This study investigates the relationship between sparse computation and evolution in various models using a simple function we call sparsify. We use the sparsify function to alter the sparsity of arbitrary matrices during evolutionary search. The sparsify function is tested on a recurrent neural network, a gene interaction matrix, and a gene regulatory network in the context of four different optimization problems. We demonstrate that the function positively affects evolutionary adaptation. Furthermore, this study shows that the sparsify function enables automatic meta-adaptation of sparsity for the discovery of better solutions. Overall, the findings suggest that the sparsify function can be a valuable tool to improve the optimization of complex systems.

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