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Genetic Algorithms in Problem Solving 1998 7 16. EVOLVING COMPUTER PROGRAMS (1) t Evolving Lisp Programs Keplers Third Law: P 2 = cA 3 PROGRAM ORBITAL_PERIORD.

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Presentation on theme: "Genetic Algorithms in Problem Solving 1998 7 16. EVOLVING COMPUTER PROGRAMS (1) t Evolving Lisp Programs Keplers Third Law: P 2 = cA 3 PROGRAM ORBITAL_PERIORD."— Presentation transcript:

1 Genetic Algorithms in Problem Solving

2 EVOLVING COMPUTER PROGRAMS (1) t Evolving Lisp Programs Keplers Third Law: P 2 = cA 3 PROGRAM ORBITAL_PERIORD C# Mars # A = 1.52 P = SQRT(A * A * A) PRINT P END ORBITAL_PERIORD (defun orbital_period () ; Mars ; (setf A 1.52) (sqrt (* A (* A A))))

3 EVOLVING COMPUTER PROGRAMS (2) Kozas algorithm - Genetic Programming (GP) 1. Choose a set of possible functions and terminals for the program. F = {+, - *, /, }, T = {A} 2. Generate an initial population of random trees (programs) using the set of possible functions and terminals. 3. Calculate the fitness of each program in the population by running it on a set of fitness cases (a set of input for which the correct output is known). 4. Apply selection, crossover, and mutation to the population to form a new population. 5. Steps 3 and 4 are repeated for some number of generations.

4 EVOLVING COMPUTER PROGRAMS (3)

5 EVOLVING COMPUTER PROGRAMS (4) Block-Stacking Problem u T = {CS, TB, NN} u F = {MS(x), MT(x), DU(exp1, exp2), NOT(exp1), EQ(exp1, exp2) } u (EQ (DU (MT CS) (NOT CS)) (DU (MS NN) (NOT NN)))

6 EVOLVING COMPUTER PROGRAMS (5) t Evolving Cellular Automata (CA) Example (N=11, radius = 1) space-time diagram Rule table: neighborhood: output bit : Lattice: t = t =

7 EVOLVING COMPUTER PROGRAMS (6) Density-classification task (N=149, r =3)

8 DATA ANALYSIS AND PREDICTION (1) t Predicting Dynamical Systems individual u C = {($20 Price of Xerox Stock on day 1) ^ ($25 Price of Xerox Stock on day 2 $27) ^ ($22 Price of Xerox Stock on day 3 $25)} crossover, mutation

9 DATA ANALYSIS AND PREDICTION (2)

10 DATA ANALYSIS AND PREDICTION (3) t Predicting Protein Structure

11 EVOLVING NEURAL NETWORKS (1)

12 EVOLVING NEURAL NETWORKS (2) t Evolving Weights in a Fixed Network

13 EVOLVING NEURAL NETWORKS (3)

14 EVOLVING NEURAL NETWORKS (4) t Evolving Network Architectures Direct Encoding

15 EVOLVING NEURAL NETWORKS (5) Grammatical Encoding


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