克服生物复杂性测量中的问题
Overcoming Problems in the Measurement of Biological Complexity
摘要：
在遗传算法中，基因组的熵随时间的波动被解释为基因组的生物体存储的关于其环境的信息的波动，这反映在更复杂的生物体中。由于实际使用的总体规模较小，因此计算该熵存在技术问题。在这项工作中，我们提出并测试了另一种方法来测量人口中的熵变的算法信息论，其中两代步骤之间的熵变是Kolmogorov复杂性的第一步条件的第二个。作为这种技术的一个应用实例，我们报告了有性生殖存在或不存在的系统之间熵演化的实验差异。
英文摘要：
In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome's organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. As an example application of this technique, we report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.
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