进化在机器智能中的作用

摘要：

机器智能可以直接从经验中发展出来，也可以通过进化继承经验。目前的大部分研究工作都集中在直接从经验中学习的算法上。我认为，另一种选择，进化，是重要的机器智能的发展和投资不足方面的研究分配。这项工作的主要目的是评估在进化算法的频谱投资研究。我的首要建议是在更广泛的进化方法中进行多样化的研究。我还定义了元进化算法，并认为它们可能会在影响机器智能发展的许多因素之间产生最佳权衡。

英文摘要：

Machine intelligence can develop either directly from experience or by inheriting experience through evolution. The bulk of current research efforts focus on algorithms which learn directly from experience. I argue that the alternative, evolution, is important to the development of machine intelligence and underinvested in terms of research allocation. The primary aim of this work is to assess where along the spectrum of evolutionary algorithms to invest in research. My first-order suggestion is to diversify research across a broader spectrum of evolutionary approaches. I also define meta-evolutionary algorithms and argue that they may yield an optimal trade-off between the many factors influencing the development of machine intelligence.

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