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Selection Methods


Fitness-Proportionate Selection (Roulette Wheel) ... Fitness-Proportionate Selection (Roulette Wheel) Used originally by Holland ... – PowerPoint PPT presentation

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Title: Selection Methods

Selection Methods
  • Choosing the individuals in the population that
    will create offspring for the next generation.
  • Richard P. Simpson

Methods often used
  • Fitness-Proportionate Selection (Roulette Wheel)
  • Fitness-Proportionalte Selection (Stochastic
    Universal Sampling)
  • Sigma Scaling
  • Elitism
  • Boltzmann Selection
  • Rank Selection
  • Tournament Selection
  • Steady-State Selection

Fitness-Proportionate Selection (Roulette Wheel)
  • Used originally by Holland
  • Here the expected number of times an individual
    will bes selected to reproduce is that
    individuals fitness divided by the average
    fitness of the population
  • Let T sum of the expected values of the
    individuals in the population.
  • Choose a random number between 0 and T
  • Loop through the individuals in the pop. summing
    the expected values until the sum is greater than
  • Select the individual that puts the sum over
    this limit.

Fitness-Proportionate Selection (Roulette Wheel)
  • This methods results in the expected number of
    offspring for each individual
  • Does not work well for small populations
  • To correct this SUS(stochastic universal
    sampling) has been proposed.
  • Here one spins the wheel one using N equally
    spaced points to select N parents.

SUS(stochastic universal sampling)
  • Fitness-proportionate selections main problem
  • Early in the search the fitness variance in the
    population is high. The highly fit individuals
    will multiply quickly and soon dominate the pop.
    This is called premature convergence, ie
    exploration is slows rapidly. Often finds non
    optimal hills.
  • Fitness-Proportionate Selection (Roulette Wheel)
  • The rate of evolution depends on the variance of
    fitnesses in the population.

Sigma Scaling
  • Used to address the previous problem
  • In these cases raw fitness values are mapped to
    expected values so as to make the GA less
    susceptible to premature convergence.
  • Here an attempt is made to keep selection
    pressure relatively constant over the course of
    the run.
  • Sigma scaling, an individuals expected value is
    a function of its fitness, the population mean,
    and the population standard deviation.

mean fitness
standard deviation at time t
  • Kenneth De Jong (1975)
  • Can be used with many of the other selection
  • Here a certain percent of the population is
    carried forward to the next population.
  • This implies that the best individual ever
    discovered will appear in the final generations
  • Sometimes elitism significantly improves
    performance, depends on the problem.

Boltzmann Selection
  • Sometimes we would like to vary the selection
    pressure over a run.
  • We could start with low selection pressure and
    increase it as we progress from generation to
  • This implies that we are searching more during
    the initial generations of the run and evolving
    faster toward the end of the run.

Rank Selection
  • This is another attempt to prevent premature
  • The individuals are sorted according to fitness.
  • Ranking avoids giving the far largest share of
    offspring to a small group of highly fit
    individuals, and thus reduces the selection
    pressure when the fitness variance is high.

Bakers ranking method
  • Individuals are ranked according to fitness.
  • the user chooses the expected value Max of the
    individual with rank N, and Min, the expected
    value of the individual with rank 1.
  • Constrants Maxgt0 and Sum of ExpVal N
  • 1lt Maxlt2 and Min 2 - Max

Truncation Selection
  • In truncation selection individuals are sorted
    according to their fitness. Only the best
    individuals are selected for parents. These
    selected parents produce uniform at random
  • The parameter for truncation selection is the
    truncation threshold Trunc. Trunc indicates the
    proportion of the population to be selected as
    parents and takes values ranging from 50-10.
    Individuals below the truncation threshold do not
    produce offspring.

Tournament Selection
  • Two individuals are chosen at random from the
    population.. A random number r between 0 and 1
    is chosen. If rltk (where k is a parameter say
    .75) the more fit individual is selected
    otherwise the least fit is selected. Do this
    twice to retrieve to parents.
  • Fitness-proportionate methods require two passes
    thru the pop. for each generation. One to
    determine the average and one to compute the
    expected value of each individual.
  • This is a very efficient method that works quite
    well and suits itself to parallel solutions.

Fitness Uniform Selection(FUSS)
Population Search Space