2. Search procedure that probabilistically applies. search operators to set of points in the search

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1 Evoluationary Computation 1. Computational procedures patterned after biological evolution 2. Search procedure that probabilistically applies search operators to set of points in the search space 169 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

2 Biological Evolution Lamarck and others: Species \transmute" over time Darwin and Wallace: Consistent, heritable variation among individuals in population Natural selection of the ttest Mendel and genetics: A mechanism for inheriting traits genotype! phenotype mapping 170 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

3 GA(F itness; F itness threshold; p; r; m) Initialize: P p random hypotheses Evaluate: for each h in P, compute F itness(h) While [ma h F itness(h)] < F itness threshold 1. Select: Probabilistically select (1 r)p members of P to add to P S. Pr(h i ) = F itness(h i) Pp j=1 F itness(h j) 2. Crossover: Probabilistically select rp 2 pairs of hypotheses from P. For each pair, hh1; h2i, produce two ospring by applying the Crossover operator. Add all ospring to P s. 3. Mutate: Invert a randomly selected bit in m p random members of P s 4. Update: P P s 5. Evaluate: for each h in P, compute F itness(h) Return the hypothesis from P that has the highest tness. 171 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

4 Representing Hypotheses Represent (Outlook = Overcast _ Rain) ^ (W ind = Strong) by Outlook W ind Represent by IF W ind = Strong THEN P layt ennis = yes Outlook W ind P layt ennis lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

5 Operators for Genetic Algorithms Single-point crossover: Initial strings Crossover Mask Offspring Two-point crossover: Uniform crossover: Point mutation: lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

6 Selecting Most Fit Hypotheses Fitness proportionate selection:... can lead to crowding Tournament selection: Pr(h i ) = F itness(h i) Pp j=1 F itness(h j) Pick h1; h2 at random with uniform prob. With probability p, select the more t. Rank selection: Sort all hypotheses by tness Prob of selection is proportional to rank 174 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

7 Genetic Programming Population of programs represented by trees sin() r 2 y sin ^ y lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

8 Crossover sin ^ sin 2 y ^ y 2 sin ^ sin 2 ^ 2 y y 183 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

9 GP for Classifying Images [Teller and Veloso, 1997] Fitness: based on coverage and accuracy Representation: Primitives include Add, Sub, Mult, Div, Not, Ma, Min, Read, Write, If-Then-Else, Either, Piel, Least, Most, Ave, Variance, Dierence, Mini, Library Mini refers to a local subroutine that is separately co-evolved Library refers to a global library subroutine (evolved by selecting the most useful minis) Genetic operators: Crossover, mutation Create \mating pools" and use rank proportionate reproduction 188 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

10 Biological Evolution Lamark (19th century) Believed individual genetic makeup was altered by lifetime eperience But current evidence contradicts this view What is the impact of individual learning on population evolution? 189 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

11 Summary: Evolutionary Programming Conduct randomized, parallel, hill-climbing search through H Approach learning as optimization problem (optimize tness) Nice feature: evaluation of Fitness can be very indirect { consider learning rule set for multistep decision making { no issue of assigning credit/blame to indiv. steps 193 lecture slides for tetbook Machine Learning, T. Mitchell, McGraw Hill, 1997

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