December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Novelty Search optimization method
In all the experiments preceding this chapter, we defined an objective function as a derivative based on its proximity to the final goal of the problem. However, the maze-solving problem posed challenges that could not be solved by a goal-oriented objective function. Specific maze configurations can introduce strong local optima in which a goal-oriented objective search may become stuck. In many cases, a deceptive fitness function landscape such as this effectively blocks a goal-oriented objective search from finding a successful solution.
Thus, using the practical experience we gained during the creation of a maze solver in the previous chapter, we embarked on the path of creating a more advanced solver. ...
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