Genetic Algorithms (Evolutionary Computing) Genetic Algorithms are used to try to “evolve” the solution to a problem Generate prototype solutions called chromosomes (individuals) Backpack problem as example: http://home.ksp.or.jp/csd/english/ga/gatrial/ Ch9_A2_4.html All individuals form the population Generate new individuals by reproduction Use a fitness function to evaluate individuals Survival of the fittest: population has a fixed size Individuals with higher fitness is more likely to reproduce
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Genetic Algorithms (Evolutionary Computing) Genetic Algorithms are used to try to “evolve” the solution to a problem Generate prototype solutions called.
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Genetic Algorithms(Evolutionary Computing)
Genetic Algorithms are used to try to “evolve” the solution to a problem Generate prototype solutions called chromosomes
All individuals form the population Generate new individuals by reproduction Use a fitness function to evaluate individuals Survival of the fittest: population has a fixed size Individuals with higher fitness is more likely to
reproduce
Reproduction Methods Mutation
Alter a single gene in the chromosome randomly to create a new chromosome
Example Cross-over
Pick a random location within chromosome New chromosome receives first set of genes
from parent 1, second set from parent 2 Example
Inversion Reverse the chromsome
Interpretation Genetic algorithms try to solve a
hill climbing problem Method is parallelizable The trick is in how you represent
the chromosome Tries to avoid local maxima by
keeping many chromsomes at a time
Another Example:Traveling Sales Rep Problem
How to represent a chromosome? What effects does this have on
crossover and mutation?
TSP Chromosome: Ordering of city numbers
(1 9 2 4 6 5 7 8 3) What can go wrong with crossover? To fix, use order crossover technique Take two chromosomes, and take two