December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Starting with problem analysis
Starting with a proper analysis of the problem space is a recipe for success. Neuroevolution is lenient with programmer errors. Such mistakes are a part of the environment, and the evolution process can adapt to them. However, there is a particular category of mistakes that can hinder the evolution process from finding a successful solution: the numerical stability of the evolution process. Most types of activation function are designed to operate in a range of inputs between zero and one. As a result, too large or negative values do not have much influence on the evolution process.
Thus, you may need to preprocess the input data to avoid these numeric issues. Do not skip the analysis of the input data samples ...
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