In evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion of final productive fitness, which is the ideal target fitness for any evolutionary process. Although it is inefficient to compute, we show empirically that it allows for an a posteriori analysis of how well a given evolutionary optimization process hit the ideal exploration/exploitation trade-off, providing insight into why diversity-aware evolutionary optimization often performs better.

在进化算法中，多样性的概念已被生物学所采用，并被用来描述解决方案候选者群体的分布。众所周知，保持合理数量的多样性通常会通过调整勘探/开采权衡而使进化优化过程的整体结果受益，但对于哪种多样性是最优的知之甚少。我们基于特定解决方案候选者在进化路径上有几代的影响来介绍生产适应性的概念。我们推导出最终生产适用性的概念，这是任何进化过程的理想目标适用性。尽管计算效率低下，但我们通过经验证明它允许后验分析给定的进化优化过程如何达到理想的探索/开发权衡，从而深入了解为何多样性感知的进化优化通常表现更好。