Visualizations of Deep Learning, Recurrent Neural Networks, and Evolutionary Algorithms
Monday, January 31, 2011
High-Order Adaptive Mutation
This is a visualization of an experiment I'm doing involving high-order, self-adaptive mutation distributions. This idea has grown out of an ongoing conversation with the creator of floatworld, a really neat (and increasingly full-featured) Artificial Life simulator.
The basic idea is that over time, the environment an adaptive organism finds itself in will change, and it may do so at varying rates. Rather than allowing only a fixed size/rate of mutation, why not allow the parameters controlling mutation to themselves be folded into the evolutionary process?
The visualization above is the result of creating such a system, and additionally making use of a covariance matrix, so that the distribution can be stretched and rotated arbitrarily.
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