032607 - Lecture 16 Recall Standard(Simple Genetic...

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Lecture 16: 03/26/2007 Recall: Standard (Simple) Genetic Algorithm (SGA) Fixed-size populations (n) Individuals in population are fixed-length (L) bit strings Exogenous fitness function—each bit string x has a fitness f(x)—assume that f(x)>0, all x P(t)-->P(t+1) (populations) [that is P(t) evolves to P(t+1)] via: 1. Selection of parents for P(t+1) from P(t) 2. Generation of offspring from selected parents Talked briefly about various selection methods (fitness-prop, tournament, ranked, elitist.) Generation of offspring via crossover mutation . Crossover probability p c and mutation probability p m —p c ~.75, p m ~.01 and also different kinds of crossover (one-point, two-point, or uniform.) Do some back-of-envelope calculation regarding SGA with fitness-proportional selection with one-pt. crossover (w/ prob. P c ), bitwise mutation (p m ). What is fitness-prop. selection ? A probabilistic selection method that gives every individual a non-zero probability of being selected as a parent (ie. don’t just pick the best!) and doesn’t guarantee any individual is a parent. To get the n-parents, draw from P9t); draw an individual xєP sel (x) proportional to f(x) with replacement.
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  • Spring '07
  • Algorithms, SGA, average fitness, fitness proportional selection, H Holland, highly fit schemas

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