114 ) 1.978 2.137 ) ) 0.0 ) (IF (> REPRESSOR_LEVEL 4.015 ) 0.036 (IF (< GLUCOSE_LEVEL 5.128 ) 10.0 (IF (< REPRESSOR_LEVEL 4.268 ) 2.022 9.122 ) ) ) ) ) ) (IF (> CAP_LEVEL 0.842 ) 0.0 5.97 ) ) (IF (< CAP_LEVEL 1.769 ) 2.022 (IF ( LACTOSE_LEVEL 1.256 ) (IF (> LACTOSE_LEVEL 1.933 ) (IF (> GLUCOSE_LEVEL 2.022 ) (IF (< GLUCOSE_LEVEL 5.183 ) 6.323 (IF (> CAP_LEVEL 1.208 ) 9.713 0.842 ) ) 10.0 ) (IF (> GLUCOSE_LEVEL 6.270 ) 2.109 ) 1.965 ) ) 0.665 ) 1.982 ) ) )

198. IN C-STYLE PSEUDO CODE if(LACTOSE_LEVEL < 9.139) { if(REPRESSOR_LEVEL < 6.270) { LAC_mRNA_LEVEL = 2.022; } else { LAC_mRNA_LEVEL = 0.0; } else { if(CAP_LEVEL < 1.769) { LAC_mRNA_LEVEL = 2.022; } else { if(GLUCOSE_LEVEL < 2.382) { LAC_mRNA_LEVEL = 10.0; } else { LAC_mRNA_LEVEL = 1.982; } } }

199. PARAMETERIZED TOPOLOGIES One of the most important characteristics of computer programs is that they ordinarily contain inputs (free variables) and conditional operations

200. PARAMETERIZED TOPOLOGY FOR LOWPASS FILTER

201. PARAMETERIZED TOPOLOGY FOR HIGHPASS FILTER

202. PARAMETERIZED TOPOLOGY FOR GENERAL-PURPOSE CONTROLLER

203. EVOLVED EQUATIONS FOR GENERAL- PURPOSE CONTROLLER

205. PATENTABLE NEW INVENTIONS PID tuning rules that outperform the Ziegler-Nichols and Åström-Hägglund tuning rules General-purpose controllers outperforming Ziegler-Nichols and Åström-Hägglund rules

206. PROGRESSION OF QUALITATIVELY MORE SUBSTANTIAL RESULTS PRODUCED BY GP Toy problems Human-competitive non-patent results 20 th -century patented inventions 21 st -century patented inventions Patentable new inventions

207. PARALLELIZATION WITH SEMI-ISOLATED SUBPOPULATIONS

208. GP PARALLELIZATION Like Hormel, Get Everything Out of the Pig, Including the Oink Keep on Trucking It Takes a Licking and Keeps on Ticking The Whole is Greater than the Sum of the Parts

209. PETA-OPS Human brain operates at 10 12 neurons operating at 10 3 per second = 10 15 ops per second 10 15 ops = 1 peta-op = 1 bs (brain second)

210. EVOLVABLE HARDWARE CORNER OF XILINX XC6216

211. FUNCTION UNIT FOR CELL OF XILINIX XC6216

212. SORTING NETWORK

213. EVOLVED SORTING NETWORK

214. GP 1987–2002 SystemDatesSpeed-up over first system Human- competitive results Problem Category Serial LISP1987–19941 (base)0 toy problems 64 transputers1994–199792human-competitive results not related to patented inventions 64 PowerPC’s1995–20002041220th-century patented inventions 70 Alpha’s1999–20011,481220th-century patented inventions 1,000 Pentium II’s2000–200213,9001221st-century patented inventions 4-week runs on 1,000 Pentium II’s 2002-2003130,0002patentable new inventions

215. PROMISING GP APPLICATION AREAS Problem areas involving many variables that are interrelated in highly non-linear ways Inter-relationship of variables is not well understood Discovery of the size and shape of the solution is a major part of the problem "Black art" problems

216. PROMISING GP APPLICATION AREAS (CONTINUED) Areas where you simply have no idea how to program a solution, but where you know what you want

217. PROMISING GP APPLICATION AREAS (CONTINUED) Problem areas where a good approximate solution is satisfactory  design  control  bioinformatics  classification  data mining  system identification  forecasting

218. PROMISING GP APPLICATION AREAS (CONTINUED)  Areas where large computerized databases are accumulating and computerized techniques are needed to analyze the data  genome, protein, microarray data  satellite image data  astronomical data  petroleum databases  financial databases  medical records  marketing databases

219. PROMISING GP APPLICATION AREAS (CONTINUED)  Areas for which humans find it very difficult to write good programs  parallel computers  cellular automata  multi-agent strategies  field-programmable game arrays  digital signal processors  swarm intelligence

220. CHARACTERISTICS SUGGESTING USE OF GP (1) discovering the size and shape of the solution, (2) reusing substructures, (3) discovering the number of substructures, (4) discovering the nature of the hierarchical references among substructures, (5) passing parameters to a substructure, (6) discovering the type of substructures (e.g., subroutines, iterations, loops, recursions, or storage), (7) discovering the number of arguments possessed by a substructure, (8) maintaining syntactic validity and locality by means of a developmental process, or (9) discovering a general solution in the form of a parameterized topology containing free variables

221. DESIGNING A GIRAFFE Long neck Long tongue Vegetable-digesting enzymes in stomach 4 legs Long legs Brown coloration

222. THE DESIGN OF A GOOD GIRAFE Neck length Tongue length Carnivorous?Number of legs Leg length Coloration 15.11 feet 14 inchesNo49.96 feetBrown Floating point BooleanIntegerFloating point Categorical

223. NON-LINEARITY — GIRAFE Taken one-by-one, some gene values found in a giraffe, such as the long neck contribute (alone) negatively to fitness –requires considerable material to construct –requires considerable energy to maintain –prone to injury (thereby hurting rate of survival and reproduction) Thus, maximizing any one variable will not lead to the global optimum solution

224. NON-LINEARITY (CONTINUED) When the variables are taken in pairs (there are 15 possible pairs), many combinations of pairs (e.g., Long neck and long tongue) are doubly detrimental

225. NON-LINEARITY (CONTINUED) But, certain combinations of traits, when taken together, are "co-adapted sets of alleles" that yield a very fit animal for eating high acacia leaves in the jungle environment, having good camouflage, having high escape velocity when faced with predators, and exploiting a niche (and avoiding competition) with other animals feeding on low-hanging vegetation

226. SEARCH METHODS IN GENERAL Initial structure(s) Fitness measure Operations for creating new structures Parameters Termination criterion and method of designating the result

227. SPACE WITH MANY LOCAL OPTIMA

228. SEARCH METHODS Blind random search does not use acquired information in deciding on the future direction of the search Hill combing and gradient descent use acquired information; however, they are prone to becoming trapped on local optima The previous point is especially true for non- trivial search spaces

229. 7 DIFFERENCES BETWEEN GP AND ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES

230. REPRESENTATION Genetic programming overtly conducts it search for a solution to the given problem in program space

231. ROLE OF POINT-TO-POINT TRANSFORMATIONS IN THE SEARCH Genetic programming does not conduct its search by transforming a single point in the search space into another single point, but instead transforms a set of points into another set of points

232. ROLE OF HILL CLIMBING IN THE SEARCH Genetic programming does not rely exclusively on greedy hill climbing to conduct its search, but instead allocates a certain number of trials, in a principled way, to choices that are known to be inferior

233. DETERMINISM IN THE SEARCH Genetic programming conducts its search probabilistically

234. ROLE OF AN EXPLICIT KNOWLEDGE BASE Genetic programming does NOT make use of a knowledge base

235. ROLE OF FORMAL LOGIC IN THE SEARCH Genetic programming does not utilize formal logic in it’s search strategy. Contradictory alternatives are created and actively maintained.

236. UNDERPINNINGS OF THE TECHNIQUE Biologically inspired

237. TURING (1948) Turing made the connection between searches and the challenge of getting a computer to solve a problem without explicitly programming it in his 1948 essay “Intelligent Machines” "Further research into intelligence of machinery will probably be very greatly concerned with 'searches'... “

238. TURING’S 3 APPROACHES TO MACHINE INTELLIGENCE (1948) LOGIC-BASED SEARCH One approach that Turing identified is a search through the space of integers representing candidate computer programs.

239. TURING’S 3 APPROACHES (CONTINUED) CULTURAL SEARCH A second approach is the "cultural search“ which relies on knowledge and expertise acquired over a period of years from others (akin to present-day knowledge- based systems).

240. TURING’S 3 APPROACHES (CONTINUED) GENETICAL OR EVOLUTIONARY SEARCH "There is the genetical or evolutionary search by which a combination of genes is looked for, the criterion being the survival value.“

241. TURING (1950) From Turing’s 1950 paper "Computing Machinery and Intelligence" … “We cannot expect to find a good child- machine at the first attempt. One must experiment with teaching one such machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution, by the identifications”

242. TURING (1950) (CONTINUED) “Structure of the child machine = Hereditary material “Changes of the child machine = Mutations “Natural selection = Judgment of the experimenter”