AI 中文总结
本研究将分析性重放实验引入进化计算领域，通过分步指南设计该实验，以遗传编程种群为例验证其能量化进化潜力，发现成功潜力与适应度无必然关联，可拓展进化计算理论基础。
AI 中文摘要
在本研究中，我们将分析性重放实验引入进化计算领域。重放实验起源于实验室实验进化，是一种用于识别增强事件的实证方法，这些事件会提高观察到的进化结果的可能性。通过从不同的历史时间点重启种群的进化，重放实验对不同时间点可能进化出的分布进行采样，这使我们能够量化种群的进化潜力如何因其历史而发生变化。在本研究中，我们提供了为进化计算系统设计重放实验的分步指南。随后，我们给出了一个示范性重放实验示例，该实验测量了进化后的遗传编程（genetic programming）种群中问题解决成功的增强情况，结果表明，成功潜力的增加并不一定与种群适应度的增加相对应。总体而言，我们认为分析性重放实验可以成为拓展进化计算理论基础的有力工具，并且我们提出了重放实验所带来的有前景的未来研究方向建议。
英文摘要
In this work, we introduce analytical replay experiments to the evolutionary computing community. Replay experiments originated in the context of laboratory experimental evolution as an empirical approach to identifying potentiating events that increased the likelihood of an observed evolutionary outcome. By restarting a population's evolution from different historical time points, replay experiments sample the distribution of what could have evolved from different points in time, which allows us to quantify how a population's potential for different evolutionary outcomes changed as a result of that population's history. In this work, we give a step-by-step guide to designing replay experiments for evolutionary computing systems. We then provide a demonstrative example replay experiment that measures how potentiation for problem-solving success changed in an evolved genetic programming population, showing that increases in potential for success do not necessarily correspond with increases in a population's fitness. Broadly, we argue that analytical replay experiments can be a powerful tool for expanding the theoretical foundations of evolutionary computing, and we offer suggestions for promising future research directions enabled by replay experiments.
