Directing evolution: the automated design of evolutionary pathways using directed graphs
Directing evolution: the automated design of evolutionary pathways using directed graphs
复制标题
定向进化：使用有向图自动设计进化路径
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
D. Tauritz
中科院分区：
文献类型：
--
作者：
Braden N. Tisdale;Deacon Seals;A. Pope;D. Tauritz
随着计算能力的增长，进化算法（EA）的自动专业化和设计，以调整其性能，以个别问题类变得更具吸引力。为此，近几十年来，利用各种技术进行了大量研究。然而，很少有技术已经设计出自动化的EA的整体结构的设计。大多数EA的实现仅仅依赖于传统的进化周期，即亲本选择、繁殖和生存选择，而那些具有独特结构的EA通常会用另一个静态的手工周期来取代这一周期。现有的技术，用于修改的进化结构使用的表示，要么是松散的结构和高度随机的，或者是受约束的，不能很容易地发展复杂的途径。容易地发展复杂的进化途径的能力将极大地扩展在专业化期间可以探索的启发式空间，潜在地允许表现优于传统周期的EA的表示。这项工作提出了一种方法，通过引入一种新的有向图为基础的表示，创建是可变的和灵活的，允许黑盒设计师产生可重用的，高性能的EA的进化过程中的自动化设计。实验表明，我们的方法可以产生高性能的EA演示可理解的战略。
As computing power grows, the automated specialization and design of evolutionary algorithms (EAs) to tune their performance to individual problem classes becomes more attractive. To this end, a significant amount of research has been conducted in recent decades, utilizing a wide range of techniques. However, few techniques have been devised which automate the design of the overall structure of an EA. Most EA implementations rely solely on the traditional evolutionary cycle of parent selection, reproduction, and survival selection, and those with unique structures typically replace this with another static, hand-made cycle. Existing techniques for modifying the evolutionary structure use representations which are either loosely structured and highly stochastic, or which are constrained and unable to easily evolve complicated pathways. The ability to easily evolve complex evolutionary pathways would greatly expand the heuristic space which can be explored during specialization, potentially allowing for the representation of EAs which outperform the traditional cycle. This work proposes a methodology for the automated design of the evolutionary process by introducing a novel directed-graph-based representation, created to be mutable and flexible, permitting a black-box designer to produce reusable, high-performance EAs. Experiments show that our methodology can produce high-performance EAs demonstrating intelligible strategies.