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Infant Populations and Lateral Genetic Mobility vs. Complexification

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  • afcarl2
    The beneficial objectives of Supervisor NEAT are twofold: a) Enable implementation of pyramid number of populations vs. starting genome complexity; and b)
    Message 1 of 1 , Feb 3, 2007
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      The beneficial objectives of Supervisor NEAT are twofold:
      a) Enable implementation of pyramid number of populations vs.
      starting genome complexity; and
      b) Enable implementation of automated evolution of embedded
      genome/populations in Embedded NEAT.

      Investigation has demonstrated each of the following:
      1) NEAT parameters have a profound impact on relative dominance of
      lateral genetic mobility vs. complexification;
      2) Standard NEAT parameter values lack sufficient lateral genetic
      mobility to "get within" the genetic mobility radius of an optimal
      solution in lower dimension space, prior to being weighed-down with
      the associated computational overhead of increased complexity due to
      pre-mature complexification;
      3) Exploitation of infant populations to maximize lateral genetic
      mobility and radius is more beneficial in identifying optimal
      solution space despite higher dimensionality of NEAT parameter space
      associated with Supervisor NEAT, when coupled with a pyramid
      population vs. starting genome complexity scheme;

      Current investigation involves the determination of (8) control
      constants for multiple time histories comprising 40,000 time points,
      each with associated (4) input floats and (1) target output float.

      One beneficial characteristic usage of Supervisor NEAT with infant
      populations, is the ability to identify infant populations
      establishing new "high-water-marks" of fitness, and "getting
      another" population with the same NEAT parameter set of values
      (i.e. "going where the gold is").
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