December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Fitness function
The fitness function used in the experiments described in this chapter directly applies the novelty score defined previously as the fitness value of the genome. As a result, the neuroevolution process tries to maximize the novelty of the produced individuals by using such a fitness function.
For different tasks in this experiment, we use various fitness factors:
- The novelty score is used to guide the neuroevolution process (solution search optimization). It is assigned as a fitness value to each genome and used for genome evaluation during generations of evolution.
- The goal-oriented fitness score (the distance to the maze exit) obtained from the maze simulator is used to test if the ultimate goal has been achieved (that ...
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