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Hands-On Neuroevolution with Python
book

Hands-On Neuroevolution with Python

by Iaroslav Omelianenko
December 2019
Intermediate to advanced
368 pages
11h 10m
English
Packt Publishing
Content preview from Hands-On Neuroevolution with Python

NEAT section

This section specifies parameters that are specific to the NEAT algorithm. This section includes the following parameters:

  • fitness_criterion: The function that computes the termination criterion from a set of fitness values of all the genomes in the population. The parameter values are the names of standard aggregate functions, such as min, max, and mean. The min and max values are used to terminate the evolution process if the minimal or maximal fitness of the population exceeds the given fitness_threshold. When the value is set to mean, the average fitness of the population is used as a termination criterion.
  • fitness_threshold: The threshold value is compared against the fitness and calculated by the fitness_criterion function ...
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Publisher Resources

ISBN: 9781838824914