A diversity-enhanced genetic algorithm with a similarity penalty explores multiple viable parameter regions more efficiently than random scans and, in a toy test, finds a more diverse set of optima than SciPy's differential evolution.
Shapiro, in Advanced Course on Artificial Intelligence (Springer, 1999) pp
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A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces
A diversity-enhanced genetic algorithm with a similarity penalty explores multiple viable parameter regions more efficiently than random scans and, in a toy test, finds a more diverse set of optima than SciPy's differential evolution.