1 / 15

Genetic Algorithms in Problem Solving

Genetic Algorithms in Problem Solving. 1998 년 7 월 16 일 조 동 연. EVOLVING COMPUTER PROGRAMS (1). Evolving Lisp Programs Kepler’s 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 ;

edna
Download Presentation

Genetic Algorithms in Problem Solving

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 Algorithms in Problem Solving 1998년 7월 16일 조 동 연

  2. EVOLVING COMPUTER PROGRAMS (1) • Evolving Lisp Programs • Kepler’s Third Law: P2 = cA3 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) • Koza’s 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 • T = {CS, TB, NN} • F = {MS(x), MT(x), DU(exp1, exp2), NOT(exp1), EQ(exp1, exp2) } • (EQ (DU (MT CS) (NOT CS)) (DU (MS NN) (NOT NN)))

  6. EVOLVING COMPUTER PROGRAMS (5) • Evolving Cellular Automata (CA) • Example (N=11, radius = 1) • space-time diagram Rule table: neighborhood: 000 001 010 011 100 101 110 111 output bit : 0 0 0 1 0 1 1 1 Lattice: t = 0 1 0 1 0 0 1 1 1 0 1 0 t = 1 0 1 0 0 0 1 1 1 1 0 1

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

  8. DATA ANALYSIS AND PREDICTION (1) • Predicting Dynamical Systems • individual • 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) • Predicting Protein Structure

  11. EVOLVING NEURAL NETWORKS (1)

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

  13. EVOLVING NEURAL NETWORKS (3)

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

  15. EVOLVING NEURAL NETWORKS (5) • Grammatical Encoding

More Related