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## Genetic Algorithms in Problem Solving

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**Genetic Algorithms in Problem Solving**1998년 7월 16일 조 동 연**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))))**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.**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)))**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**EVOLVING COMPUTER PROGRAMS (6)**• Density-classification task (N=149, r =3)**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**DATA ANALYSIS AND PREDICTION (3)**• Predicting Protein Structure**EVOLVING NEURAL NETWORKS (2)**• Evolving Weights in a Fixed Network**EVOLVING NEURAL NETWORKS (4)**• Evolving Network Architectures • Direct Encoding**EVOLVING NEURAL NETWORKS (5)**• Grammatical Encoding