The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is particularly relevant in Genetic Programming. This paper studies how evaluation time influences not only time to solution in parallel/distributed systems, but may also affect size evolution of individuals in the population, and eventually will reduce the bloat phenomenon GP features. This paper considers time and space as two sides of a single coin when devising a more natural method for fighting bloat. This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested. Experimental data confirms the strength of the approach: using computing time as a measure of individuals' complexity allows to control the growth in size of genetic programming individuals.

中文翻译：

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是时候就如何对抗 GP 中的膨胀提出新观点了

进化算法的现在和未来取决于现代并行和分布式计算基础设施的正确使用。尽管顺序方法仍然占主导地位，但可用的多核、众核和分布式系统将使用户和研究人员更频繁地部署算法的并行版本。在这种情况下，在对个人进行并行评估时节省的时间出现了新的可能性。这种节省时间在遗传编程中尤其重要。本文研究评估时间如何不仅影响并行/分布式系统中的求解时间，还可能影响种群中个体的大小演化，并最终减少膨胀现象 GP 特征。在设计一种更自然的方法来对抗膨胀时，本文将时间和空间视为一枚硬币的两个面。这种新观点使我们能够理解可以推导出控制膨胀的新方法，并且描述和测试了这种方法中的第一种。实验数据证实了该方法的优势：使用计算时间作为个体复杂性的度量可以控制遗传编程个体大小的增长。