Emergence and Open-ended Evolution in Genetic Programming

标题突现和遗传规划中的开放式进化******突现在科学技术中的作用刚刚开始变得非常明显。长期以来，涌现现象一直局限于特定领域的专家讨论，但随着大规模模拟方法在科学和工程领域的引入，这种情况即将发生变化，因为它将失去其“外来特征”，并以其本来的样子变得可见：在众多学科中具有解释力的普遍现象。另一方面，新奇和创新一直被认为是科技进步的动力。然而，它们的建模和形式化一直很困难，因为时间在任何新颖性概念和在形式化系统中捕获该概念的技术困难中发挥着重要作用。******我们将使用生物启发计算的前沿方法之一，遗传规划，来研究计算系统中的新颖性创造和出现现象。特别是，我们感兴趣的是如何在这样的系统中实现持续的创造力过程，持续的新一代。我们将进一步尝试形式化系统中新实体的出现，使用遗传规划作为实验室来设置适当的条件。******通过从达尔文的进化过程中获得灵感，遗传编程不仅可以解决单个问题实例，就像在组合优化中一样，而且可以使用自然的“配方”，导致适应性生物和高级生物的智能产生解决问题的实体。随着摩尔定律的持续有效性，未来十年将会出现生物启发算法（尤其是从进化中收集的算法）带来的与人类竞争的结果的爆炸式增长。******遗传编程是人工智能兴起的一部分，它是由同样的力量驱动的——(1)所有科学和技术领域的大量数据的可用性；(2)计算更复杂算法的处理周期持续增加；(3)神经网络的深度学习等新范例彻底改变了整个应用领域。******我们将把遗传编程作为我们的“实验室”，探索算法系统中的新颖性、创新性、突现性和创造性，并将其应用于多个领域：机器学习和人工智能、大数据分析、计算科学和数学。除了对遗传编程算法的方法改进之外，我们还将研究它们在两个重要应用领域的行为，即深度学习神经网络的结构和用于修复计算机代码中的错误的代码补丁的演变。

Title Emergence and Open-ended Evolution in Genetic Programming******The role of emergence in science and technology is just beginning to become highly visible. Emergent phenomena have long been constrained to the discussion of specialists in particular fields but with the introduction of large-scale simulation approaches to science and engineering, this is about to change in in that it will loose its "exotic character" and become visible in what it is: A widespread phenomenon with explanatory power in a vast array of disciplines. Novelty and innovation, on the other hand, have long been recognized as drivers of progress in science and technology. However, their modelling and formalization has been difficult, due to the essential rule time plays in any notion of novelty and the technical difficulties of capturing that concept in formal systems. ******We shall use one of the methods at the cutting edge of Bio-inspired Computing, Genetic Programming, to study the phenomena of novelty creation and emergence in computational systems. In particular, we are interested in how to achieve an ongoing process of creativity, a continued generation of novelty, in such systems. We further shall attempt to formalize the emergence of new entities in systems, using Genetic Programming as a laboratory in which to set appropriate conditions. ******By taking inspiration from the Darwinian process of evolution, Genetic Programming allows not only to solve individual problem instances, like in combinatorial optimization, but to use the “recipe” of Nature which led to adaptive organisms and the intelligence of higher-level organisms to generate problem-solving entities. With the continued validity of Moore's law, the next decade will demonstrate an explosion of human-competitive results from bio-inspired algorithms, and in particular from those gleaned from evolution. ******Genetic Programming is part of the upswing in Artificial Intelligence, which is driven by the same forces – (1) availability of large amounts of data in all areas of science and technology; (2) continued growth in the availability of processing cycles for calculation of ever more complex algorithms; (3) new paradigms like deep learning for neural networks that have revolutionized entire application areas.******We shall take Genetic Programming as our “laboratory” to explore novelty, innovation, emergence and creativity in an algorithmic system with applications to multiple domains: machine learning and artificial intelligence, big data analysis, computational science and mathematics. Besides methodological improvements to genetic programming algorithms, we shall examine their behaviour in two important application areas, the structuring of deep learning neural networks and the evolution of code patches for repairing bugs in computer code.