On averaging the best samples in evolutionary computation
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 $\mu=1$ leads to a sub-optimal simple regret in the case of the sphere function. We provide a theoretically-based selection rate $\mu/\lambda$ that leads to better progress rates. With our choice of selection rate, we get a provable regret of order $O(\lambda^{-1})$ which has to be compared with $O(\lambda^{-2/d})$ in the case where $\mu=1$. We complete our study with experiments to confirm our theoretical claims.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.11685
- arXiv:
- arXiv:2004.11685
- Bibcode:
- 2020arXiv200411685M
- Keywords:
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- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Machine Learning;
- Statistics - Machine Learning