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.
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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.