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Genetic Algorithms Sushil J. Louis Evolutionary Computing Systems LAB Dept. of Computer Science University of Nevada, Reno

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Presentation on theme: "Genetic Algorithms Sushil J. Louis Evolutionary Computing Systems LAB Dept. of Computer Science University of Nevada, Reno"— Presentation transcript:

1 Genetic Algorithms Sushil J. Louis Evolutionary Computing Systems LAB Dept. of Computer Science University of Nevada, Reno http://www.cs.unr.edu/~sushil/

2 2 Search techniques Hill Climbing/Gradient Descent –You are getting closer OR You are getting further away from correct combination –Quicker –Distance metric could be misleading –Local hills

3 3 Search techniques Parallel hillclimbing –Everyone has a different starting point –Perhaps not everyone will be stuck at a local optima –More robust, perhaps quicker

4 4 Genetic Algorithms Parallel hillclimbing with information exchange among candidate solutions Population of candidate solutions Crossover for information exchange Good across a variety of problem domains

5 5 Genetic Algorithm Generate pop(0) Evaluate pop(0) T=0 While (not converged) do –Select pop(T+1) from pop(T) –Recombine pop(T+1) –Evaluate pop(T+1) –T = T + 1 Done

6 6 Evaluate Decoded individual Fitness Application dependent fitness function

7 7 Designing a parity checker Search for circuit that performs parity checking Parity: if even number of 1s in input correct output is 0, else output is 1 Important for computer memory and data communication chips What is the genotype? – selected, crossed over and mutated A circuit is the phenotype – evaluated for fitness. How do you construct a phenotype from a genotype to evaluate?

8 8 What is a genotype? 111111 100111 A genotype is a bit string that codes for a phenotype 000000 Randomly chosen crossover point 11 111100 0000 CrossoverParentsOffspring 110100 111111 110111 Mutation Randomly chosen mutation point

9 9 Genotype to Phenotype mapping 100111110100 150 length binary string 1101110 1010100 0100000 1101100 0110110 1010000 1 row of 150 becomes 6 rows of 25

10 10 Genotype to Phenotype mapping A circuit is made of logic gates. Receives input from the 1 st column and we check output at last column. 16111621 2631 146 Each group of five bits codes for one of 16 possible gates and the location of second input

11 11 Evaluating the phenotype Feed the gate an input combination Check whether the output produced by a decoded member of the population is correct Give one point for each correct output This is essentially a circuit simulation Max Fitness = 2^6 = 64

12 12 Circuits Parity Checker Adder

13 13 Traveling Salesperson Problem Find a shortest length tour of N cities N! possible tours 10! = 3628800 70! = 119785716699698917960727837216890987364589381425464 25857555362864628009582789845319680000000000000000 Chip layout, truck routing, logistics

14 14 Predicting subsurface structure Find subsurface structure that agrees with experimental observations Mining, oil exploration, swimming pools

15 15 Designing a truss Find a truss configuration that minimizes vibration, minimizes weight, and maximizes stiffness.

16 16 How does it work 01101131690.140.581 11000245760.491.972 010008640.060.220 10011193610.311.231 Sum11701.04.00 Avg293.251.00 Max576.491.972.00 String decoded f(x^2) fi/Sum(fi) Expected Actual

17 17 How does it work cont’d 0110|120110012144 1100|011100125625 11|00041101127729 10|01131000016256 Sum1754 Avg439 Max729 String mate offspring decoded f(x^2)


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