Visiting Fellowship: Bringing contemporary biology into Evolutionary Computation: Plasticity, hierarchy, and genetic re-use

访问学者：将当代生物学带入进化计算：可塑性、层次结构和基因重用

• 批准号: EP/G000484/1
• 负责人: Riccardo Poli
• 金额: $ 4.98万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG000484%2F1
• 关键词: Visiting; Fellowship; Bringing; contemporary; biology

Evolutionary computation is a branch of computer science using ideas from Darwinian evolution and genetics to design systems and algorithms. Starting from a primordial ooze of random tentative solutions to a problem, generation after generation, evolutionary algorithms refine them using genetic mutations and recombinations and the principle of survival of the fittest. This ultimately leads to the creation of highly fit novel solutions to the problem at hand. Evolutionary computation has provided tens of remarkable human-competitive results. However, there are clear limits in terms of the size and complexity of the kinds of solutions we are able to currently evolve. The main problems are: * limited modularity (modularity is the ability to build large systems out of smaller, semi-independent ones), * limited scalability (scalability refers to how rapidly the computational effort of running a problem-solver increases with the size of the problems),* lack of plasticity (plasticity refers to the ability for a solution to change its behaviour in response to external conditions and events)This project will tackle all three of these significant problems in evolutionary computation with a radically new approach, inspired by biology that has already solved these problems.

进化计算是计算机科学的一个分支，它利用达尔文进化论和遗传学的思想来设计系统和算法。从一个问题的随机尝试性解决方案的原始渗出物开始，一代又一代，进化算法使用基因突变和重组以及适者生存的原则来完善它们。这最终导致创建高度适合手头问题的新颖解决方案。进化计算已经提供了数十个显著的人类竞争结果。然而，就我们目前能够发展的解决方案的规模和复杂性而言，存在明显的限制。主要问题是：* 有限的模块化（模块化是指从较小的、半独立的系统中构建大型系统的能力），* 有限的可扩展性（可伸缩性是指运行问题求解器的计算工作量随着问题的大小增加的速度），* 缺乏可塑性（可塑性是指溶液根据外部条件和事件改变其行为的能力）这个项目将用一种全新的方法来解决进化计算中的所有这三个重要问题，这种方法的灵感来自已经解决这些问题的生物学。