The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. In this paper the author investigates whether combining learning and evolution permits to find better solutions than those discovered by evolution alone. More specifically, the author presents a series of empirical studies that highlight some specific conditions determining the success of such a combination, like the introduction of noise during the learning and selection processes. Results are obtained in two qualitatively different domains, where agent/environment interactions are minimal or absent.

翻译：演化与学习之间的相互关系是人工智能和神经演化领域中的一个争议性话题。经过三十多年的研究，学界仍未就此达成共识。本文作者探讨了将学习与演化相结合是否能够比单纯依靠演化发现更优解决方案。具体而言，作者通过一系列实证研究揭示了决定这种结合成功的特定条件，例如在学习与选择过程中引入噪声。研究在两个性质截然不同的领域中获得结果，这些领域中的智能体/环境交互极少或不存在。