A general framework is introduced to estimate how much external information has been infused into a search algorithm, the so-called active information. This is rephrased as a test of fine-tuning, where tuning corresponds to the amount of pre-specified knowledge that the algorithm makes use of in order to reach a certain target. A function $f$ quantifies specificity for each possible outcome $x$ of a search, so that the target of the algorithm is a set of highly specified states, whereas fine-tuning occurs if it is much more likely for the algorithm to reach the target than by chance. The distribution of a random outcome $X$ of the algorithm involves a parameter $\theta$ that quantifies how much background information that has been infused. A simple choice of this parameter is to use $\theta f$ in order to exponentially tilt the distribution of the outcome of the search algorithm under the null distribution of no tuning, so that an exponential family of distributions is obtained. Such algorithms are obtained by iterating a Metropolis-Hastings type of Markov chain, and this makes it possible to compute the their active information under equilibrium and non-equilibrium of the Markov chain, with or without stopping when the targeted set of fine-tuned states has been reached. Other choices of tuning parameters $\theta$ are discussed as well. Nonparametric and parametric estimators of active information and tests of fine-tuning are developed when repeated and independent outcomes of the algorithm are available. The theory is illustrated with examples from cosmology, student learning, reinforcement learning, a Moran type model of population genetics, and evolutionary programming.
翻译:引入一个通用框架来估计有多少外部信息被注入到搜索算法中, 即所谓的活跃信息。 这被重新表述为微调测试, 调幅与算法为达到某个目标而使用的预指定的知识量相对应。 函数美元量化了每种可能的搜索结果的具体性 $x美元, 因此算法的目标是一组高度指定的状态, 而如果算法比偶然更有可能达到目标, 则会进行微调。 随机结果的分布包含一个参数 $\theta$ 的调序, 该调序与该算法为特定目标的精确度相当。 这个参数的简单选择是使用 $\theta f$, 以在不进行调试的情况下, 使搜索算法的结果的分布急剧下降, 从而获得分布的指数式组合。 这种算法是通过对计算算法的推移算方法比偶然地达到目标。 随机结果的X$ 算法的分布涉及一个参数 $@thetaal resulationalate explain rial explain rial rial legal ledgeal legal and laftations laft laft laft laft laft lax laft lax lax lax laft laft laft laft laft lax lax oral lax lax labild. se se oral oral lautd. 在不进行精确算和没有固定和不固定或非固定的精确测试中, 。 。 romocal- 。 。 。 romocal- romocal- rocil- rocal- 。 rocal- 和没有固定的校正正正正正和不固定和不固定和不固定和不固定和不固定和不固定和不设制 。