From Genetic Programming to Computational Evolution

这里提出的研究的目标是从早期的非常抽象和简化的EC模型，更接近现实的自然进化模型中实现的计算系统，换句话说，在“计算进化”的方向。为此，一些已建立的进化算法的基本假设将受到严格的修订。此外，分子和进化生物学的新概念不知道在这些算法的设计时间将被纳入。虽然这些概念几乎是无穷无尽的，但我在未来五年的重点是以下主题： (i)表观遗传相互作用允许基因组根据环境的提示进行重组。这是一个极其重要的概念，特别是在生物学中进化和发展之间的强化联系方面。将表观遗传机制纳入算法将通过为人工调控网络添加另一层控制信号来实现。这构成了一种允许算法基因组与其环境之间相互作用的新方式。 (ii)合作的演化及其对演化转型的贡献。虽然早期的模型研究两级系统，在这项研究中，我们将研究多层次的选择，并从适应群体和层次结构的大小，以解决进化的问题所获得的好处。预计这些方法将带来可观的效率收益，因为它们解决了进化算法的关键问题之一，特别是遗传编程：可扩展性。 (iii)生物学和计算环境中的新奇事物的出现。新奇和相关的创新现象是达尔文思想的中心原则。如果没有新设备的发明，自然界和计算机都无法获得进化的全部好处。在这里，我们将研究在自然环境中产生新奇的机制，例如通过基因组中的基因复制，并寻求将配方转移到计算系统中。

The goal of the research proposed here is to move from earlier very abstract and simplified EC models, closer to realistic models of natural evolution implemented in computational systems, in other words, in the direction of "computational evolution". To this end, a number of basic assumptions of established evolutionary algorithms will be subjected to critical revision. In addition, new concepts of molecular and evolutionary biology not known at the time of the design of these algorithms will be incorporated. While the set of those concepts is virtually endless, my focus for the next five years is on the following topics: (i) epigenetic interactions that allow a genome to reorganize in response to cues from its environment. This is an extremely important concept, notably with respect to the reinforced connection between evolution and development in biology. The inclusion of epigenetic mechanisms in algorithms will happen through the addition of a further layer of control signals for artificial regulatory networks. This constitutes a new way of allowing interactions between an algorithmic genome and its environment. (ii) The evolution of cooperation and its contribution to evolutionary transitions. While earlier models studied two-level systems, in this research we shall study multi-level selection and the benefits to be gained from adapting the size of groups and hierarchies to the problem that is to be solved by evolution. Substantial efficiency gains are expected from these approaches, since they address one of the key problems evolutionary algorithms, and Genetic Programming in particular: scalability. (iii) The emergence of novelty in both biological and computational environments. Novelty and the associated phenomenon of innovation is a central tenet in Darwinian thought. Without the invention of new devices neither nature nor computing reap the full benefits of evolution. Here, we shall examine the mechanisms by which novelty is produced in natural settings, for example by gene duplication in genomes, and seek to transfer recipes into computational systems.