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Evolution strategies for continuous optimization: A survey of the state-of-the-art
Swarm and Evolutionary Computation ( IF 8.2 ) Pub Date : 2020-04-19 , DOI: 10.1016/j.swevo.2020.100694
Zhenhua Li , Xi Lin , Qingfu Zhang , Hailin Liu

Evolution strategies are a class of evolutionary algorithms for black-box optimization and achieve state-of-the-art performance on many benchmarks and real-world applications. Evolution strategies typically evolve a Gaussian distribution to approach the optimum. In this paper, we present a survey of recent advances in evolution strategies. We summarize the techniques, extensions, and practical considerations of evolution strategies for various optimization problems. We discuss some important open questions and promising topics that desire further research. Many of the discussed techniques and principles are applicable to other algorithms.



中文翻译:

持续优化的进化策略:最新技术概览

进化策略是用于黑箱优化的一类进化算法,可在许多基准和实际应用中实现最先进的性能。演化策略通常会演化出高斯分布以逼近最佳值。在本文中,我们对进化策略的最新进展进行了概述。我们总结了各种优化问题的进化策略的技术,扩展和实际考虑。我们讨论了一些重要的开放性问题和有希望的话题,需要进一步研究。讨论的许多技术和原理都适用于其他算法。

更新日期:2020-04-19
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