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Evolutionary Computing Genetic Algorithms

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Design Representation in Genetic Algorithms. A design description is ... a combination of different types of varibles, e.g. numerical, boolean, lists, etc. ... – PowerPoint PPT presentation

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Title: Evolutionary Computing Genetic Algorithms


1
Evolutionary ComputingGenetic Algorithms
  • TMR4115
  • Thursday, Oct 30 2003
  • Prof. Stein Ove Erikstad

2
Classes of Evolutionary Algorithms and AI
  • Genetic Algorithms
  • analogous to evolution
  • Simulated annealing
  • analoguous to the way metal cools into a minimum
    energy crystalline structure
  • Neural networks

3
Genetic Algorithms
  • Search based on mechanisms from natural selection
    and genetics
  • chromosomes
  • genes
  • alleles
  • locus
  • genotype
  • phenotype
  • generation
  • population
  • survival of the fittest
  • The driving force in GA is the combination and
    exchange of chromosome material during breeding

4
Difference from classical optimization
  • Population of points
  • not a single one
  • Use fitness (payoff)
  • not derivatives
  • Probabilistic transition rules
  • not deterministic

5
Design Representation in Genetic Algorithms
  • A design description is represented as a string
  • This string may be
  • a direct binary representation of a design
    variable
  • a binary representation of a discrete point in an
    interval
  • a combination of different types of varibles,
    e.g. numerical, boolean, lists, etc.

100100100111101010111001011000111
6
Three core processes
  • Reproduction
  • Crossover
  • Mutation

7
Reproduction
  • Individuals are selected for mating
  • Probability of selection increases with fitness
  • survival of the fittest

8
Crossover
  • Simple crossover by splitting strings and
    exchanging parts
  • Crossover point selected randomly

1 0 1 1
0 0 0 1
1 0 1 1
1 0 1 1 0 0 1 0
0 0 1 0
0 1 1 1 0 0 0 1
0 0 1 0
0 1 1 1
0 1 1 1
0 0 0 1
9
Mutation
  • Arbitrary change at single point
  • Typically low frequency in both natural and
    artificial systems
  • But useful insurance towards premature loss of
    important notions

Mutation Flipping bit position
0 1 1 1 0 1 0 1
0 1 1 1 0 0 0 1
10
A Simple GA Algorithm
We want to determine the value of the design
variable x for which the performance f(x) is
maximized
  • Create initial population
  • Determine range
  • Create random individs
  • Create associated bitstring representation
  • Select for reproduction
  • Determine fitness for each
  • Assign propability for reproduction based on
    fitness
  • Use spinning wheel to select individs for mating

11
A Simple GA Algorithm 2
  • Use crossover to create children
  • For each pair, randomly select crossover point
  • Exchange bitstrings to create two new children
  • Use mutation to insert new genetic material
  • For each individ, use mutation probability to
    determine whether to mutate or not
  • If mutation, randomly select position to flip bit

12
Example
  • Excel

13
Some strategies for improved performance
  • Normalized fitness function
  • often necessary for survival of the fittest
    mechanism to work
  • increases relative fitness difference
  • Elitism
  • preserve best individs from generation to
    generation
  • avoids loosing good solutions
  • may lead to premature convergence
  • Parent competition
  • children must also comete against parents
  • quicker convergence poorer search coverage
  • Tuning algorithm settings
  • population size
  • mutation probability

14
Pro Cons
  • Computationally intensive
  • for complex fitness evaluation
  • Gives little insight into design model behaviour
  • blackbox approach
  • Difficult to know about quality of solution
  • Easy to adapt to existing systems
  • wrap around existing analysis tools
  • only link is fitness calculation (performance)
  • No requirements to model behaviour
  • Not trapped in local optima
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