EA-Powered Basin Number Estimation by Means of Preservation and Exploration

When using an evolutionary algorithm on an unknown problem, properties like the number of global/local optima must be guessed for properly picking an algorithm and its parameters. It is the aim of current paper to put forward an EA-based method for real-valued optimization to provide an estimate on the number of optima a function exhibits, or at least of the ones that are in reach for a certain algorithm configuration, at low cost. We compare against direct clustering methods applied to different stages of evolved populations; interestingly, there is a turning point in evaluations after which our method is clearly better, although for very low budgets, the clustering methods have advantages. Consequently, it is argued in favor of further hybridizations.

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