On averaging the best samples in evolutionary computation

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Authors Laurent Meunier, Yann Chevaleyre, Jeremy Rapin, Clรฉment W. Royer, Olivier Teytaud arXiv ID 2004.11685 Category cs.NE: Neural & Evolutionary Cross-listed cs.LG, stat.ML Citations 0 Last Checked 4 months ago
Abstract
Choosing the right selection rate is a long standing issue in evolutionary computation. In the continuous unconstrained case, we prove mathematically that a single parent $ฮผ=1$ leads to a sub-optimal simple regret in the case of the sphere function. We provide a theoretically-based selection rate $ฮผ/ฮป$ that leads to better progress rates. With our choice of selection rate, we get a provable regret of order $O(ฮป^{-1})$ which has to be compared with $O(ฮป^{-2/d})$ in the case where $ฮผ=1$. We complete our study with experiments to confirm our theoretical claims.
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